Jamesob's Guide to Running SOTA LLMs Locally
Original: Jamesob's guide to running SOTA LLMs locally
Why This Matters
Provides a rare, detailed blueprint for self-hosted LLM inference, advancing the local AI movement.
Developer jamesob published a GitHub guide detailing how to run state-of-the-art LLMs locally, covering hardware setups ranging from $2,000 to $40,000, including GPU configurations, speech-to-text (STT), and Docker-based model runners.
GitHub user jamesob has released a comprehensive guide titled 'Everything I know about running LLMs locally,' offering practical, hands-on advice for running state-of-the-art large language models on personal hardware. The guide covers a wide budget range: a $2,000 setup can run Qwen models with capable STT, while a $40,000 build approaches Claude Opus-level performance. The author's own build centers on a last-generation EPYC CPU system costing approximately $5,600, paired with four NVIDIA RTX PRO 6000 GPUs totaling 384GB of VRAM. The guide also covers PCIe switching hardware from c-payne.com, Docker container configurations for preferred models, and local speech-to-text setup. The author explicitly notes that no content in the README (aside from tables) was written by AI, emphasizing a human-curated, factual resource. The project has received 310 stars and 8 forks on GitHub since publication.