Runpod Blog.

Runpod has reduced prices by up to 40% across Serverless and Secure Cloud GPUs—making high-performance AI compute more accessible for developers, startups, and enterprise teams.

RAG and fine-tuning are two powerful strategies for adapting large language models (LLMs) to domain-specific tasks. This post compares their use cases, performance, and introduces RAFT—an integrated approach that combines the best of both methods for more accurate and adaptable AI models.

Retrieval-Augmented Generation (RAG) and fine-tuning are powerful ways to adapt large language models. Learn the key differences, trade-offs, and when to use each.

Explore how to deploy and benchmark LLMs locally using tools like Ollama and NVIDIA NIMs. This deep dive covers performance, cost, and scaling insights across GPUs including RTX 4090 and H100 NVL.

Curious how local LLM deployment stacks up? This post explores benchmarking strategies, optimization tips, and what DevOps teams need to know about performance tuning.

Runpod benchmarks AMD’s MI300X against Nvidia’s H100 SXM using Mistral’s Mixtral 8x7B model. The results highlight performance and cost trade-offs across batch sizes, showing where AMD’s larger VRAM shines.

We benchmarked AMD’s MI300X against NVIDIA’s H100 on Mixtral 8x7B. Discover which GPU delivers faster inference and better performance-per-dollar.