GPU & AI Guides | Tutorials for Workflows on Runpod

March 24, 2026

Serverless GPU: what it is, when to use it, and how to choose a provider

Serverless GPU lets you run AI inference workloads on demand, scaling to zero when idle and spinning up in milliseconds. Learn what it is, when it's the right architecture, how it compares to persistent GPU instances, and what to look for when choosing a provider.

Guides

March 17, 2026

Deploy vLLM with Docker on Runpod: Container Config, Model Loading, and Production Tuning

Learn how to deploy vLLM with Docker on Runpod end-to-end: from GPU selection and pod configuration to Network Volume caching, server flag tuning, and a production-ready OpenAI-compatible inference endpoint using Llama 3.1 8B on an L40S.

Guides

March 10, 2026

The LLM inference optimization playbook: architecting for latency, throughput, and cost

Benchmarks and configuration patterns for optimizing LLM inference cost, latency, and throughput using vLLM, quantization, and autoscaling infrastructure.

Guides

February 17, 2026

Best GPU for AI training (2026 guide)

Choosing the best GPU for AI training depends on model size, memory requirements, and budget. In this guide, we compare top training GPUs including the NVIDIA B200 (180GB), H200 SXM (141GB), H100 (SXM and PCIe), AMD MI300X (192GB), and RTX 5090 (32GB). Whether you’re training large language models, fine-tuning open-source LLMs, or running diffusion workloads, we break down which GPU is best for 7B, 13B, 70B, and larger models, plus when to scale to multi-GPU clusters.

Guides

August 28, 2025

LLM Fine-Tuning on a Budget: Top FAQs on Adapters, LoRA, and Other Parameter-Efficient Methods

Parameter-efficient fine-tuning (PEFT) adapts LLMs by training tiny modules—adapters, LoRA, prefix tuning, IA³—instead of all weights, slashing VRAM use and costs by 50–70% while keeping near full-tune accuracy. Fine-tune and deploy budget-friendly LLMs on Runpod using smaller GPUs without sacrificing speed.

Guides

August 20, 2025

The Complete Guide to NVIDIA RTX A6000 GPUs: Powering AI, ML, and Beyond

Discover how the NVIDIA RTX A6000 GPU delivers enterprise-grade performance for AI, machine learning, and rendering—with 48GB of VRAM and Tensor Core acceleration—now available on-demand through Runpod’s scalable cloud infrastructure.

Guides

July 31, 2025

AI Model Compression: Reducing Model Size While Maintaining Performance for Efficient Deployment

Reduce AI model size by 90%+ without sacrificing accuracy using advanced compression techniques on Runpod—combine quantization, pruning, and distillation on scalable GPU infrastructure to enable lightning-fast, cost-efficient deployment across edge, mobile, and cloud environments.

Guides

July 31, 2025

Overcoming Multimodal Challenges: Fine-Tuning Florence-2 for Advanced Vision-Language Tasks

Fine-tune Microsoft’s Florence-2 on Runpod’s A100 GPUs to solve complex vision-language tasks—streamline multimodal workflows with Dockerized PyTorch environments, per-second billing, and scalable infrastructure for image captioning, VQA, and visual grounding.

Guides

July 31, 2025

Synthetic Data Generation: Creating High-Quality Training Datasets for AI Model Development

Generate unlimited, privacy-compliant synthetic datasets on Runpod—train AI models faster and cheaper using GANs, VAEs, and simulation tools, with scalable GPU infrastructure that eliminates data scarcity, accelerates development, and meets regulatory standards.

Guides

July 31, 2025

MLOps Pipeline Automation: Streamlining Machine Learning Operations from Development to Production

Accelerate machine learning deployment with automated MLOps pipelines on Runpod—streamline data validation, model training, testing, and scalable deployment with enterprise-grade orchestration, reproducibility, and cost-efficient GPU infrastructure.

Guides

July 31, 2025

Computer Vision Pipeline Optimization: Accelerating Image Processing Workflows with GPU Computing

Accelerate your computer vision workflows on Runpod with GPU-optimized pipelines—achieve real-time image and video processing using dynamic batching, TensorRT integration, and scalable containerized infrastructure for applications from autonomous systems to medical imaging.

Guides

July 31, 2025

Reinforcement Learning in Production: Building Adaptive AI Systems That Learn from Experience

Deploy adaptive reinforcement learning systems on Runpod to create intelligent applications that learn from real-world interaction—leverage scalable GPU infrastructure, safe exploration strategies, and continuous monitoring to build RL models that evolve with your business needs.

Guides

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