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
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
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
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
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
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
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
