spheron.network

# Spheron

> Spheron is an enterprise GPU rental marketplace. We aggregate NVIDIA GPU compute from Tier 2, 3, and 4 compliant data centers globally into a single platform with live pricing, 1-click deployment, and full root access. Developers and teams rent H100, B200, B300, A100, H200, GH200, L40S, RTX PRO 6000, RTX 5090, and RTX 4090 GPUs for AI training, inference, and research at a fraction of hyperscaler costs.

Spheron is not a single cloud provider. It is a marketplace that aggregates GPU supply from multiple enterprise-grade data center partners. Prices fluctuate based on real-time availability. All infrastructure comes from Tier 2/3/4 compliant facilities (not consumer hardware). Customers get VM or bare metal options, per-minute billing, SSH root access, and zero vendor lock-in through a single account.

Live GPU pricing: https://www.spheron.network/pricing/
Launch a GPU instance: https://app.spheron.ai

## Core Pages

- [Homepage](https://www.spheron.network/): Platform overview, live GPU pricing table, cost comparison vs hyperscalers, compliance info, and getting started
- [GPU Rental Catalog](https://www.spheron.network/gpu-rental/): Browse all available NVIDIA GPU models with specs, pricing, and 1-click rent
- [Pricing](https://www.spheron.network/pricing/): Live marketplace pricing for every GPU model, updated in real time
- [Blog](https://www.spheron.network/blog/): Technical guides, GPU benchmarks, deployment tutorials, cost analysis, and product updates
- [Documentation](https://docs.spheron.ai): Deployment guides, API reference, SSH setup, framework tutorials, and troubleshooting
- [API Reference](https://docs.spheron.ai/api-reference): REST API for programmatic GPU provisioning, instance management, and billing
- [GPU Cloud Dashboard](https://app.spheron.ai): Self-service platform to browse, deploy, and manage GPU instances
- [Contact / Enterprise](https://www.spheron.network/contact/): Reach the Spheron team for enterprise inquiries and support
- [Supply GPUs to Spheron](https://www.spheron.network/partner/): Apply to the Spheron GPU supplier program. Data center operators and neoclouds list H100, H200, B200, and B300 capacity, receive vetted buyer demand, and keep their own pricing and contracts
- [Rent GPU for Gonka.ai](https://www.spheron.network/partner/gonka/): Rent H100, H200, and B200 bare metal servers for Gonka.ai network participation, with stables payment support (USDT, USDC) and setup guides
- [Customer Stories](https://www.spheron.network/customers/): Case studies of AI teams sourcing NVIDIA GPU capacity through Spheron, including PremAI
- [Privacy Policy](https://www.spheron.network/privacy/): Data handling and privacy practices
- [Book Enterprise Consultation](https://meetings-eu1.hubspot.com/prashant-maurya): For bulk GPU needs (100+ GPUs), custom sourcing, and dedicated support

## Customer Stories

Each story details what we sourced for the customer (specific GPU models, data center partner profile) and how Spheron's marketplace fit their use case.

- [PremAI Case Study](https://www.spheron.network/customers/premai/): How PremAI runs confidential AI inference on H200 servers with NVIDIA TEE enabled, sourced through Spheron from Tier 3/4 facilities

## Feature Pages

Each page covers billing model details, use cases, and how to get started.

- [On-Demand GPU Instances](https://www.spheron.network/features/on-demand-instances/): Per-minute billing with no contracts or minimums. Deploy H100, A100, B200, and other NVIDIA GPUs in under 2 minutes
- [Spot GPU Instances](https://www.spheron.network/features/spot-instances/): Rent NVIDIA GPUs at up to 50% off with spot pricing. Built for batch jobs, training experiments, and flexible workloads
- [Reserved GPU Commitments](https://www.spheron.network/features/reserved-commitments/): Volume pricing with guaranteed availability. Custom clusters from 8 to 512+ GPUs with InfiniBand and dedicated setup

## GPU Rental Pages

Each page covers specs, live pricing, use cases, provider comparison, and a direct rent button.

- [NVIDIA H100 80GB HBM3](https://www.spheron.network/gpu-rental/h100/): Hopper architecture, 3.35 TB/s bandwidth, InfiniBand available. Best for LLM training 175B+ and large-scale inference
- [NVIDIA B200 192GB HBM3e](https://www.spheron.network/gpu-rental/b200/): Blackwell architecture, 8 TB/s bandwidth, NVLink 1.8TB/s. For trillion-parameter models and next-gen LLMs
- [NVIDIA B300 288GB HBM3e](https://www.spheron.network/gpu-rental/b300/): Blackwell Ultra, 10 TB/s bandwidth. Highest memory capacity for the most demanding training workloads
- [NVIDIA H200 141GB HBM3e](https://www.spheron.network/gpu-rental/h200/): Enhanced Hopper, 4.8 TB/s bandwidth. Optimized for LLM inference, RAG systems, and long context windows
- [NVIDIA GH200 96GB HBM3](https://www.spheron.network/gpu-rental/gh200/): Grace-Hopper superchip, up to 432GB system RAM. For memory-intensive AI and HPC workloads
- [NVIDIA A100 80GB HBM2e](https://www.spheron.network/gpu-rental/a100/): Ampere architecture, proven workhorse for training up to 20B parameters and cost-effective inference
- [NVIDIA L40S 48GB GDDR6](https://www.spheron.network/gpu-rental/l40s/): Ada Lovelace, excellent price/performance for inference serving and mixed graphics/compute
- [NVIDIA RTX PRO 6000 96GB GDDR7](https://www.spheron.network/gpu-rental/rtx-pro-6000/): Blackwell professional GPU for AI development, fine-tuning, and rendering
- [NVIDIA RTX 5090 32GB GDDR7](https://www.spheron.network/gpu-rental/rtx-5090/): Latest consumer flagship for AI experimentation and cost-effective development
- [NVIDIA RTX 4090 24GB GDDR6X](https://www.spheron.network/gpu-rental/rtx-4090/): Most affordable GPU for fine-tuning, prototyping, and small-to-medium model training
- [NVIDIA Rubin R100 288GB HBM4 (Pre-Order)](https://www.spheron.network/gpu-rental/r100/): Pre-order page for the Rubin R100 (H300) GPU — 50 PFLOPS FP4, 22 TB/s bandwidth, NVLink 6. Expected H2 2026. Register interest for early access.
- [NVIDIA GB300 288GB HBM3e](https://www.spheron.network/gpu-rental/gb300/): Blackwell Ultra GPU with 288 GB HBM3e, 8 TB/s bandwidth, NVLink 5, Grace CPU coupling. Reserve any quantity for trillion-parameter training and frontier inference.
- [NVIDIA GB200 192GB HBM3e](https://www.spheron.network/gpu-rental/gb200/): Blackwell GPU with 192 GB HBM3e, 8 TB/s bandwidth, NVLink 5, Grace CPU coupling. Reserve any quantity for large-scale LLM training and inference.

## Guides: Foundational

- [What Is a GPU Cloud? Definition, How It Works, and When to Use One (2026)](https://www.spheron.network/blog/what-is-gpu-cloud/): Definitional guide covering GPU cloud architecture, billing models (on-demand, spot, reserved, serverless), common GPU SKUs with live pricing, GPU cloud vs hyperscaler vs on-prem comparison, and a step-by-step getting started walkthrough on Spheron

## Guides: GPU Selection & Pricing

- [AWS H100 Pricing 2026: P5 Cost vs Spheron and Neoclouds](https://www.spheron.network/blog/aws-h100-pricing-2026/): P5/P5e/P5en instance pricing, hidden AWS H100 costs, break-even math for Savings Plans, and a direct neocloud comparison
- [Lambda Cloud H100 Pricing 2026](https://www.spheron.network/blog/lambda-cloud-h100-pricing-2026/): Per-hour cost breakdown for Lambda Cloud H100 on-demand and reserved pricing, with direct comparison against Spheron, RunPod, and CoreWeave H100 rates.
- [GPU Cloud Pricing Comparison 2026](https://www.spheron.network/blog/gpu-cloud-pricing-comparison-2026/): H100, H200, B200, A100, L40S pricing across 15+ providers with hidden cost analysis
- [Google Cloud A3 H100 Pricing 2026](https://www.spheron.network/blog/google-cloud-a3-h100-pricing/): GCP A3 High vs Spheron H100 SXM5 per-hour pricing, hidden costs (egress, persistent disk, CUD mechanics), and monthly TCO comparison
- [Azure H100 Pricing 2026: ND H100 v5 vs Spheron](https://www.spheron.network/blog/azure-h100-pricing/): Azure ND96isr H100 v5 pay-as-you-go, reserved, and spot pricing breakdown with hidden costs and Spheron H100 cost comparison
- [Best NVIDIA GPUs for LLMs in 2026](https://www.spheron.network/blog/best-nvidia-gpus-for-llms/): B300, B200, H200, H100, RTX 5090 ranked by use case with VRAM needs and benchmarks
- [Best GPU for AI Image Generation 2026](https://www.spheron.network/blog/best-gpu-ai-image-generation-2026/): RTX 4090 to H100 GPU decision guide for Stable Diffusion, SDXL, Flux.1, and Flux.2 with VRAM math, step-time benchmarks, and cost-per-image tables
- [GPU Cloud Benchmarks 2026](https://www.spheron.network/blog/gpu-cloud-benchmarks/): Side-by-side pricing, specs, and inference throughput from 10+ providers
- [GPU Memory Requirements for LLMs](https://www.spheron.network/blog/gpu-memory-requirements-llm/): VRAM calculator for models from 7B to 685B covering weights, KV cache, and quantization
- [GPU Requirements Cheat Sheet 2026](https://www.spheron.network/blog/gpu-requirements-cheat-sheet-2026/): Quick reference for matching workloads to the right GPU
- [GPU Cost Optimization Playbook](https://www.spheron.network/blog/gpu-cost-optimization-playbook/): Strategies to cut AI compute bills by 60% through instance selection, spot pricing, and idle elimination
- [GPU Goodput Engineering: Why AI Clusters Sit at 5% Utilization and How to Fix It](https://www.spheron.network/blog/gpu-goodput-engineering-enterprise-ai-cluster-utilization/): Operator playbook for diagnosing and fixing GPU goodput problems in enterprise AI clusters - covers DCGM metrics, MIG packing, prefill-decode disaggregation, L40S vs H100 right-sizing, and Spheron spot scheduling.
- [AI Buyer's Guide](https://www.spheron.network/blog/ai-buyers-guide/): How to evaluate GPU providers beyond raw specs
- [Top 10 Cloud GPU Providers](https://www.spheron.network/blog/top-10-cloud-gpu-providers/): Comparison of major GPU platforms on performance, pricing, and control
- [Best GPU for AI Inference in 2026](https://www.spheron.network/blog/best-gpu-for-ai-inference-2026/): L40S vs H100 vs H200 vs B200 benchmarks, tokens/sec/dollar, and workload-based decision guide
- [MLPerf Inference v6.0 Results Explained](https://www.spheron.network/blog/mlperf-inference-v6-benchmark-results-2026/): What MLPerf v6.0 scores mean for GPU cloud users choosing between H200, B200, and MI355X

## Guides: GPU Deep Dives

- [NVIDIA B200 Complete Guide](https://www.spheron.network/blog/nvidia-b200-complete-guide/): Specs, benchmarks, cloud pricing, and H100 upgrade path
- [NVIDIA B300 Blackwell Ultra Guide](https://www.spheron.network/blog/nvidia-b300-blackwell-ultra-guide/): Architecture, specs, pricing, and benchmark data
- [NVIDIA GH200 Guide](https://www.spheron.network/blog/nvidia-gh200-guide/): Grace-Hopper superchip architecture and performance analysis
- [NVIDIA H100 Specs: Complete Datasheet, FP8/FP16 Throughput, and Memory Bandwidth (2026)](https://www.spheron.network/blog/nvidia-h100-specs/): Full H100 spec sheet covering GH100 die, TSMC 4N, 80B transistors, 3.35 TB/s HBM3, FP8/FP16/BF16 TFLOPS tables, MIG profiles, NVLink 900 GB/s, and real-world Llama inference math
- [NVIDIA H100 vs H200](https://www.spheron.network/blog/nvidia-h100-vs-h200/): Benchmarks, specs, and performance comparison for AI inference
- [NVIDIA H200 Specs: 141GB HBM3e, 4.8 TB/s Bandwidth, FP8 Datasheet (2026)](https://www.spheron.network/blog/nvidia-h200-specs/): Full H200 technical datasheet - 141 GB HBM3e, 4.8 TB/s bandwidth, FP8/FP16 TFLOPS, SXM5 vs NVL form factors, NVLink 4 topology, and cloud pricing comparison
- [NVIDIA H200 vs B200 vs GB200](https://www.spheron.network/blog/nvidia-h200-vs-b200-vs-gb200/): Generation-over-generation comparison
- [NVIDIA L40S for AI Inference](https://www.spheron.network/blog/nvidia-l40s-for-ai-inference/): Specs, benchmarks, and pricing for inference workloads
- [NVIDIA L40 vs L40S Comparison](https://www.spheron.network/blog/nvidia-l40-vs-l40s-inference-comparison/): FP8 Transformer Engine differences, inference benchmarks, NVENC comparison, and Spheron L40S cloud pricing
- [RTX 4090 for AI/ML](https://www.spheron.network/blog/rtx-4090-for-ai-ml/): Benchmarks, specs, and pricing for development workloads
- [NVIDIA RTX 6000 Ada Generation Guide](https://www.spheron.network/blog/nvidia-rtx-6000-ada-generation-guide/): 48 GB GDDR6 ECC specs, AI inference benchmarks, fine-tuning capacity, and cloud pricing vs RTX 4090 and RTX PRO 6000 Blackwell
- [RTX 5090 vs H100 vs B200](https://www.spheron.network/blog/rtx-5090-vs-h100-vs-b200/): Cross-tier GPU comparison for different budgets
- [RTX 5090 vs RTX 4090 for AI: Benchmarks and Cost Per Token (2026)](https://www.spheron.network/blog/rtx-5090-vs-rtx-4090/): Head-to-head comparison for AI workloads: vLLM throughput, QLoRA fine-tuning benchmarks, model fit guide, and cost per million tokens using live Spheron pricing
- [NVIDIA H100 vs RTX 4090 for AI (2026)](https://www.spheron.network/blog/nvidia-h100-vs-rtx-4090-ai-training-inference-2026/): Head-to-head comparison of H100 and RTX 4090 for AI training, inference, and cost per token. Covers VRAM limits, FP8 capability, multi-GPU scaling, and the hybrid 4090-dev/H100-train workflow.
- [NVIDIA RTX 5090 Specs: 32GB GDDR7, Blackwell Architecture, and 5th Gen Tensor Cores](https://www.spheron.network/blog/nvidia-rtx-5090-specs/): Full RTX 5090 spec datasheet covering GB202 die, CUDA cores, Tensor Core precision tables, memory bandwidth, and model fit analysis for AI workloads
- [RTX 5090 vs RTX PRO 6000 Blackwell: Consumer vs Pro GPU for AI (2026)](https://www.spheron.network/blog/rtx-5090-vs-rtx-pro-6000-blackwell-comparison/): Side-by-side comparison of RTX 5090 (32GB) and RTX PRO 6000 (96GB) on the same Blackwell die: specs, AI benchmarks, driver stack, ECC, power/thermals, live Spheron pricing, and decision matrix for ML engineers
- [NVIDIA Rubin R100 Guide](https://www.spheron.network/blog/nvidia-rubin-r100-guide/): Next-gen architecture overview and what it means for GPU cloud
- [NVIDIA Rubin CPX Explained](https://www.spheron.network/blog/nvidia-rubin-cpx-long-context-inference/): Covers what NVIDIA's Rubin CPX GPU was, why it was replaced by Groq 3 LPX at GTC 2026, and the current hardware hierarchy for long-context inference.
- [NVIDIA A100 vs V100](https://www.spheron.network/blog/nvidia-a100-vs-v100/): Ampere vs Volta architecture, VRAM, Tensor Cores, MIG support, and cloud pricing comparison
- [NVIDIA A100 vs H100](https://www.spheron.network/blog/nvidia-a100-vs-h100/): Ampere vs Hopper architecture, FP8 vs BF16 throughput, Llama 70B training and inference benchmarks, KV cache analysis, and live Spheron cloud pricing comparison
- [NVIDIA L40S vs A100: Inference Throughput, VRAM, and Cost-Per-Token (2026)](https://www.spheron.network/blog/l40s-vs-a100/): FP8 vs BF16 inference benchmark comparison, cost-per-million-token analysis, and A100-to-L40S migration playbook
- [L40S vs H100 for AI Inference](https://www.spheron.network/blog/l40s-vs-h100/): Cost-per-token decision guide comparing NVIDIA L40S and H100 at batch 1/8/32, with Spheron on-demand and spot pricing comparison and a TL;DR decision matrix.
- [H100 NVL vs H100 SXM5 vs H100 PCIe: Form Factor Decision Guide (2026)](https://www.spheron.network/blog/h100-nvl-vs-sxm5-vs-pcie-form-factor-guide/): Three-way H100 form factor breakdown: 94GB HBM3 NVL for 70B inference, SXM5 NVSwitch for multi-GPU training, PCIe for cost-sensitive fine-tuning
- [NVIDIA GB200 NVL72 Guide](https://www.spheron.network/blog/nvidia-gb200-nvl72-guide/): 72 B200 GPUs, 13.4 TB unified memory, 1.44 exaflops per rack, and when rack-scale beats 8xB200
- [NVIDIA Vera Rubin NVL72: H300 GPU Specs, Cloud Pricing, and Blackwell Upgrade Guide](https://www.spheron.network/blog/nvidia-vera-rubin-nvl72-guide/): Full architecture breakdown of the Vera Rubin NVL72 rack (72 R100 GPUs, HBM4, NVLink 6), performance comparisons against Blackwell and Hopper, cloud availability timeline, projected pricing, and workload decision matrix for upgrading.
- [NVIDIA Groq 3 LPU Explained](https://www.spheron.network/blog/nvidia-groq-3-lpu-explained/): Non-GPU inference chip architecture, 150 TB/s SRAM bandwidth, LPU vs GPU comparison
- [Cerebras vs NVIDIA H100: Wafer-Scale vs GPU for LLM Inference (2026)](https://www.spheron.network/blog/cerebras-vs-nvidia-h100-inference-2026/): WSE-3 vs H100 SXM5 architecture, Llama 70B benchmarks, cost per million tokens at different batch sizes, and a decision framework for choosing between Cerebras Inference API and Spheron GPU cloud
- [Rubin vs Blackwell vs Hopper](https://www.spheron.network/blog/nvidia-rubin-vs-blackwell-vs-hopper/): Full specs, HBM evolution, NVLink generations, and workload-based guidance for 2026
- [HBM3e vs HBM4 vs HBM4e for LLM Inference](https://www.spheron.network/blog/hbm3e-vs-hbm4-vs-hbm4e-llm-inference-guide/): Bandwidth-bound decode explained, HBM generation spec comparison, per-GPU TPS ceilings for 70B and MoE models, quantization as a bandwidth multiplier, and a cost-per-bandwidth decision matrix
- [AMD MI400 vs NVIDIA B300](https://www.spheron.network/blog/amd-mi400-vs-nvidia-b300/): CDNA 5 vs Blackwell Ultra specs, LLM inference projections, ROCm vs CUDA, and GPU cloud pricing
- [Intel Gaudi 3 vs NVIDIA H200 and B200: LLM Inference Benchmarks and Migration Guide (2026)](https://www.spheron.network/blog/intel-gaudi-3-vs-nvidia-h200-b200-llm-inference-2026/): Spec comparison, Llama 70B and DeepSeek V4 benchmarks, SynapseAI vs CUDA stack, cost-per-million-tokens analysis, and vLLM-HPU migration guide
- [Tenstorrent vs NVIDIA: Open-Source AI Hardware Compared](https://www.spheron.network/blog/tenstorrent-vs-nvidia-open-source-ai-hardware/): Wormhole and Blackhole chip architecture, TT-Metal software stack, Llama 70B inference comparison vs H100 SXM5, and when CUDA is still the right call
- [Etched AI Sohu vs NVIDIA: Transformer ASIC vs GPU for LLM Inference (2026)](https://www.spheron.network/blog/etched-ai-sohu-vs-nvidia-transformer-asic-inference/): Sohu chip architecture, 500k tokens/sec Llama 70B claim analysis, Sohu vs B200 and Groq 3 LPU comparison, software stack risk, and a decision framework for transformer-only ASIC vs GPU cloud

## Guides: LLM Training & Fine-Tuning

- [How to Fine-Tune LLMs in 2026](https://www.spheron.network/blog/how-to-fine-tune-llm-2026/): Costs, GPU requirements, and step-by-step workflows for Llama, Qwen, DeepSeek
- [Multi-Node GPU Training Without InfiniBand](https://www.spheron.network/blog/multi-node-gpu-training-without-infiniband/): Tradeoffs and cost analysis for distributed training
- [Axolotl vs Unsloth vs Torchtune](https://www.spheron.network/blog/axolotl-vs-unsloth-vs-torchtune/): Fine-tuning framework comparison
- [LoRA Multi-Adapter Serving](https://www.spheron.network/blog/lora-multi-adapter-serving-gpu-cloud/): Serve multiple LoRA adapters on GPU cloud
- [Spot GPU Training Case Study](https://www.spheron.network/blog/spot-gpu-training-case-study/): How a 12-person AI startup trained a 70B model for $11,200 using spot GPUs
- [Spot GPU Training Resilience: Checkpointing and Preemption Recovery (2026)](https://www.spheron.network/blog/spot-gpu-training-resilience-checkpointing-guide/): Engineering guide to fault-tolerant LLM fine-tuning on spot instances: FSDP/ZeRO-3 checkpointing, optimizer state preservation, async checkpoint offload, and self-healing job controllers
- [Fine-Tuning at Scale Case Study](https://www.spheron.network/blog/fine-tuning-scale-case-study/): Real-world fine-tuning infrastructure patterns
- [GRPO Fine-Tuning on GPU Cloud](https://www.spheron.network/blog/grpo-fine-tuning-gpu-cloud/): Train reasoning models with verifiable rewards using GRPO and TRL, with GPU memory math, vLLM rollout server setup, and spot vs on-demand cost breakdown for H200 and B200
- [DPO Fine-Tuning on GPU Cloud](https://www.spheron.network/blog/dpo-fine-tuning-gpu-cloud/): Direct Preference Optimization training guide with GPU memory math, reference model offload strategies, multi-GPU FSDP and ZeRO-3 setup, 70B recipe on 8x H200 with TRL and Axolotl, and cost benchmarks vs GRPO and PPO
- [RLHF Training Infrastructure: verl, OpenRLHF, and TRL on GPU Cloud](https://www.spheron.network/blog/rlhf-training-infrastructure-verl-openrlhf-trl-gpu-cloud/): Framework comparison and GPU sizing guide for full RLHF pipelines: reward model training, PPO/REINFORCE loops, multi-node setup, and H100 vs B200 cost model for 70B base models
- [Model Merging on GPU Cloud: TIES, DARE, SLERP, and Evolutionary Merging](https://www.spheron.network/blog/model-merging-gpu-cloud-ties-dare-slerp-evolutionary/): Merge LLMs without training using TIES, DARE, SLERP, and CMA-ES evolutionary merging. GPU memory math, mergekit setup on Spheron, cost vs fine-tuning comparison.
- [Distributed LLM Training on GPU Cloud: FSDP, DeepSpeed ZeRO-3, and Megatron-Core Multi-Node Guide](https://www.spheron.network/blog/distributed-llm-training-fsdp-deepspeed-megatron-multi-node/): Multi-node LLM training setup with FSDP2, DeepSpeed ZeRO-3, Megatron-Core parallelism, NCCL tuning, and cost analysis for 70B and 405B models.
- [Federated Learning on GPU Cloud: Deploy Flower, NVIDIA FLARE, and OpenFL (2026)](https://www.spheron.network/blog/federated-learning-gpu-cloud/): Multi-region FL deployment guide covering Flower v2, NVIDIA FLARE 2.6, OpenFL, federated LoRA fine-tuning of Llama 4, differential privacy, and EU AI Act Article 10 compliance mapping.
- [Slurm for AI Workloads on GPU Cloud (2026 Guide)](https://www.spheron.network/blog/slurm-gpu-cloud-ai-training-hpc-scheduler-guide/): HPC-style job scheduling for LLM training and multi-node inference on Slurm clusters, including sbatch recipes, Pyxis/Enroot containers, topology-aware scheduling, and live H100 pricing
- [Continuous Pretraining on GPU Cloud: Domain Adaptation for Frontier LLMs](https://www.spheron.network/blog/continuous-pretraining-llm-gpu-cloud-domain-adaptation/): CPT vs SFT vs DPO decision framework, data composition (domain ratio and replay buffers), catastrophic forgetting prevention with EWC and LoRA-CPT, and multi-node B200 cost recipe for 70B model CPT over 100B tokens.
- [AI Pretraining Data Curation on GPU Cloud: NeMo Curator, Datatrove, and FineWeb-Style Pipelines](https://www.spheron.network/blog/ai-pretraining-data-curation-nemo-curator-datatrove-fineweb-gpu-cloud/): GPU-accelerated data curation pipelines for foundation model training: NeMo Curator cuDF dedup, Datatrove FineWeb-Edu reproduction, quality classifiers, cost math, and hand-off to Megatron-Core and TorchTitan.
- [Liger Kernel LLM Training on GPU Cloud](https://www.spheron.network/blog/liger-kernel-llm-training-gpu-cloud/): Fused Triton kernels for RMSNorm, RoPE, SwiGLU, and FusedLinearCrossEntropy. One-line HuggingFace/TRL/Axolotl patch, 40-60% VRAM savings, benchmarks on H100/H200/B200 with Spheron pricing math
- [MLOps Pipeline Orchestration on GPU Cloud: Kubeflow, ZenML, and Metaflow (2026)](https://www.spheron.network/blog/mlops-pipeline-gpu-cloud-kubeflow-zenml-metaflow-2026/): Compare Kubeflow Pipelines, ZenML, and Metaflow for self-hosted AI training pipelines on GPU cloud, with spot scheduling, checkpoint volumes, and LoRA fine-tuning DAG examples
- [Beyond LoRA: DoRA, GaLore, PiSSA, and VeRA PEFT Guide (2026)](https://www.spheron.network/blog/peft-methods-2026-dora-galore-pissa-vera-guide/): VRAM tables, decision matrix, code recipes, and cost benchmarks for DoRA, GaLore, PiSSA, VeRA, MoRA, and LoRA-FA across 7B to 70B models
- [Sparse Autoencoder Training on GPU Cloud](https://www.spheron.network/blog/sparse-autoencoder-training-gpu-cloud-llm-interpretability/): Production infrastructure for training SAEs against LLMs for mechanistic interpretability, activation steering, and EU AI Act compliance.
- [Synthetic Data Generation on GPU Cloud: Distilabel, Augmentoolkit, and Nemotron-4](https://www.spheron.network/blog/synthetic-data-generation-pipelines-gpu-cloud-distilabel-augmentoolkit-nemotron/): Build GPU-accelerated synthetic data pipelines for LLM fine-tuning. Distilabel instruction generation, Augmentoolkit doc-to-QA, Nemotron-4 340B self-hosting, and cost math on Spheron.

## Guides: LLM Inference & Deployment

- [LLM Deployment Guide](https://www.spheron.network/blog/llm-deployment-guide/): From prototype to production in 5 phases with real cost numbers
- [vLLM Production Deployment 2026](https://www.spheron.network/blog/vllm-production-deployment-2026/): Multi-GPU tensor parallelism, FP8, load balancing on bare metal
- [NeMo Guardrails on GPU Cloud: Production Runtime Safety Rails for Self-Hosted LLMs and Agents (2026)](https://www.spheron.network/blog/nemo-guardrails-production-deployment-llm-gpu-cloud/): Deploy NVIDIA NeMo Guardrails with vLLM on GPU cloud. Covers Colang flows, LlamaGuard 3 comparison, jailbreak detection, PII masking, and sub-80ms rail latency.
- [vLLM Model Runner V2 (MRV2) Deployment Guide](https://www.spheron.network/blog/vllm-model-runner-v2-mrv2-deployment-guide/): Step-by-step MRV2 deployment for 56% faster vLLM inference with GPU-native Triton kernels, EPLB for MoE models, and Eagle3 speculative decoding on Spheron GPUs
- [Deploy TokenSpeed on GPU Cloud](https://www.spheron.network/blog/deploy-tokenspeed-gpu-cloud/): Step-by-step guide to self-hosting LightSeek Foundation's TokenSpeed inference engine with vLLM on Spheron B200 and H200, including MLA kernel tuning for agentic workloads
- [Deploy DeepEP and DeepGEMM on GPU Cloud](https://www.spheron.network/blog/deploy-deepep-deepgemm-moe-inference-kernels-gpu-cloud/): Install and integrate DeepSeek's MoE inference kernels - DeepEP for NVSHMEM-based expert dispatch overlap and DeepGEMM for FP8 grouped GEMM on B200 and H200 clusters with SGLang and vLLM
- [SGLang Production Deployment Guide](https://www.spheron.network/blog/sglang-production-deployment-guide/): Alternative serving engine setup and benchmarks
- [Deploy Llama Stack on GPU Cloud (2026)](https://www.spheron.network/blog/llama-stack-deployment-gpu-cloud/): Step-by-step guide to deploying Meta's Llama Stack production framework on H100, H200, and B200 GPUs. Covers inference, safety (Llama Guard), agents, RAG, eval, and cost comparison.
- [Hugging Face TGI Production Deployment Guide](https://www.spheron.network/blog/hugging-face-tgi-production-deployment-guide/): Step-by-step TGI deployment on GPU cloud with Docker, tensor parallelism via --num-shard, FP8/GPTQ/AWQ quantization, speculative decoding, and a TGI vs vLLM vs SGLang benchmark comparison.
- [Deploy Ray Serve on GPU Cloud](https://www.spheron.network/blog/ray-serve-gpu-cloud-llm-deployment/): Production LLM serving with Python-native orchestration: single-node vLLM setup, multi-node Ray cluster, autoscaling, agent pipeline composition, and cost comparison vs Anyscale
- [vLLM vs TensorRT-LLM vs SGLang Benchmarks](https://www.spheron.network/blog/vllm-vs-tensorrt-llm-vs-sglang-benchmarks/): Head-to-head inference engine comparison
- [Modular MAX and Mojo on GPU Cloud (2026 Guide)](https://www.spheron.network/blog/modular-max-mojo-gpu-cloud-llm-inference/): MAX inference engine setup, Mojo kernel authoring, H100 throughput and TTFT benchmarks vs vLLM and SGLang, and dollar-per-million-token cost analysis on Spheron
- [Inference Engineering Guide 2026](https://www.spheron.network/blog/inference-engineering-guide-2026/): What inference engineering is and how GPU cloud fits in
- [LLM Inference SLO Engineering: TTFT, ITL, and P99 Latency Budgets](https://www.spheron.network/blog/llm-inference-slo-ttft-itl-latency-budget-guide-2026/): How to define and enforce TTFT and inter-token latency SLOs for production LLM services, including error budgets, capacity planning math, autoscaling on SLO burn rate, observability with Prometheus and Grafana, and a worked cost sizing example on Spheron GPU cloud.
- [LLM-as-Judge Evaluation Pipelines on GPU Cloud](https://www.spheron.network/blog/llm-as-judge-evaluation-pipeline-gpu-cloud/): Deploy self-hosted LLM judge infrastructure with vLLM on H200 spot instances - judge model selection, eval framework integration, bias mitigation, and cost playbook for 10k evals
- [AI Red Teaming Infrastructure on GPU Cloud: PyRIT, Garak, and Inspect AI](https://www.spheron.network/blog/ai-red-teaming-gpu-cloud-pyrit-garak-inspect/): Deploy PyRIT, Garak, and Inspect AI against self-hosted vLLM endpoints on Spheron GPU cloud for EU AI Act-compliant jailbreak testing and LLM security evaluation
- [AI Agent Benchmarking Infrastructure on GPU Cloud: SWE-bench, GAIA, Terminal-Bench, OSWorld at Scale](https://www.spheron.network/blog/ai-agent-benchmarking-gpu-cloud-swebench-gaia/): Stand up SWE-bench Verified, GAIA, Terminal-Bench Core, and OSWorld harnesses on GPU cloud - parallel evaluation with Ray, GPU sizing per benchmark, live cost breakdowns, reproducibility checklist, and CI/CD pipeline integration for post-training teams.
- [DSPy on GPU Cloud: Self-Optimizing LLM Pipelines](https://www.spheron.network/blog/dspy-gpu-cloud-self-optimizing-llm-pipelines/): How to deploy DSPy 3.x with MIPROv2, BootstrapFewShot, and self-hosted Llama 4 / Qwen 3.6 Plus backends on Spheron spot GPUs to cut optimization costs vs commercial APIs
- [Mixture of Agents (MoA) on GPU Cloud: Multi-LLM Voting Architecture Deployment (2026)](https://www.spheron.network/blog/mixture-of-agents-gpu-cloud/): Deploy proposer + aggregator MoA stacks with vLLM on GPU cloud - GPU sizing for 4-6 concurrent models, latency tradeoffs, cost math vs single-model inference, and FastAPI orchestration layer.
- [Speculative Decoding Production Guide](https://www.spheron.network/blog/speculative-decoding-production-guide/): Speed up inference with speculative decoding on GPU cloud
- [DFlash Block Diffusion Speculative Decoding](https://www.spheron.network/blog/dflash-block-diffusion-speculative-decoding-gpu-cloud/): 6x faster LLM inference with block diffusion speculative decoding on H100, A100, and L40S with vLLM and SGLang deployment guides
- [Batch LLM Inference on GPU Cloud: Offline Pipelines for 10x Lower Cost](https://www.spheron.network/blog/batch-llm-inference-gpu-cloud/): Offline batch processing pipelines for document summarization, classification, and embedding generation using vLLM, Ray Data, and spot GPUs on Spheron.
- [Deploy Time Series Foundation Models on GPU Cloud](https://www.spheron.network/blog/deploy-time-series-foundation-models-gpu-cloud/): Production deployment guide for Chronos, Moirai, TimesFM, and Lag-Llama - VRAM tables, BentoML/Triton serving, throughput benchmarks, and cost comparison vs AWS Forecast and GCP Vertex AI
- [Deploy Diffusion Language Models on GPU Cloud: LLaDA 2, Mercury, and dLLM Production Guide (2026)](https://www.spheron.network/blog/deploy-diffusion-language-models-dllm-gpu-cloud-2026/): Production deployment guide for LLaDA 2 and Mercury diffusion LLMs on H200/B200 GPU cloud, with parallel token decoding tuning, VRAM sizing, throughput benchmarks vs Llama 4, and autoregressive vs dLLM tradeoff analysis
- [Continuous Batching & Paged Attention](https://www.spheron.network/blog/llm-serving-optimization-continuous-batching-paged-attention/): Key serving optimizations explained
- [KV Cache Optimization Guide](https://www.spheron.network/blog/kv-cache-optimization-guide/): Memory management for large context inference
- [Sleep-Time Compute on GPU Cloud](https://www.spheron.network/blog/sleep-time-compute-gpu-cloud/): How to pre-populate KV caches during idle GPU cycles to cut time-to-first-token by 3-5x for persistent agent and RAG workloads, with a vLLM + Redis reference implementation.
- [Semantic Caching for LLM Inference: GPTCache, Redis Vector Cache, and Prompt Cache Setup (2026)](https://www.spheron.network/blog/semantic-cache-llm-inference-gpu-cloud/): Guide to deploying semantic caching in front of LLM serving - covers GPTCache vs Redis vector cache, embedding model selection, threshold tuning, cost math, and co-located stack deployment on GPU cloud.
- [Prefill-Decode Disaggregation](https://www.spheron.network/blog/prefill-decode-disaggregation-gpu-cloud/): Separating prefill and decode for cost-efficient inference
- [Token-Level GPU Pooling for Multi-LLM Marketplace Inference](https://www.spheron.network/blog/token-level-gpu-pooling-multi-llm-marketplace-inference/): Aegaeon-style architecture for serving 100+ LLMs on a shared GPU cluster - token-level scheduling, KV cache pooling, prefill/decode split, goodput math vs dedicated GPUs, and a step-by-step Spheron H200/B200 build guide.
- [Ollama vs vLLM](https://www.spheron.network/blog/ollama-vs-vllm/): When to use each for local and cloud inference
- [OpenAI-Compatible API Self-Hosted](https://www.spheron.network/blog/openai-compatible-api-self-hosted/): Host your own OpenAI-compatible endpoint on GPU cloud
- [Deploy NVIDIA Triton Inference Server on GPU Cloud (2026)](https://www.spheron.network/blog/triton-inference-server-deployment-guide/): Step-by-step guide to deploying Triton with Docker, model repository setup, vLLM backend, dynamic batching, and a Triton vs vLLM vs TensorRT-LLM decision matrix.
- [TensorRT-LLM Production Deployment Guide](https://www.spheron.network/blog/tensorrt-llm-production-deployment-guide/): Engine build pipeline, FP8/INT4/FP4 quantization, multi-GPU TP/PP config, Triton backend, and cost benchmarks on H200 and B200
- [Self-Host AI Coding Assistant on GPU Cloud](https://www.spheron.network/blog/self-host-ai-coding-assistant-gpu-cloud/): Deploy Tabby or Continue with Qwen2.5-Coder on Spheron for private, self-hosted code autocomplete
- [Self-Host Open WebUI and LibreChat on GPU Cloud](https://www.spheron.network/blog/self-host-open-webui-librechat-gpu-cloud/): Production guide to deploying Open WebUI or LibreChat as a private team ChatGPT on Spheron GPU cloud with vLLM backend, SSO, RAG, and concurrent-user benchmarks.
- [Token Factory on GPU Cloud: Maximize Tokens per Watt (2026 Guide)](https://www.spheron.network/blog/token-factory-gpu-cloud-tokens-per-watt-guide/): How to maximize tokens per watt for AI inference using the token factory framework, with GPU benchmarks and live pricing on Spheron.
- [AI Inference Power Consumption and GPU Electricity Costs](https://www.spheron.network/blog/ai-inference-power-electricity-cost-2026/): GPU TDP reference, electricity cost calculator, cooling overhead, regional price variance, and how GPU cloud pricing eliminates the power bill variable
- [Why Your LLM Inference Is Slow (And How to Fix It)](https://www.spheron.network/blog/llm-inference-slow/): Seven common causes with fixes: VRAM spillover, no KV cache, FP16 overhead, static batching, and more
- [Inference-Time Compute Scaling on GPU Cloud](https://www.spheron.network/blog/inference-time-compute-scaling-gpu-cloud/): How reasoning models spend more GPU per query for better answers, with GPU sizing and cost control
- [llm-d on Kubernetes: Disaggregated LLM Inference](https://www.spheron.network/blog/llm-d-kubernetes-disaggregated-inference-guide/): CNCF Sandbox project for prefill/decode disaggregation on Kubernetes with H100/B200 configs
- [Mamba-3 and State Space Models on GPU Cloud (2026 Guide)](https://www.spheron.network/blog/mamba-3-state-space-model-gpu-cloud-deployment/): Deploy Mamba-3 SSM inference on GPU cloud. Covers SSM vs transformer GPU economics, VRAM sizing, vLLM/SGLang deployment, and long-context throughput benchmarks
- [Test-Time Training on GPU Cloud: Deploy TTT Layers for Adaptive LLM Inference (2026 Guide)](https://www.spheron.network/blog/test-time-training-gpu-cloud-ttt-layers-adaptive-llm-inference/): What Test-Time Training is and how it differs from inference-time compute scaling. Covers TTT-Linear vs TTT-MLP, inner-loop SGD hardware implications, vLLM TTT adapter deployment on Spheron L40S and RTX 5090, throughput/latency tradeoffs by inner-loop step count, and a cost comparison of TTT vs LoRA per-user adapters.
- [Deploy xLSTM and RWKV-7 on GPU Cloud (2026)](https://www.spheron.network/blog/xlstm-rwkv7-linear-attention-gpu-cloud-deployment-2026/): Step-by-step deployment guide for xLSTM 7B and RWKV-7 World v3 with transformers + xlstm and rwkv.cpp, VRAM sizing, benchmarks vs Llama 3.3 70B, and cost-per-million-token analysis
- [Deploy Liquid AI LFM2 Models (LFM2-8B-A1B, LFM2-2.6B) on GPU Cloud](https://www.spheron.network/blog/liquid-foundation-models-lfm-deployment-gpu-cloud-2026/): Production setup guide for Liquid AI's non-transformer LFM family on GPU cloud. Covers VRAM sizing, native vLLM setup (no adapter required), throughput benchmarks vs Llama 3.1 8B, and cost-per-million-token analysis on L40S and H100.
- [Heterogeneous GPU Inference: Mix GPU Types to Cut Costs by 40%](https://www.spheron.network/blog/heterogeneous-gpu-inference-cost-optimization/): Run prefill on H100/B200 and decode on L40S/A100 to reduce inference cost 25-40%, with Dynamo and vLLM setup guide
- [Deploy Microsoft Phi-5 on GPU Cloud (2026)](https://www.spheron.network/blog/deploy-phi-5-gpu-cloud/): Hardware requirements, VRAM sizing, vLLM/SGLang deployment, AWQ/GPTQ/MXFP4 quantization, and a cost comparison vs OpenAI gpt-4o-mini for Phi-5 self-hosted inference.
- [Deploy Small Language Models on GPU Cloud (2026)](https://www.spheron.network/blog/deploy-small-language-models-gpu-cloud/): Enterprise SLM deployment guide covering Phi-3, Mistral 7B, Gemma 2 9B, and Llama 3.2 with vLLM, Ollama, LoRA fine-tuning, and a cost comparison vs OpenAI and Anthropic APIs.
- [GPT-6 vs Self-Hosted LLMs: Cost, Latency, and Privacy in 2026](https://www.spheron.network/blog/gpt-6-vs-self-hosted-llm-2026/): Cost crossover analysis comparing GPT-6 API pricing against self-hosted Nemotron Ultra 253B, DeepSeek V4, GLM-5.1, and Qwen3-235B-A22B on Spheron GPU Cloud, with vLLM deployment quickstart

## Guides: Model Deployment Tutorials

- [Deploy DeepSeek-OCR on GPU Cloud: Self-Host Production Document and Visual OCR Inference (2026)](https://www.spheron.network/blog/deploy-deepseek-ocr-gpu-cloud/): Production deployment guide for DeepSeek-OCR on Spheron GPU cloud using vLLM/SGLang, covering VRAM sizing, FastAPI endpoint, cost-per-million-pages comparison, and RAG integration with Qdrant and ColPali.
- [Self-Host Document Intelligence: Docling, Marker, and MinerU for RAG Ingestion](https://www.spheron.network/blog/self-host-document-intelligence-docling-marker-mineru-rag-guide/): Compare IBM Docling, Marker, and MinerU for self-hosted PDF parsing on GPU cloud, with Docker/Kubernetes deployment and end-to-end RAG ingestion pipeline guide.
- [Deploy DeepSeek V4](https://www.spheron.network/blog/deploy-deepseek-v4-gpu-cloud/): Step-by-step GPU cloud deployment
- [Deploy Llama 4](https://www.spheron.network/blog/deploy-llama-4-gpu-cloud/): GPU requirements and deployment walkthrough
- [Deploy Qwen 3](https://www.spheron.network/blog/deploy-qwen3-gpu-cloud/): Setup guide for Qwen 3 on GPU cloud
- [Deploy Gemma 4](https://www.spheron.network/blog/deploy-gemma-4-gpu-cloud/): Google's open model on GPU cloud
- [Deploy Vision-Language Models](https://www.spheron.network/blog/deploy-vision-language-models-gpu-cloud/): Multi-modal model deployment guide
- [Deploy SmolVLM and SmolVLA on GPU Cloud](https://www.spheron.network/blog/smolvlm-smolvla-gpu-cloud-edge-ai-robotics/): Deploy Hugging Face SmolVLM (256M-2.2B) and SmolVLA on GPU cloud with VRAM sizing, vLLM deployment, benchmarks vs frontier VLMs, and cost-per-task math on RTX 4090 and L40S
- [Deploy OpenVLA on GPU Cloud](https://www.spheron.network/blog/deploy-openvla-gpu-cloud/): Self-host OpenVLA 7B on H100, A100, or L40S with vLLM action-token decoding, LoRA fine-tuning on custom robot embodiments, closed-loop latency tuning, and edge/cloud deployment patterns
- [Deploy WAN 2.1 AI Video Generation](https://www.spheron.network/blog/deploy-wan-2-1-ai-video-generation-gpu-setup/): Video generation model setup
- [NVIDIA NIM Self-Host Guide](https://www.spheron.network/blog/nvidia-nim-self-host-deployment-guide/): Deploy NVIDIA NIM containers on Spheron
- [Deploy DeepSeek R2](https://www.spheron.network/blog/deploy-deepseek-r2-gpu-cloud/): Self-host the open-source MoE reasoning model with vLLM, FP8 quantization, and H100/H200/B200 benchmarks
- [Deploy DeepSeek V3.2 Speciale](https://www.spheron.network/blog/deploy-deepseek-v3-2-speciale/): Hardware requirements and vLLM setup for the top-tier open-source reasoning model
- [Deploy Gemma 3](https://www.spheron.network/blog/deploy-gemma-3-gpu-cloud/): Run Gemma 3 (1B-27B) with vLLM or Ollama, GPU requirements and cost breakdown
- [Deploy GLM-5.1](https://www.spheron.network/blog/deploy-glm-5-1-gpu-cloud/): Self-host the 754B MoE model with vLLM and SGLang, GPU configs and benchmarks
- [Deploy GPT-OSS](https://www.spheron.network/blog/deploy-gpt-oss-gpu-cloud/): Self-host OpenAI's first open-source model (20B and 120B MoE) with vLLM and SGLang
- [Deploy MiMo-V2-Flash](https://www.spheron.network/blog/deploy-mimo-v2-flash-gpu-cloud/): Xiaomi's 309B MoE model with vLLM, expert parallelism, and hybrid thinking mode
- [Deploy Open-Source TTS on GPU Cloud](https://www.spheron.network/blog/deploy-open-source-tts-gpu-cloud-2026/): Kokoro, Fish Speech, and Hume TADA deployment with GPU requirements and cost analysis
- [Deploy Whisper v4 and Production ASR on GPU Cloud](https://www.spheron.network/blog/whisper-v4-asr-gpu-cloud-production-guide/): faster-whisper and WhisperX deployment with GPU sizing, streaming transcription, speaker diarization, and batch cost analysis
- [Self-Host Faster-Whisper on GPU Cloud](https://www.spheron.network/blog/faster-whisper-gpu-cloud-production-deployment-guide/): CTranslate2 backend, INT8/FP16 quantization, model size selection, FastAPI reference server, WebSocket streaming with VAD, and cost-per-minute math vs. hosted ASR APIs
- [Deploy Qwen 3.5](https://www.spheron.network/blog/deploy-qwen-3-5-gpu-cloud/): 397B MoE model VRAM requirements and vLLM setup for every model size
- [Deploy FLUX.2 on GPU Cloud (2026)](https://www.spheron.network/blog/deploy-flux2-gpu-cloud-production-guide/): Production FLUX.2 deployment guide: VRAM requirements, FP8 vs GGUF quantization, ComfyUI and diffusers setup, and per-image cost comparison on H100, A100, and RTX 4090.
- [Deploy Qwen 3.6 Plus](https://www.spheron.network/blog/deploy-qwen-3-6-plus-gpu-cloud/): Hybrid MoE with 1M context, VRAM requirements and vLLM setup
- [Deploy Qwen3.5-Omni](https://www.spheron.network/blog/deploy-qwen3-5-omni-gpu-cloud/): Self-host real-time multimodal AI (text, audio, video) with GPU sizing and vLLM setup
- [Deploy Nemotron 3 Super](https://www.spheron.network/blog/nemotron-3-super-deployment-guide/): NVIDIA's hybrid Mamba-Transformer MoE on H100 or B200 with vLLM config and quantization tradeoffs
- [Deploy Nemotron Ultra 253B on GPU Cloud](https://www.spheron.network/blog/deploy-nemotron-ultra-253b-gpu-cloud/): Self-host NVIDIA Nemotron Ultra 253B on a single 8xH100 node with vLLM. Covers FP8/MXFP4 quantization, benchmark comparison vs DeepSeek R1, production tuning, and cost analysis vs NIM API and hyperscalers.
- [Deploy NeuTTS Air](https://www.spheron.network/blog/neutts-air-spheron-voice-ai/): Ultra-realistic on-device voice AI with instant voice cloning, architecture and benchmarks
- [Deploy NVIDIA Cosmos for Synthetic Data Generation](https://www.spheron.network/blog/deploy-nvidia-cosmos-gpu-cloud-synthetic-data/): Deploy NVIDIA Cosmos world foundation models on GPU cloud for physical AI synthetic training data generation
- [Deploy NVIDIA Isaac GR00T N1 on GPU Cloud](https://www.spheron.network/blog/deploy-nvidia-isaac-gr00t-n1-gpu-cloud/): Self-host the humanoid robot foundation model for embodied AI - covers VLA architecture, Isaac Lab setup, LoRA fine-tuning on teleoperation data, and sub-100ms inference pipeline
- [Deploy 3D Gaussian Splatting on GPU Cloud](https://www.spheron.network/blog/deploy-3d-gaussian-splatting-gpu-cloud/): Train and serve 3DGS scenes for AR/VR, robotics simulation, and autonomous driving with GPU requirements, pipeline setup, and cost model
- [Deploy SAM 3 on GPU Cloud](https://www.spheron.network/blog/deploy-sam-3-gpu-cloud/): Production deployment guide for Meta's Segment Anything Model 3 - VRAM sizing, video memory bank tuning, TensorRT export, Triton serving, and cost comparison vs Replicate and managed CV APIs
- [Deploy MiniMax M2.7 on GPU Cloud](https://www.spheron.network/blog/deploy-minimax-m2-7-gpu-cloud/): Step-by-step guide to self-hosting the 229B self-evolving agentic coding model with vLLM and SGLang on H200 and H100 GPU nodes
- [Deploy Mistral Small 4](https://www.spheron.network/blog/deploy-mistral-small-4-gpu-cloud/): Self-host the 119B MoE model with vLLM on 2x H200 or 4x H100, FP8 setup, reasoning tuning, and spot pricing guide
- [Deploy Magistral on GPU Cloud](https://www.spheron.network/blog/deploy-magistral-gpu-cloud/): Step-by-step guide to self-hosting Magistral Small and Medium on H100/H200 with vLLM, including reasoning template setup, KV cache tuning, and live pricing
- [Deploy Ministral 3 on GPU Cloud](https://www.spheron.network/blog/deploy-ministral-3-gpu-cloud/): Self-host Mistral's 3B, 8B, and 14B reasoning and vision models with vLLM on Spheron GPU cloud, with GPU sizing, AWQ quantization, and live pricing
- [Deploy Devstral on GPU Cloud](https://www.spheron.network/blog/deploy-devstral-gpu-cloud/): Self-host Mistral's 24B coding LLM with vLLM on H100, L40S, or RTX 4090, with IDE integration for Continue, Aider, and Cline plus FinOps breakeven vs Cursor
- [Deploy HRM on GPU Cloud: Self-Host a 27M-Parameter Hierarchical Reasoner (2026)](https://www.spheron.network/blog/deploy-hrm-gpu-cloud/): Step-by-step guide to deploying the Hierarchical Reasoning Model on RTX 4090 or A100 with PyTorch and Ray Serve, including ARC-AGI benchmarks and cost comparison vs DeepSeek R1.

## Guides: DePIN / Network Nodes

- [How to Run a Pearl Research Node on GPU Cloud (H100/H200 Setup)](https://www.spheron.network/blog/run-pearl-research-node-gpu-cloud-h100-h200/): Step-by-step setup guide for running a Pearl Research PoUW node on Spheron H100 or H200 bare metal, including pearld sync, Taproot wallet, and vLLM miner configuration

## Guides: CUDA and Framework News

- [CUDA News Today: NVIDIA Toolkit, AMD ROCm, and AI Framework Releases (2026)](https://www.spheron.network/blog/cuda-news-today-nvidia-toolkit-amd-rocm-ai-frameworks-2026/): Weekly digest of CUDA toolkit and ROCm releases, framework compatibility news, driver branches, and library version notes for GPU cloud engineers
- [NVIDIA Transformer Engine: FP8 Mixed Precision on H100 and H200 (2026)](https://www.spheron.network/blog/nvidia-transformer-engine-h100-h200-fp8/): Installation, PyTorch and JAX integration, te.Linear/te.TransformerLayer API, BF16 vs FP8 training throughput benchmarks, inference benchmarks for Llama 3.3 70B and Qwen3 72B, and TE vs vLLM/SGLang/TRT-LLM FP8 decision guide
- [What is FP8 Quantization? Inference Performance, Accuracy, and Hardware Support](https://www.spheron.network/blog/fp8-quantization-inference-performance-hardware-explained/): Definitional guide to FP8: E4M3 vs E5M2 formats, FP8 vs FP16 vs INT8 comparison, hardware support on Hopper and Blackwell, and how vLLM, TensorRT-LLM, and SGLang enable FP8 inference.

## Guides: Kernel Development

- [OpenAI Triton Kernel Development on GPU Cloud](https://www.spheron.network/blog/openai-triton-kernel-gpu-cloud-2026/): Step-by-step guide to writing and shipping custom GPU kernels in Python using Triton 3.x on H100 and B200 GPU cloud, with profiling, vLLM integration, and cost analysis
- [CUDA 13 Tile Programming on GPU Cloud (2026)](https://www.spheron.network/blog/cuda-13-tile-programming-gpu-cloud/): Write custom GPU kernels with CUDA Tile and the cuTile Python DSL on A100 and B300 SXM6 bare-metal instances
- [FlashAttention 2 vs FlashAttention 3: H100 and H200 Speedups, FP8 Support, and Migration Guide (2026)](https://www.spheron.network/blog/flashattention-2-vs-flashattention-3-h100-h200-guide/): FA2 vs FA3 architecture comparison, H100/H200 throughput benchmarks at 2K-128K sequence lengths, FP8 attention tradeoffs, vLLM/SGLang migration flags, and tokens/sec/$ cost analysis on Spheron Hopper instances.
- [FlashAttention-4 on GPU Cloud: Blackwell Inference Guide (2026)](https://www.spheron.network/blog/flashattention-4-blackwell-gpu-cloud-guide/): SM100 tile architecture explained, FA4 vs FA3 vs FA2 benchmarks, vLLM/SGLang setup on B200/B300, long-context latency gains, and migration guide from Hopper to Blackwell.
- [torch.compile and CUDA Graphs for LLM Inference: Production PyTorch 2.6 Guide](https://www.spheron.network/blog/torch-compile-cuda-graphs-llm-inference-pytorch-2-6/): PyTorch 2.6 production guide covering CUDA graph capture, Inductor backend, persistent NVMe cache for zero cold-start compile, and throughput benchmarks on H200, B200, and RTX Pro 6000.
- [GPU Profiling for AI Workloads: Nsight Compute, Nsight Systems, and PyTorch Profiler](https://www.spheron.network/blog/gpu-profiling-ai-workloads-nsight-compute-pytorch-profiler-guide/): Kernel-level GPU profiling for LLM inference and training using Nsight Compute roofline charts, Nsight Systems timelines, and PyTorch Profiler with HTA on cloud GPUs.
- [PyTorch FlexAttention Production Guide (2026)](https://www.spheron.network/blog/pytorch-flexattention-production-guide-gpu-cloud/): score_mod and mask_mod APIs for custom attention patterns, torch.compile kernel generation, H100/H200/B200 benchmarks, and vLLM/SGLang integration on bare-metal GPU cloud.

## Guides: Infrastructure & Architecture

- [Production GPU Cloud Architecture](https://www.spheron.network/blog/production-gpu-cloud-architecture/): Failover, monitoring, and reliability patterns for marketplace GPU clouds
- [Kubernetes GPU Orchestration 2026](https://www.spheron.network/blog/kubernetes-gpu-orchestration-2026/): DRA, KAI Scheduler, and Grove setup
- [SkyPilot Multi-Cloud GPU Orchestration Guide (2026)](https://www.spheron.network/blog/skypilot-multi-cloud-gpu-orchestration-guide/): Install SkyPilot, register Spheron as a custom cloud target, launch cost-aware vLLM jobs across providers, enable spot recovery with managed jobs, and deploy LLM endpoints via SkyServe
- [NVIDIA Run:ai on GPU Cloud: Scheduling, Fractional GPU, and Multi-Tenant Quotas](https://www.spheron.network/blog/nvidia-runai-gpu-cloud-kubernetes-scheduling-guide/): Run:ai architecture, Helm installation on Kubernetes, GPU quota projects, fractional GPU sharing vs MIG/MPS, gang scheduling for distributed training, and licensing math vs Kueue and KAI Scheduler
- [Migrate from AWS/GCP/Azure](https://www.spheron.network/blog/migrate-from-aws-gcp-azure/): Step-by-step migration to alternative GPU clouds
- [Serverless vs On-Demand vs Reserved GPU](https://www.spheron.network/blog/serverless-gpu-vs-on-demand-vs-reserved/): Choosing the right GPU billing model
- [Dedicated vs Shared GPU Memory](https://www.spheron.network/blog/dedicated-vs-shared-gpu-memory/): When to use each allocation approach
- [GPU Monitoring for ML](https://www.spheron.network/blog/gpu-monitoring-for-ml/): Tracking utilization, thermals, and cost efficiency
- [LLM Observability on GPU Cloud: Langfuse, Arize Phoenix, Helicone](https://www.spheron.network/blog/llm-observability-gpu-cloud-langfuse-arize-phoenix-helicone/): Self-host LLM tracing and observability with Langfuse, Arize Phoenix, and Helicone on GPU Cloud. Covers OpenTelemetry instrumentation for vLLM/SGLang/TGI, DCGM metric correlation, and EU AI Act compliance.
- [100 Concurrent AI Agents Case Study](https://www.spheron.network/blog/100-concurrent-ai-agents-case-study/): Running agent infrastructure at scale on GPU cloud
- [MCP Server GPU Deployment](https://www.spheron.network/blog/mcp-server-gpu-deployment/): Deploy MCP servers on dedicated GPU instances
- [Agentic RAG on GPU Cloud](https://www.spheron.network/blog/agentic-rag-gpu-infrastructure-guide/): Deploy embedding, vector search, and LLM on one stack with sub-200ms TTFT
- [GraphRAG Deployment Guide 2026](https://www.spheron.network/blog/graphrag-gpu-cloud-deployment-guide/): Full pipeline from entity extraction with vLLM to community detection, graph storage with Neo4j or Kuzu, cost analysis, and production deployment on H100 and H200 GPUs on Spheron
- [ColPali and Multimodal Document RAG on GPU Cloud](https://www.spheron.network/blog/colpali-multimodal-document-rag-gpu-cloud/): Deploy ColPali and ColQwen2.5 for visual PDF and slide retrieval without OCR, with VRAM sizing, Qdrant multi-vector setup, and end-to-end pipeline deployment on Spheron H200 and B200.
- [NVIDIA OpenShell and Agent Toolkit](https://www.spheron.network/blog/nvidia-openshell-agent-toolkit-gpu-cloud-guide/): Deploy secure agentic AI with NemoClaw, seccomp sandboxing, and H100/B200 GPU sizing
- [AI's Memory Wall Problem](https://www.spheron.network/blog/ai-memory-wall-inference-latency-guide-2026/): Why more GPUs don't fix inference latency when you're memory-bound, and how to fix it
- [Scale AI Agent Fleets on GPU Cloud: MCP Orchestration and Autoscaling Guide](https://www.spheron.network/blog/scale-ai-agent-fleets-gpu-cloud-mcp-orchestration/): How to scale from 1 to 100+ AI agents in production using MCP orchestration, autoscaling patterns, and GPU cost optimization.
- [Browser-Use and Computer-Use AI Agent Deployment on GPU Cloud](https://www.spheron.network/blog/browser-use-computer-use-agent-gpu-cloud/): Self-host operator-style browser agents with VLM backends (Qwen2.5-VL, Llama 4 Vision, InternVL3), headless browser fleets, VRAM sizing, and cost vs. Anthropic Computer Use API
- [Deploy CrewAI on GPU Cloud: Production Multi-Agent Workflows with Self-Hosted LLM Inference (2026 Guide)](https://www.spheron.network/blog/deploy-crewai-gpu-cloud-production-multi-agent-guide/): Production deployment guide for CrewAI multi-agent workflows backed by vLLM on Spheron GPUs, covering GPU sizing, cost math, memory persistence, and observability.
- [Agent Memory Infrastructure on GPU Cloud: Mem0, Zep, and Persistent Vector Memory (2026)](https://www.spheron.network/blog/agent-memory-gpu-cloud-mem0-zep-guide/): Deploy Mem0 and Zep with self-hosted GPU-backed embedding and summarization models on Spheron for production AI agent memory
- [LangGraph vs LangChain: Production AI Agent Decision Guide (2026)](https://www.spheron.network/blog/langgraph-vs-langchain/): When to use LangGraph vs LangChain, architectural differences, migration patterns, and GPU infrastructure for self-hosted agent deployments
- [EU AI Act Compliance on GPU Cloud (2026)](https://www.spheron.network/blog/eu-ai-act-compliance-gpu-cloud-guide-2026/): Risk classification, data residency rules, model governance requirements, and a step-by-step compliance checklist for AI teams deploying on GPU cloud infrastructure
- [Confidential GPU Computing with NVIDIA TEE (2026)](https://www.spheron.network/blog/confidential-gpu-computing-nvidia-tee-encrypted-vram/): Deploy LLMs with encrypted VRAM, remote attestation, and KMS integration on H100/H200/B200 for HIPAA, PCI-DSS, and ITAR regulated workloads
- [NVIDIA DGX Spark + GPU Cloud Pipeline](https://www.spheron.network/blog/nvidia-dgx-spark-gpu-cloud-pipeline/): Local-to-cloud AI development guide for DGX Spark buyers: when to stay local, how to containerize, and how to deploy to Spheron for production inference
- [What is NVLink? GPU Interconnect Bandwidth Explained (2026)](https://www.spheron.network/blog/what-is-nvlink-gpu-interconnect-bandwidth-explained/): Definitive guide to NVLink bandwidth across NVLink 1.0-5.0, NVLink vs PCIe comparison table, NVSwitch architecture, and when NVLink is required for AI training and inference
- [GPU Networking for AI Clusters: InfiniBand vs RoCE vs Spectrum-X](https://www.spheron.network/blog/gpu-networking-infiniband-roce-spectrum-x-guide/): InfiniBand NDR vs RoCEv2 vs NVIDIA Spectrum-X decision guide with bandwidth benchmarks, cost math, and workload-based fabric selection for AI training and inference clusters
- [Self-Host Vector Databases on GPU Cloud: Qdrant, Milvus, Weaviate](https://www.spheron.network/blog/self-host-vector-database-gpu-cloud-qdrant-milvus-weaviate/): GPU-accelerated Qdrant, Milvus CAGRA, and Weaviate production deployment with HNSW tuning, sharding, replica strategy, and co-location with vLLM for sub-50ms RAG
- [Self-Host Perplexity-Style AI Search: Perplexica, Morphic, and SearXNG on GPU Cloud (2026)](https://www.spheron.network/blog/self-host-ai-search-perplexica-morphic-gpu-cloud/): Step-by-step tutorial deploying Perplexica with vLLM and Llama 3.3 70B on H100, including GPU sizing, cost benchmarking vs Perplexity Pro, and semantic caching.
- [AI Agent Code Execution Sandboxes on GPU Cloud: E2B, Daytona, and Firecracker (2026)](https://www.spheron.network/blog/ai-agent-code-execution-sandbox-e2b-daytona-firecracker/): Self-host Firecracker microVMs and E2B OSS on bare metal GPU instances with GPU passthrough, sandbox pooling, gVisor vs Firecracker isolation, and per-execution cost analysis vs managed E2B
- [Deploy OpenHands on GPU Cloud: Self-Host the Open-Source AI SWE Agent (2026 Guide)](https://www.spheron.network/blog/deploy-openhands-gpu-cloud/): Self-host OpenHands autonomous SWE agent with vLLM inference backend on H100/H200, MIG-based concurrent agents, and cost math vs Devin and GitHub Copilot Workspace.
- [NCCL Tuning for Multi-GPU LLM Training (2026)](https://www.spheron.network/blog/nccl-tuning-multi-gpu-llm-training-2026/): Complete NCCL environment variable and topology guide for distributed LLM training, with NCCL_IB_HCA, GDRDMA, hang avoidance, and a real 8xH200 all-reduce tuning walkthrough
- [GPU Inference Autoscaling with KEDA and Knative on Kubernetes](https://www.spheron.network/blog/keda-knative-gpu-autoscaling-kubernetes-llm-cold-start/): End-to-end guide to cold-start optimization and scale-to-zero for LLM serving using KEDA queue-depth triggers, Knative Serving, CRIU checkpointing, and a cost model across traffic patterns
- [KServe vs Seldon Core vs BentoML on GPU Cloud](https://www.spheron.network/blog/kserve-vs-seldon-core-vs-bentoml-kubernetes-ml-serving-guide/): Architecture comparison, GPU support matrix, autoscaling, and step-by-step Llama 3 70B deployment for all three Kubernetes ML serving operators
- [Parallel File Systems for AI on GPU Cloud: WekaIO, Lustre, and BeeGFS Guide](https://www.spheron.network/blog/parallel-file-systems-ai-gpu-cloud-wekaio-lustre-beegfs-guide/): Production deployment guide for WekaIO, Lustre, and BeeGFS on GPU cloud, with MDT/OST sizing, BeeOND scratch tiers, checkpoint throughput benchmarks, and 8/32/128-node reference architectures.

## Guides: Scientific Computing & HPC

- [NVIDIA Parabricks on GPU Cloud: Genomics Pipelines on H100 and B200 (2026)](https://www.spheron.network/blog/nvidia-parabricks-gpu-cloud-genomics-guide/): Deploy Parabricks for 50x faster FASTQ-to-VCF WGS pipelines. GATK/DeepVariant benchmarks, hardware sizing, Nextflow integration, and cost-per-genome vs AWS HealthOmics.
- [Deploy NVIDIA Holoscan on GPU Cloud: Real-Time Sensor AI for Medical Imaging and Industrial Inspection](https://www.spheron.network/blog/nvidia-holoscan-gpu-cloud-sensor-ai-guide/): Guide to deploying NVIDIA Holoscan SDK on bare-metal GPU cloud for sensor AI pipelines including endoscopy, ultrasound, and industrial inspection. Covers GXF architecture, L40S/H100/RTX Pro 6000 sizing, HIPAA/FDA SaMD/EU MDR compliance, and batch reprocessing workflows.

## Guides: Cost & Provider Comparisons

- [AWS/GCP/Azure GPU Alternative](https://www.spheron.network/blog/aws-gcp-azure-gpu-alternative/): Why teams are leaving hyperscalers for GPU marketplaces
- [Avoid Unexpected AWS GPU Costs](https://www.spheron.network/blog/avoid-unexpected-aws-costs/): Hidden fees and how to eliminate them
- [GPU Cloud Egress and Data Transfer Costs for AI Workloads (2026)](https://www.spheron.network/blog/gpu-cloud-egress-data-transfer-costs-ai-workloads-2026/): Breaks down egress pricing across AWS, GCP, Azure, and neoclouds; includes LLM token streaming bandwidth math, real-world TCO comparisons, and a migration checklist for moving egress-heavy AI workloads to zero-egress providers.
- [AI Inference Cost Economics 2026](https://www.spheron.network/blog/ai-inference-cost-economics-2026/): Unit economics of inference at scale
- [GPU Cost Per Token: LLM Inference Benchmark 2026](https://www.spheron.network/blog/gpu-cost-per-token-benchmark-llm-inference-2026/): Cross-model, cross-GPU cost-per-token benchmarks for Llama 4, Qwen 3, Gemma 4, DeepSeek V3, and Mistral with quantization impact and live Spheron pricing
- [GPU Cloud for Startups 2026](https://www.spheron.network/blog/gpu-cloud-startups-2026/): How early-stage teams should approach GPU infrastructure
- [How Renting GPUs Cuts Training Costs](https://www.spheron.network/blog/renting-gpus/): Rental vs on-prem cost comparison over 3 years
- [Spheron vs RunPod](https://www.spheron.network/blog/spheron-vs-runpod/): Feature and pricing comparison
- [Spheron vs Vast.ai](https://www.spheron.network/blog/spheron-vs-vastai/): Full VM access and pricing comparison
- [Spheron vs CoreWeave](https://www.spheron.network/blog/spheron-vs-coreweave/): Cost and flexibility comparison
- [Spheron vs Crusoe Cloud](https://www.spheron.network/blog/spheron-vs-crusoe/): Direct comparison of Spheron's on-demand GPU pricing vs Crusoe's stranded-gas reserved-capacity model, covering H100/H200/B200 pricing, sustainability claims, contract terms, and networking architecture
- [Spheron vs Lambda Labs](https://www.spheron.network/blog/spheron-vs-lambda-labs/): Direct head-to-head comparison of Spheron and Lambda Labs covering H100 pricing, billing models, availability, multi-node training, and deployment experience.
- [RunPod Alternatives](https://www.spheron.network/blog/runpod-alternatives/): Comparing the top RunPod competitors
- [RunPod H100 Pricing 2026: Per-Hour and Serverless Cost vs Spheron](https://www.spheron.network/blog/runpod-h100-pricing-2026/): RunPod H100 pricing across Community Cloud, Secure Cloud, and Serverless tiers - per-second billing math, hidden fees, and direct comparison against Spheron, Lambda, and CoreWeave
- [CoreWeave Alternatives](https://www.spheron.network/blog/coreweave-alternatives/): Options beyond CoreWeave for GPU cloud
- [LLM Inference On-Premise vs GPU Cloud: 2026 Cost Breakdown](https://www.spheron.network/blog/llm-inference-on-premise-vs-cloud/): Total cost of ownership analysis, break-even utilization thresholds, and a 5-question decision framework for teams choosing between on-premise GPU servers and GPU cloud for LLM inference.
- [AWS Outages and Neo Clouds](https://www.spheron.network/blog/aws-outages-neo-clouds/): How the October 2025 AWS outage impacted AI teams and why neo cloud GPU providers offer more resilient alternatives
- [Google TPU Trillium v6 vs NVIDIA B200 for LLM Inference (2026)](https://www.spheron.network/blog/google-tpu-trillium-v6-vs-nvidia-b200-llm-inference/): Architecture comparison, Llama 4 and DeepSeek inference benchmarks, cost per million tokens, software ecosystem differences, and migration guide from CUDA to TPU.

## Product Updates

- [Spheron April 2026 Product Update](https://www.spheron.network/blog/spheron-april-2026-product-update/): v1.15.0 through v1.17.1 release notes: Sesterce and Spheron AI persistent volumes, hot-attach, stale-lock fix, marketplace redesign, team volume discounts on storage, NVLink GPU flagging in the API, and API reference restructure

## Platform Details

### What Spheron Offers

Spheron is a GPU compute marketplace. You browse live pricing from multiple data center partners, pick a GPU configuration, and deploy in 60-90 seconds. Every instance comes with full SSH root access and a dedicated IP.

**GPU options:** NVIDIA H100, B200, B300, H200, GH200, A100, L40S, RTX PRO 6000, RTX 5090, RTX 4090.

**Instance types:** Virtual machines (quick provisioning, cost-efficient) or bare metal servers (zero hypervisor overhead, maximum performance). Multi-GPU configs up to 8x per node. Cluster deployments up to 80+ GPUs with InfiniBand (400 Gb/s) for distributed training.

**Billing:** Per-minute granularity, no minimum commitment, no long-term contracts. Pay-as-you-go with credit card, bank transfer, or stables (USDT, USDC).

**Pre-configured templates:** PyTorch, TensorFlow, CUDA, JAX, Jupyter, NVIDIA Container Toolkit, and custom Docker images.

**Compliance:** All data centers are Tier 2/3/4 compliant. HIPAA, ISO 27001, SOC 2 Type I/II certifications available. 99.9% uptime SLA.

**Networking:** InfiniBand (400 Gb/s) and NVLink available on select providers for multi-node distributed training. Dedicated IPs for every instance.

### Who Uses Spheron

AI startups building LLM products. ML engineers and data scientists running training and inference. Enterprise teams deploying production AI platforms on compliant infrastructure. Research institutions and academic labs needing access to H100/B200 without capital investment. Generative AI developers working with Stable Diffusion, ComfyUI, and video generation. AI agent developers running distributed inference infrastructure.

### How Spheron Compares to Hyperscalers

Spheron aggregates supply from multiple data center partners, creating a competitive marketplace where prices reflect real supply and demand. This typically results in significantly lower per-hour GPU costs compared to AWS, Google Cloud, and Azure. Unlike hyperscalers, Spheron offers no vendor lock-in (switch providers through one platform), bare metal options alongside VMs, and per-minute billing without reserved instance requirements.

### Supported Frameworks & Tools

PyTorch (2.x with CUDA 12.1+), TensorFlow 2.x, JAX, Hugging Face Transformers, DeepSpeed, Megatron-LM, NVIDIA Triton Inference Server, vLLM, SGLang, RAPIDS (cuDF, cuML, cuGraph), ONNX Runtime, Docker, Kubernetes with NVIDIA Container Toolkit.

## FAQ

- **Is it VM or bare metal?** Both. Choose VMs for quick provisioning or bare metal for maximum performance. Switch between them from the dashboard.
- **Do I get a dedicated IP?** Yes. Every instance includes a dedicated IP with full SSH root access.
- **Can I run containers?** Yes. Full root access with Docker and Kubernetes support. NVIDIA Container Toolkit is pre-installed.
- **Is InfiniBand supported?** On select H100 providers. 400 Gb/s InfiniBand with GPUDirect RDMA. Availability shown in the dashboard before you deploy.
- **What uptime can I expect?** 99.9% availability SLA from Tier 3/4 data centers with redundant power, cooling, and networking.
- **How fast is deployment?** 60-90 seconds for H100, 45-75 seconds for A100. Pre-warmed infrastructure with 1-click deployment.
- **Is there a minimum rental period?** No. Per-minute billing, no contracts, no minimum spend.
- **Multi-GPU support?** Up to 8x GPUs per node with NVLink. Bare metal clusters up to 80+ GPUs with InfiniBand for distributed training.
- **Spot instances?** Yes. Up to 70% savings for fault-tolerant workloads. Best for training jobs with checkpointing.
- **What regions?** US, Europe, and Canada with ongoing expansion.
- **Stables payments?** Yes. USDT and USDC accepted alongside credit card and bank transfer.

## Contact & Community

- [Launch GPU Instance](https://app.spheron.ai): Sign up and deploy in under 2 minutes
- [Enterprise Consultation](https://meetings-eu1.hubspot.com/prashant-maurya): For 100+ GPU deployments, custom sourcing, and dedicated support
- [Discord Community](https://sphn.wiki/discord): Technical support, peer discussions, and platform announcements
- [Twitter/X @SpheronAI](https://twitter.com/spheronai): Product updates and GPU availability announcements
- [LinkedIn](https://linkedin.com/company/spheronai): Company news and business updates
- [GitHub](https://github.com/spheron-core): Open-source tools and SDK libraries
- [Email](mailto:info@spheron.ai): General inquiries and partnership opportunities

## Optional

- [FP4 Quantization on Blackwell](https://www.spheron.network/blog/fp4-quantization-blackwell-gpu-cost/): Cost implications of FP4 on B200/B300
- [MXFP4 Microscaling Quantization Guide](https://www.spheron.network/blog/mxfp4-microscaling-quantization-gpu-cloud/): Step-by-step guide to MXFP4/NVFP4 quantization using MR-GPTQ and TensorRT Model Optimizer, with vLLM and TensorRT-LLM deployment on Blackwell GPUs
- [AWQ Quantization for LLM Deployment](https://www.spheron.network/blog/awq-quantization-guide-llm-deployment/): Practical quantization guide
- [NVIDIA TensorRT Model Optimizer (ModelOpt) Quantization Guide](https://www.spheron.network/blog/tensorrt-model-optimizer-modelopt-quantization-guide/): Step-by-step guide to ModelOpt FP8, INT4, and FP4 quantization on GPU cloud, with calibration, export to TensorRT-LLM/vLLM/SGLang, accuracy benchmarks, and cost-per-token analysis on H100, H200, and B200.
- [LLM Pruning with SparseGPT and Wanda](https://www.spheron.network/blog/llm-pruning-sparsegpt-wanda-gpu-cloud/): 2:4 structured sparsity walkthrough for Llama 3.3 70B on H100, SparseGPT vs Wanda comparison, vLLM sparse kernel deployment, and pruning + AWQ stacking guide
- [MoE Inference Optimization](https://www.spheron.network/blog/moe-inference-optimization-gpu-cloud/): Mixture-of-experts serving on GPU cloud
- [Model Distillation on GPU Cloud](https://www.spheron.network/blog/model-distillation-gpu-cloud-7b-student-70b-teacher/): 7B student from 70B teacher workflow
- [Fractional GPU Inference](https://www.spheron.network/blog/fractional-gpu-inference-vgpu-mps-right-sizing/): vGPU, MPS, and right-sizing for inference
- [Run Multiple LLMs on One GPU](https://www.spheron.network/blog/run-multiple-llms-one-gpu-mig-time-slicing-guide/): MIG and time-slicing guide
- [NVIDIA Dynamo Disaggregated Inference](https://www.spheron.network/blog/nvidia-dynamo-disaggregated-inference-guide/): NVIDIA's inference disaggregation framework
- [NVIDIA NixL Disaggregated Inference](https://www.spheron.network/blog/nvidia-nixl-disaggregated-inference-guide/): NixL for distributed inference
- [DeepSeek vs Llama 4 vs Qwen 3](https://www.spheron.network/blog/deepseek-vs-llama-4-vs-qwen3/): Open model comparison
- [Open-Weight Frontier Model Showdown 2026: GPT-OSS vs GLM-5.1 vs DeepSeek V4](https://www.spheron.network/blog/open-weight-frontier-model-showdown-2026/): GPU benchmarks, VRAM requirements, and cost-per-token comparison for the three leading open-weight frontier models
- [Kimi K2.5 Guide](https://www.spheron.network/blog/kimi-k2-5-guide/): Moonshot AI's model overview
- [Deploy Kimi K2.6 on GPU Cloud](https://www.spheron.network/blog/deploy-kimi-k2-6-gpu-cloud/): Self-host Moonshot AI's multimodal agentic model with MoonViT vision encoder and 256K context using vLLM and SGLang on H200 and B200
- [ROCm vs CUDA 2026](https://www.spheron.network/blog/rocm-vs-cuda-gpu-cloud-2026/): AMD vs NVIDIA software stack comparison
- [AMD MI300X vs NVIDIA H200](https://www.spheron.network/blog/amd-mi300x-vs-nvidia-h200/): Cross-vendor GPU comparison
- [AMD MI350X vs NVIDIA B200](https://www.spheron.network/blog/amd-mi350x-vs-nvidia-b200/): Next-gen cross-vendor comparison
- [PyTorch vs TensorFlow](https://www.spheron.network/blog/pytorch-vs-tensorflow/): Framework decision guide
- [GPU Shortage 2026](https://www.spheron.network/blog/gpu-shortage-2026/): Supply dynamics and how marketplaces help
- [ComfyUI on GPU Cloud 2026](https://www.spheron.network/blog/comfyui-gpu-cloud-2026/): Running ComfyUI workflows on rented GPUs
- [AI Video Generation GPU Guide](https://www.spheron.network/blog/ai-video-generation-gpu-guide/): GPU requirements for video AI
- [Voice AI GPU Infrastructure](https://www.spheron.network/blog/voice-ai-gpu-infrastructure/): GPU needs for real-time voice AI
- [WebRTC LLM Streaming: Real-Time Voice Agent Infrastructure on GPU Cloud](https://www.spheron.network/blog/webrtc-llm-streaming-voice-agent-gpu-cloud/): WebRTC vs HTTP streaming for voice agents, token streaming over data channels, jitter buffer tuning, LiveKit Agents and Pipecat deployment on H100/B200, and cost-per-concurrent-call breakdown
- [Speech-to-Speech AI on GPU Cloud: Moshi, Sesame CSM, Hertz-dev](https://www.spheron.network/blog/speech-to-speech-gpu-cloud-moshi-sesame-csm-hertz-dev/): Deploy unified S2S models for sub-300ms voice agents with VRAM sizing, streaming inference, and Spheron pricing
- [AI Music Generation GPU Guide: YuE, ACE-Step, MusicGen, Stable Audio Open](https://www.spheron.network/blog/deploy-open-source-ai-music-generation-gpu-cloud-2026/): Deploy self-hosted music AI on GPU cloud with VRAM requirements, batch pipeline setup, and cost-per-song math
- [GPU Infrastructure for AI Coding Tools](https://www.spheron.network/blog/gpu-infrastructure-ai-coding-tools-2026/): Powering AI code assistants
- [GPU Infrastructure for AI Agents](https://www.spheron.network/blog/gpu-infrastructure-ai-agents-2026/): Scaling agent compute
- [Structured Output & Function Calling Guide](https://www.spheron.network/blog/structured-output-function-calling-inference-guide/): Reliable structured output from LLMs
- [RAG Pipeline on Bare Metal Case Study](https://www.spheron.network/blog/rag-pipeline-bare-metal-case-study/): Production RAG infrastructure patterns
- [Reasoning Model Inference Cost Optimization](https://www.spheron.network/blog/reasoning-model-inference-cost-gpu-optimization/): Cutting costs for reasoning-heavy models
- [NVMe KV Cache Offloading](https://www.spheron.network/blog/nvme-kv-cache-offloading-llm-inference/): Extending context with NVMe offloading
- [Ring Attention and Tree Attention on GPU Cloud (2026)](https://www.spheron.network/blog/ring-attention-tree-attention-sequence-parallelism-gpu-cloud/): Setup guide for Ring Attention, Tree Attention, and Striped Attention sequence parallelism to serve 1M-10M token context LLMs on multi-GPU clusters
- [Hybrid Cloud & Edge AI Inference](https://www.spheron.network/blog/hybrid-cloud-edge-ai-inference-guide/): Combining cloud and edge GPU deployment
- [LLM Inference Router](https://www.spheron.network/blog/llm-inference-router-gpu-cloud/): Routing inference requests across GPU instances
- [AI Gateway Setup 2026: LiteLLM, Portkey, and Kong AI Gateway](https://www.spheron.network/blog/ai-gateway-litellm-portkey-kong-gpu-cloud/): Compare LiteLLM, Portkey, and Kong AI Gateway for multi-model LLM traffic. Step-by-step LiteLLM + vLLM deployment on Spheron with hybrid Spheron-first routing, virtual key budgets, and OpenTelemetry observability
- [Self-Host Embedding Models and Rerankers: TEI Deployment Guide](https://www.spheron.network/blog/self-host-embedding-reranker-tei-gpu-cloud/): Production deployment of Hugging Face TEI for BGE-M3, Qwen3-Embedding, and Jina v4 embeddings plus cross-encoder rerankers on GPU cloud, with cost-per-1M-token comparison vs OpenAI and Cohere
- [Multi-Agent AI System GPU Infrastructure](https://www.spheron.network/blog/multi-agent-ai-system-gpu-infrastructure/): Scaling multi-agent systems on GPU cloud
- [LangGraph Studio Production Deployment on GPU Cloud](https://www.spheron.network/blog/langgraph-studio-production-deployment-gpu-cloud/): Deploy LangGraph Studio with a self-hosted vLLM backend on H100, covering Postgres checkpointing, multi-agent patterns, observability, and cost vs LangGraph Cloud
- [Nebius Alternatives](https://www.spheron.network/blog/nebius-alternatives/): GPU cloud alternatives to Nebius
- [Hyperstack Alternatives](https://www.spheron.network/blog/hyperstack-alternatives/): GPU cloud alternatives to Hyperstack
- [Shadeform Alternatives](https://www.spheron.network/blog/shadeform-alternatives/): GPU cloud alternatives to Shadeform
- [Modal Alternatives](https://www.spheron.network/blog/modal-alternatives/): GPU cloud alternatives to Modal
- [Lambda Labs Alternatives](https://www.spheron.network/blog/lambda-labs-alternatives/): GPU cloud alternatives to Lambda
- [Vast.ai Alternatives](https://www.spheron.network/blog/vastai-alternatives/): GPU cloud alternatives to Vast.ai
- [FluidStack Alternatives](https://www.spheron.network/blog/fluidstack-alternatives/): GPU cloud alternatives to FluidStack
- [Paperspace Alternatives](https://www.spheron.network/blog/paperspace-alternatives/): GPU cloud alternatives to Paperspace
- [Latitude Alternatives](https://www.spheron.network/blog/latitude-alternatives/): GPU cloud alternatives to Latitude
- [DataCrunch/Verda Alternatives](https://www.spheron.network/blog/datacrunch-verda-alternatives/): GPU cloud alternatives to DataCrunch and Verda
- [Fireworks AI Alternatives 2026](https://www.spheron.network/blog/fireworks-ai-alternatives/): 10 alternatives to Fireworks AI for serverless LLM inference and fine-tuning, with break-even analysis and migration guide.
- [Together AI Alternatives 2026](https://www.spheron.network/blog/together-ai-alternatives/): 10 GPU cloud alternatives to Together AI for serverless LLM inference and fine-tuning, with pricing comparison and vLLM migration guide
- [Replicate Alternatives 2026](https://www.spheron.network/blog/replicate-alternatives/): 10 GPU cloud alternatives to Replicate for ML model hosting and inference APIs, with per-second billing breakdown and Cog migration guide
- [Anyscale Alternatives 2026](https://www.spheron.network/blog/anyscale-alternatives/): 10 GPU cloud platforms for Ray-native distributed training and inference, covering KubeRay, RLHF workloads, 8x H100 cluster pricing, and migration playbook from Anyscale to self-hosted Ray
- [Hugging Face Inference Endpoints Alternatives: 10 Self-Hosted GPU Cloud Options (2026)](https://www.spheron.network/blog/hugging-face-inference-endpoints-alternatives/): Compares HF Inference Endpoints against 10 GPU cloud alternatives including Spheron, RunPod, Together AI, Modal, and AWS SageMaker, with cost-per-million-token tables, migration playbook for TGI, and Spheron GPU pricing.
- [Baseten Alternatives](https://www.spheron.network/blog/baseten-alternatives/): Comparison of 10 ML inference platforms vs Baseten, covering replica-hour pricing, Truss lock-in, cold starts, and migration to self-hosted vLLM on dedicated GPU
- [Spheron vs Hyperstack](https://www.spheron.network/blog/spheron-vs-hyperstack/): Direct comparison
- [Spheron vs Nebius](https://www.spheron.network/blog/spheron-vs-nebius/): Direct comparison
- [Spheron vs Modal](https://www.spheron.network/blog/spheron-vs-modal/): Direct comparison
- [Spheron vs SF Compute](https://www.spheron.network/blog/spheron-vs-sf-compute/): Direct comparison
- [Spheron vs Shadeform](https://www.spheron.network/blog/spheron-vs-shadeform/): Direct comparison
- [GPU Cloud Providers in Asia-Pacific 2026](https://www.spheron.network/blog/gpu-cloud-providers-asia-pacific-2026/): H100, H200, and B200 availability across Singapore, Tokyo, Sydney, and Seoul with regional pricing tables, latency benchmarks, and APAC data residency rules
- [Top GPU Providers South Korea](https://www.spheron.network/blog/top-gpu-providers-south-korea/): Regional GPU cloud guide
- [GPU Cloud Providers in India 2026](https://www.spheron.network/blog/gpu-cloud-india-2026/): H100, H200, and B200 availability for India teams with INR billing, DPDP Act compliance, domestic provider comparison, and Spheron pricing.
- [Top GPU Rental Marketplaces](https://www.spheron.network/blog/top-gpu-rental/): GPU rental market overview and how platforms like Spheron reshape access to compute
- [GPU Capacity for AI Deployment](https://www.spheron.network/blog/gpu-capacity-for-ai-deployment/): How to plan, source, and optimize GPU infrastructure for AI training
- [GPU Cloud for Video AI 2026](https://www.spheron.network/blog/gpu-cloud-video-ai-2026/): VRAM requirements for Wan 2.1, HunyuanVideo, and AnimateDiff with setup guides
- [Image-to-Video AI on GPU Cloud: Deploy LTX-Video, Wan 2.2 I2V, and Hunyuan Video Avatar (2026)](https://www.spheron.network/blog/image-to-video-gpu-cloud-ltx-wan-hunyuan/): Deploy LTX-Video 0.9.8, Wan 2.2 I2V, and Hunyuan Video Avatar on GPU cloud with VRAM tables and cost-per-second pricing
- [Deploy Wan 2.5 on GPU Cloud: Production Video Generation Setup (2026)](https://www.spheron.network/blog/deploy-wan-2-5-gpu-cloud/): Wan 2.5 deployment on H100 and B200 with ComfyUI and diffusers, VRAM tables, benchmarks, and cost comparison vs Replicate and Fal.ai
- [Fal.ai Alternatives: 10 GPU Clouds for Image, Video, and Diffusion Model Inference (2026)](https://www.spheron.network/blog/fal-ai-alternatives/): Comparison of 10 alternatives to Fal.ai for FLUX.2 and Wan 2.5 inference, with per-image and per-second-of-video cost tables and a migration guide to ComfyUI on bare-metal GPU
- [GPU Cloud for AI Drug Discovery 2026](https://www.spheron.network/blog/gpu-cloud-ai-drug-discovery/): Deploy AlphaFold 3, Boltz-2, and RoseTTAFold All-Atom on GPU cloud with VRAM tables, cost-per-prediction analysis, and step-by-step setup guides for biotech virtual screening pipelines
- [Run Karpathy's autoresearch on GPU Cloud](https://www.spheron.network/blog/karpathy-autoresearch-spheron-gpu/): Set up Andrej Karpathy's autonomous LLM training agent on Spheron in under 10 minutes
- [Run LLMs Locally with Ollama](https://www.spheron.network/blog/run-llms-locally-ollama/): GPU-accelerated local LLM setup covering installation, model selection, quantization, and API integration
- [GGUF Dynamic Quantization on GPU Cloud](https://www.spheron.network/blog/gguf-dynamic-quantization-gpu-cloud/): Deploy LLMs 50% cheaper with Unsloth Dynamic 2.0 and llama.cpp server
- [Google TurboQuant for LLM Inference](https://www.spheron.network/blog/google-turboquant-llm-compression-gpu-cloud/): 6x KV cache compression and 8x attention acceleration using PolarQuant and QJL
- [Rent NVIDIA A100 GPUs](https://www.spheron.network/blog/rent-nvidia-a100-gpus/): A100 80GB rental at $0.76/hour with bare-metal performance and instant provisioning
- [Rent NVIDIA H200 GPUs](https://www.spheron.network/blog/rent-nvidia-h200-gpus/): H200 141GB HBM3e rental with bare-metal access and pay-as-you-go pricing
- [Rent NVIDIA RTX 5090](https://www.spheron.network/blog/rent-nvidia-rtx-5090/): Real-world LLM throughput benchmarks, cost per million tokens, and VRAM guide
- [Rent NVIDIA RTX PRO 6000](https://www.spheron.network/blog/rent-nvidia-rtx-pro-6000/): 96GB GDDR7 benchmarks, 30B AWQ throughput, and cost per million tokens vs alternatives