Skip to main contentBlog
Our team’s insights on building better and scaling smarter.
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.
Runpod and Defined.ai launch a pilot program to provide startups with access to high-quality training data and compute, enabling sector-specific fine-tuning and closing the data wealth gap.
Runpod Serverless now supports multi-GPU workers, enabling full-precision deployment of large models like Llama-3 70B. With optimized VLLM support, flashboot, and network volumes, it's never been easier to run massive LLMs at scale.
