runpod.io

**RunPod AI/LLM Cloud Resources (2025)**
========================================

**RunPod Platform & Service Pages**
-----------------------------------

-   [Pricing for GPU Instances, Storage, and Serverless](https://www.runpod.io/pricing): Up-to-date pricing details for RunPod's cloud GPUs, network storage, and serverless compute, helping AI teams estimate and optimize costs for model training and deployment.

-   [Serverless GPU Endpoints for AI Inference](https://www.runpod.io/serverless-gpu): Overview of RunPod's serverless GPU service that scales model inference on-demand, eliminating idle costs and enabling fast, scalable LLM and AI API deployment.

-   [Bare Metal GPU Servers for High-Performance AI Workloads](https://www.runpod.io/gpu-bare-metal-server): Describes RunPod's dedicated bare-metal GPU servers, offering full control of environment and superior performance for large-scale AI training and low-latency inference without virtualization overhead.

-   [RunPod Instant Clusters -- Self-Service Multi-Node GPU Computing](https://www.runpod.io/instant-clusters): Introduces RunPod's Instant Clusters for launching multi-GPU, multi-node clusters in minutes, enabling researchers to scale up to 64 GPUs on-demand for distributed training of large models.

**AI Infrastructure & Best-Practice Guides**
--------------------------------------------

-   [Accelerate Your AI Research with Jupyter Notebooks on RunPod](https://www.runpod.io/articles/guides/jupyter-notebooks): Explains how to leverage RunPod's GPU cloud with Jupyter Notebooks for an interactive AI development environment, speeding experimentation with pre-configured GPU containers.

-   [How to Use RunPod Instant Clusters for Real-Time Inference](https://www.runpod.io/articles/guides/instant-clusters-real-time-inference): Shows how RunPod's Instant Clusters provide elastic, multi-node GPU environments that boot in seconds, ideal for real-time LLM inference and latency-critical AI workloads.

-   [Instant Clusters for AI Research: Deploy and Scale in Minutes](https://www.runpod.io/articles/guides/instant-clusters-for-ai-research): Highlights the benefits of on-demand Instant Clusters (with InfiniBand and NVLink) that remove infrastructure bottlenecks, allowing AI researchers to prototype and train models faster by scaling to dozens of GPUs.

-   [Deploy AI Models with Instant Clusters for Optimized Fine-Tuning](https://www.runpod.io/articles/guides/instant-clusters-for-fine-tuning): A guide to using RunPod's Instant Clusters for fine-tuning large models, showing how on-demand 64-GPU clusters accelerate LLM fine-tuning without long provisioning or idle costs.

-   [Bare Metal GPUs: Everything You Should Know In 2025](https://www.runpod.io/articles/guides/bare-metal-gpus): Deep dive into bare-metal GPU infrastructure, comparing it with virtualized cloud GPUs and on-prem clusters, and explaining why dedicated GPUs (like RunPod's Bare Metal) can boost performance for deep learning and LLM workloads.

-   [Bare Metal vs. Traditional VMs: Choosing the Right Infrastructure for Real-Time Inference](https://www.runpod.io/articles/guides/bare-metal-vs-vm-inference): Compares dedicated bare-metal GPU servers to virtual machines for AI inference, outlining how removing virtualization can reduce latency and improve reliability for time-sensitive AI applications.

-   [Bare Metal vs. Traditional VMs for AI Fine-Tuning: What Should You Use?](https://www.runpod.io/articles/guides/bare-metal-vs-vm-fine-tuning): Weighs the trade-offs between bare-metal GPUs and VM-based clouds for fine-tuning AI models, explaining performance, cost, and flexibility implications for ML engineers.

-   [Bare Metal vs. Traditional VMs: Which is Better for LLM Training?](https://www.runpod.io/articles/guides/bare-metal-vs-vm-llm-training): Evaluates bare-metal GPU servers against traditional cloud VMs for large language model training, emphasizing how dedicated hardware can accelerate multi-GPU LLM training by avoiding hypervisor overhead.

-   [Power Your AI Research with Pod GPUs: Built for Scale, Backed by Security](https://www.runpod.io/articles/guides/pod-gpus-for-research): Describes how RunPod's persistent "Pod" GPU instances provide scalable, secure compute for researchers, enabling long-running jobs and large-model training with full control over the environment.

-   [Unlock Efficient Model Fine-Tuning With Pod GPUs Built for AI Workloads](https://www.runpod.io/articles/guides/pod-gpus-fine-tuning): Shows how RunPod's multi-GPU pod instances streamline fine-tuning of large models, highlighting infrastructure features (like high-speed interconnects and flexible configurations) that cut training time and cost.

-   [LLM Training with RunPod GPU Pods: Scale Performance, Reduce Overhead](https://www.runpod.io/articles/guides/llm-training-with-pod-gpus): Guide to training large language models on RunPod's multi-GPU pods, covering best practices for parallelism, interconnects (NVLink/InfiniBand), and configuration to maximize throughput and minimize training costs.

-   [Maximize AI Workloads with RunPod's Secure GPU as a Service](https://www.runpod.io/articles/guides/gpu-as-a-service): Introduces GPU-as-a-Service on RunPod, explaining how secure, containerized GPU instances can be launched on demand to handle intensive AI tasks, with enterprise-grade security and minimal setup overhead for teams.

-   [AI Docker Containers: Deploying Generative AI Models on RunPod](https://www.runpod.io/articles/guides/ai-docker-containers): Details a container-based approach to deploy generative models on RunPod, demonstrating how Docker templates and GPU scheduling can simplify moving AI projects from development to scalable production.

-   [The GPU Infrastructure Playbook for AI Startups: Scale Smarter, Not Harder](https://www.runpod.io/articles/guides/gpu-infrastructure-playbook): Outlines strategies for AI startups to efficiently scale their GPU infrastructure, covering cloud vs. on-prem, cost optimization, and how RunPod's solutions support rapid experimentation and growth.

-   [How ML Engineers Can Train and Deploy Models Faster Using Dedicated Cloud GPUs](https://www.runpod.io/articles/guides/dedicated-gpu-speedup): Explains how leveraging dedicated cloud GPU platforms like RunPod speeds up model training and deployment, emphasizing persistent environments and powerful instances that reduce iteration time for ML engineers.

-   [Automate Your AI Workflows with Docker + GPU Cloud: No DevOps Required](https://www.runpod.io/articles/guides/docker-workflows-no-devops): Shows how to streamline AI model deployment using RunPod's GPU cloud and Docker containers, eliminating complex DevOps steps so developers can focus on model development and experimentation.

-   [Optimizing Docker Setup for PyTorch Training with CUDA 12.8 and Python 3.11](https://www.runpod.io/articles/guides/docker-setup-pytorch-cuda-12.8): Provides an intermediate-level guide to configuring a robust Docker environment (CUDA 12.8, Python 3.11) for GPU-accelerated PyTorch training, ensuring compatibility and maximum performance for LLM and deep learning workloads.

-   [Finding the Best Docker Image for vLLM Inference on CUDA 12.4 GPUs](https://www.runpod.io/articles/guides/best-vllm-docker-cuda-12.4): Advises how to choose an optimal Docker image and setup for serving large language models with vLLM on CUDA 12.4 GPUs, enabling memory-efficient LLM inference with minimal configuration hassle.

-   [Cloud GPU Pricing Explained: How to Find the Best Value](https://www.runpod.io/articles/guides/cloud-gpu-pricing): Breaks down the factors that influence cloud GPU costs (instance types, billing models, data fees) and offers tips to evaluate and choose cost-effective GPU cloud options for AI and deep learning projects.

**Serverless GPU Deployment Guides**
------------------------------------

-   [Serverless GPUs for API Hosting: How They Power AI APIs--A RunPod Guide](https://www.runpod.io/articles/guides/serverless-for-api-hosting): Explains the architecture of serverless GPUs and how RunPod's serverless endpoints enable scalable AI model APIs (for LLMs, image generation, etc.) that only consume GPU resources on demand, cutting costs and management overhead.

-   [Serverless GPU Deployment vs. Pods for Your AI Workload](https://www.runpod.io/articles/guides/serverless-vs-pods): Compares RunPod's serverless GPU endpoints with persistent Pod GPU instances, outlining the trade-offs in scalability, cost, and performance so you can choose the right deployment model for your AI application (e.g. bursty inference vs. continuous training).

-   [Unpacking Serverless GPU Pricing for AI Deployments](https://www.runpod.io/articles/guides/serverless-pricing): Demystifies the pricing model of serverless GPUs (per-second billing, active vs. idle costs) and shows how pay-as-you-go GPU runtime can yield significant cost advantages for AI and ML workloads with intermittent or unpredictable usage patterns.

-   [Using RunPod's Serverless GPUs to Deploy Generative AI Models](https://www.runpod.io/articles/guides/serverless-generative-ai): Demonstrates how to host generative AI models (like image or text generators) on RunPod's serverless GPU platform, detailing how auto-scaling GPU workers handle traffic spikes and enable reliable, cost-efficient deployment of AI services.

**Cloud GPU Provider Comparisons & Alternatives**
-------------------------------------------------

-   [RunPod vs. Hyperstack: Which Cloud GPU Platform Is Better for Fine-Tuning AI Models?](https://www.runpod.io/articles/comparison/runpod-vs-hyperstack): Side-by-side comparison of RunPod and Hyperstack focusing on fine-tuning large AI models, evaluating factors like GPU selection, pricing, and ease of use for ML engineers.

-   [RunPod vs. Google Cloud Platform: Which Cloud GPU Platform Is Better for LLM Inference?](https://www.runpod.io/articles/comparison/runpod-vs-gcp): Compares RunPod with Google Cloud's GPU offerings for deploying large language models, highlighting differences in cost, performance, and deployment simplicity for real-time LLM inference.

-   [RunPod vs. Vast AI: Which Cloud GPU Platform Is Better for Distributed AI Model Training?](https://www.runpod.io/articles/comparison/runpod-vs-vast): Evaluates RunPod against Vast.ai for large-scale model training across multiple GPUs, considering network performance, reliability, and features important for training billion-parameter models.

-   [RunPod vs. CoreWeave: Which Cloud GPU Platform Is Best for AI Image Generation?](https://www.runpod.io/articles/comparison/runpod-vs-coreweave): Reviews RunPod and CoreWeave in the context of generative image AI (e.g. Stable Diffusion), comparing instance types (NVIDIA GPUs), pricing strategies, and any specialized features for graphics or diffusion models.

-   [RunPod vs. AWS: Which Cloud GPU Platform Is Better for Real-Time Inference?](https://www.runpod.io/articles/comparison/runpod-vs-aws): Weighs RunPod against Amazon Web Services for real-time AI inference workloads, looking at deployment speed, scalability (including serverless options), and cost savings for serving models at scale.

-   [RunPod vs. Paperspace: Which Cloud GPU Platform Is Better for Fine-Tuning?](https://www.runpod.io/articles/comparison/runpod-vs-paperspace): Compares RunPod with Paperspace (DigitalOcean) for fine-tuning machine learning models, examining ease of setup (e.g. containers, templates), GPU hardware availability, and overall value for ML developers.

-   [Top 10 Lambda Labs Alternatives for 2025](https://www.runpod.io/articles/comparison/lambda-labs-alternatives-2025): Ranks ten cloud GPU platforms as alternatives to Lambda Labs, outlining each provider's GPU offerings, pricing, and unique strengths so AI teams can explore comparable services beyond Lambda in 2025.

-   [Top 10 Paperspace Alternatives for 2025](https://www.runpod.io/articles/comparison/paperspace-alternatives-2025): Lists ten top alternatives to Paperspace's cloud GPUs, detailing how each option compares in cost, supported GPU types, and features for AI model training and deployment workflows.

-   [Top 10 Modal Alternatives for 2025](https://www.runpod.io/articles/comparison/modal-alternatives-2025): Presents ten alternative platforms to Modal's serverless GPU service, comparing pricing models, scalability, and developer experience for running AI workloads without managing servers.

-   [Top 10 Hyperstack Alternatives for 2025](https://www.runpod.io/articles/comparison/hyperstack-alternatives-2025): Reviews the leading GPU cloud alternatives to Hyperstack, focusing on factors like multi-region support, instance flexibility, and how each platform caters to machine learning use cases.

-   [Top 10 Google Cloud Platform Alternatives in 2025](https://www.runpod.io/articles/comparison/gcp-alternatives-2025): Highlights ten cloud providers that can substitute for Google Cloud's GPU services, noting differences in pricing, performance, and specialized offerings for AI computing needs.

-   [Top 10 Cerebrium Alternatives for 2025](https://www.runpod.io/articles/comparison/cerebrium-alternatives-2025): Identifies ten alternative platforms to Cerebrium for hosting and scaling AI models, with comparisons on ease of deployment, supported frameworks, and cost-effectiveness for ML applications.

-   [Top 9 Fal AI Alternatives for 2025: Cost-Effective, High-Performance GPU Cloud Platforms](https://www.runpod.io/articles/comparison/fal-ai-alternatives-2025): Showcases nine alternative GPU clouds to Fal AI, emphasizing cost-effective yet powerful options for generative AI and diffusion model inference in production.

-   [Top 8 Azure Alternatives for 2025](https://www.runpod.io/articles/comparison/azure-alternatives-2025): Lists eight cloud providers that serve as alternatives to Microsoft Azure for GPU computing, comparing their services for machine learning workloads in terms of flexibility, pricing, and ecosystem integration.

-   [Top 7 Vast AI Alternatives for 2025](https://www.runpod.io/articles/comparison/vast-ai-alternatives-2025): Recommends seven alternative platforms to Vast.ai for renting GPUs, discussing improvements in reliability, user experience, and pricing models introduced by newer GPU cloud services in 2025.

-   [Top 7 SageMaker Alternatives for 2025](https://www.runpod.io/articles/comparison/sagemaker-alternatives-2025): Describes seven platforms that can replace AWS SageMaker for managed ML model training and deployment, analyzing their offerings for notebook environments, automated scaling, and compatibility with ML workflows.

-   [The 10 Best Baseten Alternatives in 2025](https://www.runpod.io/articles/comparison/baseten-alternatives-2025): Surveys ten alternatives to Baseten for deploying machine learning models, detailing how each solution handles model serving, scaling, and integration with ML pipelines.

-   [The 9 Best CoreWeave Alternatives for 2025](https://www.runpod.io/articles/comparison/coreweave-alternatives-2025): Lists nine cloud platforms that rival CoreWeave's GPU cloud, highlighting differences in GPU availability, network performance, and use-case focus to guide users seeking other options.

-   [Top Serverless GPU Clouds for 2025: Comparing RunPod, Modal, and More](https://www.runpod.io/articles/top-serverless-gpu-clouds): A comprehensive comparison of the leading serverless GPU platforms (RunPod, Modal, Replicate, Novita, Fal, Baseten, Beam, etc.), evaluating pricing, auto-scaling, GPU variety, and performance to help choose the best service for on-demand AI inference in 2025.

-   [Top 12 Cloud GPU Providers for AI and Machine Learning in 2025](https://www.runpod.io/articles/guides/top-cloud-gpu-providers): Side-by-side breakdown of a dozen major cloud GPU providers (including RunPod, AWS, Google, Azure, etc.), comparing their hardware options (A100/H100 and others), billing models (per-second, reserved, etc.), scalability, and developer experience crucial for training LLMs and running ML workloads.

-   [Best Cloud Platforms for L40S GPU Inference Workloads](https://www.runpod.io/articles/guides/best-L40S-gpu-clouds): Reviews the top cloud platforms offering NVIDIA L40S GPUs and analyzes their performance and cost for inference tasks, guiding AI developers seeking cost-effective, high-performance options for deploying inference on L40S GPUs.

**GPU Hardware & Instance Guides**
----------------------------------

-   [Everything You Need to Know About the Nvidia A100 GPU](https://www.runpod.io/articles/guides/nvidia-a100-gpu): In-depth overview of NVIDIA's A100 Tensor Core GPU, covering its architecture, specs, and why the A100 is a cornerstone for modern AI (from massive parallel training of neural networks to high-throughput inference for large models).

-   [Everything You Need to Know About the Nvidia RTX 4090 GPU](https://www.runpod.io/articles/guides/nvidia-4090): Complete guide to NVIDIA's GeForce RTX 4090, detailing its CUDA core count, Tensor Core capabilities, memory, and how these specs translate into AI training and data processing performance in a cloud context.

-   [Everything You Need to Know About Nvidia H100 GPUs](https://www.runpod.io/articles/guides/nvidia-h100): Explains the features of NVIDIA's Hopper H100 GPU (80GB HBM3, FP8 Tensor Cores, NVLink4, etc.) and its leaps in training and inference speed for large-scale AI---demonstrating why H100 GPUs are sought after for advanced LLMs and HPC tasks.

-   [Everything You Need to Know About Nvidia RTX A5000 GPUs](https://www.runpod.io/articles/guides/nvidia-rtx-a5000): Describes the NVIDIA RTX A5000 professional GPU's specs and performance profile, and how it balances memory, reliability, and cost for demanding AI workloads, making it suitable for both training medium-sized models and high-end visualization.

-   [Everything You Need to Know About Nvidia H200 GPUs](https://www.runpod.io/articles/guides/nvidia-h200): Introduction to NVIDIA's next-generation H200 Tensor Core GPU, outlining its improvements over the H100 and potential impact on AI performance and efficiency for upcoming machine learning projects.

-   [Everything You Need to Know About the Nvidia RTX 5090 GPU](https://www.runpod.io/articles/guides/nvidia-rtx-5090): Covers the NVIDIA RTX 5090's expected features and performance (as Nvidia's latest flagship GPU), discussing why its advancements matter for AI researchers and how on-demand access to such GPUs will benefit large-scale model development.

-   [Everything You Need to Know About the Nvidia DGX B200 GPU](https://www.runpod.io/articles/guides/nvidia-dgx-b200): Explores NVIDIA's DGX B200 system/GPU, highlighting its design for enterprise AI (multi-GPU configuration, huge memory, etc.) and what makes it a powerhouse for researchers needing the fastest hardware for training cutting-edge models.

-   [Rent A100 in the Cloud -- Deploy in Seconds on RunPod](https://www.runpod.io/articles/guides/rent-a100-cloud): Announces instant cloud availability of NVIDIA 80GB A100 GPUs on RunPod, emphasizing how users can spin up A100 instances in seconds for AI training or data analytics, with pay-as-you-go pricing and global availability.

-   [Rent H100 SXM in the Cloud -- Deploy in Seconds on RunPod](https://www.runpod.io/articles/guides/rent-h100-sxm-cloud): Highlights on-demand access to NVIDIA H100 SXM GPUs via RunPod, ideal for training giant LLMs or HPC tasks, and explains the hourly pricing and ease of deployment across RunPod's global infrastructure.

-   [Rent H100 PCIe in the Cloud -- Deploy in Seconds on RunPod](https://www.runpod.io/articles/guides/rent-h100-pcie-cloud): Details how to quickly launch cloud instances with NVIDIA H100 PCIe GPUs on RunPod, suited for AI model training and big-data processing, with information on pricing, regions, and deployment speed.

-   [Rent H100 NVL in the Cloud -- Deploy in Seconds on RunPod](https://www.runpod.io/articles/guides/rent-h100-nvl-cloud): Showcases immediate availability of NVIDIA H100 NVL dual-GPU cards on RunPod for large language model workloads and generative AI, noting their combined 188GB VRAM and how researchers can utilize them on-demand.

-   [Rent GB200 NVL72 in the Cloud -- Deploy in Seconds on RunPod](https://www.runpod.io/articles/guides/rent-gb200-nvl72-cloud): Announces RunPod support for the NVIDIA (DGX) B200 NVL72 configuration, enabling cloud access to this cutting-edge multi-GPU system for trillion-parameter model training or heavy analytics, with details on hourly pricing and deployment.

-   [Rent RTX 3090 in the Cloud -- Deploy in Seconds on RunPod](https://www.runpod.io/articles/guides/rent-rtx-3090-cloud): Describes how to spin up cloud instances with NVIDIA RTX 3090 GPUs on RunPod for AI training or high-resolution graphics, emphasizing quick deployment, global availability, and use cases like model training and even gaming.

-   [Rent L40 in the Cloud -- Deploy in Seconds on RunPod](https://www.runpod.io/articles/guides/rent-l40-cloud): Details instant cloud access to NVIDIA L40 GPUs through RunPod, which are ideal for both AI model training and real-time rendering tasks, including pricing and how to get started without setup delays.

-   [Rent RTX 4090 in the Cloud -- Deploy in Seconds on RunPod](https://www.runpod.io/articles/guides/rent-rtx-4090-cloud): Shows how to launch NVIDIA RTX 4090 GPU instances on RunPod's platform, providing top-tier consumer GPU power for AI training and data processing, with notes on hourly cost, global data center availability, and rapid deployment.

-   [Rent RTX A6000 in the Cloud -- Deploy in Seconds on RunPod](https://www.runpod.io/articles/guides/rent-rtx-a6000-cloud): Explains access to NVIDIA RTX A6000 GPUs on RunPod for AI development and 3D rendering, highlighting the ease of deploying this 48GB VRAM professional GPU in the cloud for scalable, on-demand compute.

-   [The Best Way to Access B200 GPUs for AI Research in the Cloud](https://www.runpod.io/articles/guides/b200-cloud-access): Describes how AI researchers can utilize RunPod to tap into powerful NVIDIA B200 GPUs in the cloud, bypassing traditional procurement delays and enabling cutting-edge research on high-end hardware with minimal setup.

**AI & LLM Deployment Tutorials**
---------------------------------

-   [An AI Engineer's Guide to Deploying RVC (Retrieval-Based Voice Conversion) Models in the Cloud](https://www.runpod.io/articles/guides/deploy-rvc-models-cloud): Step-by-step guide to running Retrieval-Based Voice Conversion models on RunPod, demonstrating how to set up the environment and hardware for voice cloning tasks and scale them without local GPU resources.

-   [Automate AI Image Workflows with ComfyUI + Flux on RunPod: Ultimate Creative Stack](https://www.runpod.io/articles/guides/comfyui-flux-ai-image-stack): Shows how to deploy a workflow combining ComfyUI (visual node editor for Stable Diffusion) and Flux on RunPod's cloud platform, enabling automation of image generation pipelines with an intuitive interface and powerful GPUs.

-   [Run Automatic1111 on RunPod: The Easiest Way to Use Stable Diffusion A1111 in the Cloud](https://www.runpod.io/articles/guides/automatic1111-cloud): Tutorial on launching the popular Automatic1111 Stable Diffusion web UI on RunPod with minimal effort, so users can generate images via a browser using cloud GPUs, avoiding complicated local installs.

-   [Make Stunning AI Art with Stable Diffusion Web UI on RunPod (No Setup Needed)](https://www.runpod.io/articles/guides/stable-diffusion-web-ui-10.2.1): Guides intermediate users through deploying Stable Diffusion WebUI (v10.2.1) on RunPod's GPU cloud, highlighting how to get started in minutes to create AI-generated art without any local setup or hardware.

-   [Generate AI Images with Stable Diffusion WebUI 7.4.4 on RunPod: The Fastest Cloud Setup](https://www.runpod.io/articles/guides/stable-diffusion-webui-7.4.4): Demonstrates how to quickly spin up the Stable Diffusion 7.4.4 Web UI on RunPod, enabling creators to produce images in the cloud almost instantly, leveraging RunPod's one-click templates and fast GPUs.

-   [Deploy PyTorch 2.2 with CUDA 12.1 on RunPod for Stable, Scalable AI Workflows](https://www.runpod.io/articles/guides/pytorch-2.2-cuda-12.1): Walks through setting up a cloud environment with PyTorch 2.2 and CUDA 12.1 on RunPod, so developers can utilize the latest framework features and GPU optimizations for training or serving AI models reliably at scale.

-   [Get Started with PyTorch 2.4 and CUDA 12.4 on RunPod: Maximum Speed, Zero Setup](https://www.runpod.io/articles/guides/pytorch-2.4-cuda-12.4): Introduction to launching a container on RunPod pre-loaded with PyTorch 2.4 and CUDA 12.4, allowing AI practitioners to immediately harness speed improvements for model training without dealing with driver or library installs.

-   [Train Any AI Model Fast with PyTorch 2.1 + CUDA 11.8 on RunPod: The Ultimate Guide](https://www.runpod.io/articles/guides/pytorch-2.1-cuda-11.8): Provides a comprehensive tutorial for quickly training models using PyTorch 2.1 on CUDA 11.8 via RunPod, including how to select optimal GPU instances and configuration tips to achieve maximum training throughput.

-   [ComfyUI on RunPod: A Step-by-Step Guide to Running WAN 2.1 for Video Generation](https://www.runpod.io/articles/guides/comfyui-wan-video): Details how to set up and run the WAN 2.1 video generation model using ComfyUI on RunPod, illustrating a workflow for creating AI-generated videos with cloud GPUs and an easy UI, even for beginners.

-   [Train Cutting-Edge AI Models with PyTorch 2.8 + CUDA 12.8 on RunPod](https://www.runpod.io/articles/guides/pytorch-2.8-cuda-12.8): Tutorial covering how to utilize the latest PyTorch 2.8 with CUDA 12.8 on RunPod's platform, enabling researchers to train state-of-the-art models (including transformer networks and LLMs) faster by leveraging new optimizations and powerful GPUs.

-   [How to Deploy FastAPI Applications with GPU Access in the Cloud](https://www.runpod.io/articles/guides/fastapi-gpu-deploy): Explains deploying a FastAPI web application that utilizes GPUs (for example, serving a PyTorch model) on RunPod, using Docker containers to package the app and demonstrate how to attach GPU compute for accelerated API responses.

-   [Running Stable Diffusion on L4 GPUs in the Cloud: A How-To Guide](https://www.runpod.io/articles/guides/stable-diffusion-L4-how-to): Provides a guide for deploying Stable Diffusion on NVIDIA L4 GPUs using RunPod, showing how even lower-cost GPUs can be harnessed for efficient image generation and the steps to get started quickly in the cloud.

-   [Training LLMs on H100 PCIe GPUs in the Cloud: Setup and Optimization](https://www.runpod.io/articles/guides/llm-training-h100-pcie): Offers instructions and tips for training large language models on RunPod's NVIDIA H100 PCIe instances, including environment setup and performance tuning to fully utilize H100's capabilities for faster training times.

-   [How to Deploy a Custom LLM in the Cloud Using Docker](https://www.runpod.io/articles/guides/deploy-custom-llm-docker): Shows how to containerize and deploy a custom large language model on RunPod's cloud, covering from Dockerizing the model code to launching it on a GPU instance and exposing an API endpoint for inference.

-   [How to Use Open-Source AI Tools Without Knowing How to Code](https://www.runpod.io/articles/guides/no-code-open-source-ai): Introduces various open-source AI applications (for art, text, audio, etc.) that can run on RunPod's platform, guiding non-coders to deploy and use these tools via RunPod's templates and UIs without writing custom code.

-   [How to Deploy Hugging Face Models on A100 SXM GPUs in the Cloud](https://www.runpod.io/articles/guides/huggingface-a100-cloud): Step-by-step guide for launching a Hugging Face Transformers model on a RunPod A100 SXM instance, demonstrating how to set up the inference environment and take advantage of high-memory GPUs for fine-tuning or serving NLP models.

-   [How to Serve Phi-2 on a Cloud GPU with vLLM and FastAPI](https://www.runpod.io/articles/guides/serve-phi-2-vllm-fastapi): Explains deploying Microsoft's Phi-2 (2.7B parameter efficient LM) on RunPod using the vLLM library for optimized inference and FastAPI for serving, illustrating a pattern for hosting smaller LMs cheaply and effectively.

-   [How to Deploy LLaMA.cpp on a Cloud GPU Without Hosting Headaches](https://www.runpod.io/articles/guides/deploy-llama-cpp-cloud): Provides a guide to running LLaMA.cpp (a lightweight framework for LLaMA models) on RunPod, removing the usual complexity by using containerized setups so users can experiment with LLaMA on cloud GPUs with minimal fuss.

-   [The Fastest Way to Run Mixtral in a Docker Container with GPU Support](https://www.runpod.io/articles/guides/run-mixtral-gpu-docker): Shows how to quickly get Mixtral (a new large-scale model or pipeline) running in a GPU-enabled Docker container on RunPod, highlighting any specific tweaks or resources needed to maximize performance.

-   [How to Deploy RAG Pipelines with Faiss and LangChain on a Cloud GPU](https://www.runpod.io/articles/guides/deploy-rag-faiss-langchain): Guides users through setting up a Retrieval-Augmented Generation pipeline using Faiss for vector search and LangChain for orchestration, all on RunPod's cloud GPUs, enabling powerful QA or chatbot systems with custom knowledge bases.

-   [How to Run OpenChat on a Cloud GPU Using Docker](https://www.runpod.io/articles/guides/run-openchat-cloud): Instructions for deploying an open-source ChatGPT-like model (OpenChat) on RunPod by using a Docker container, so developers can host their own chatbots on GPUs without relying on third-party API services.

-   [Using Ollama to Serve Quantized Models from a GPU Container](https://www.runpod.io/articles/guides/ollama-quantized-gpu): Demonstrates how to use Ollama (an open-source LLM server) on RunPod to serve quantized large language models, which drastically reduces memory requirements, allowing efficient inference of big models on a single GPU.

-   [How to Run StarCoder2 as a REST API in the Cloud](https://www.runpod.io/articles/guides/starcoder2-rest-api): Tutorial on deploying StarCoder2 (open-source code generation model) on RunPod and exposing it via a REST API, covering how to utilize RunPod's GPUs to handle code-generation requests and integrate with developer applications.

-   [How to Serve Gemma Models on L40S GPUs with Docker](https://www.runpod.io/articles/guides/gemma-l40s-docker): Explains running Google's Gemma language models on NVIDIA L40S GPUs using Docker on RunPod, including any optimizations or configurations needed to effectively serve these models for inference.

-   [Can You Run Google's Gemma 2B on an RTX A4000? Here's How](https://www.runpod.io/articles/guides/gemma2b-rtx-a4000): Answers the question by walking through an example of deploying Google's 2-billion-parameter Gemma model on a single RTX A4000 16GB GPU, demonstrating it's feasible with the right optimization (and showing how to do it on RunPod).

-   [Deploying GPT4All in the Cloud Using Docker and a Minimal API](https://www.runpod.io/articles/guides/deploy-gpt4all-cloud): Shows how to set up GPT4All (an open-source chat model) on RunPod inside a Docker container with a lightweight API, enabling anyone to host a private ChatGPT-style service on inexpensive cloud GPUs.