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Jamesob's guide to running SOTA LLMs locally

GitHub - jamesob/local-llm: Everything I know about running LLMs locally

github.com

July 3, 2026

9 min read

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65/100

Summary

The GitHub repository provides information on running state-of-the-art large language models (LLMs) locally, including hardware recommendations and configuration tips. It also covers local speech-to-text (STT) implementation and offers insights into the author's personal setup.

Key Takeaways

A local setup for running state-of-the-art machine intelligence can be achieved for around $2,000 with models like Qwen and good speech-to-text capabilities, while a more advanced setup costs approximately $40,000 for near-Opus performance.
The recommended hardware configuration includes a last-gen EPYC system, 4 RTX PRO 6000 GPUs providing a total of 384GB VRAM, and specific PCIe switching to enhance GPU communication.
The configuration allows for running models such as GLM-5.2-594B and whisper-large-v3 for speech-to-text, with ready-to-run setups available in the repository.
Local speech-to-text processing is highlighted as a useful alternative to hosted solutions, with a configuration requiring around 11GB of VRAM on an Nvidia GPU.

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Community Sentiment

Mixed

Positives

Running local LLMs like Qwen 3.6-27B offers significant advantages, including enhanced privacy and control over sensitive information.
The performance of local setups can be impressive, with users reporting effective operation on a single 24GB GPU, showcasing the accessibility of powerful models.
The ability to run models locally allows for flexibility and convenience, as users can utilize their laptops without the heat issues associated with high-performance GPUs.

Concerns

The high costs associated with building local setups can be prohibitive, with some estimates reaching up to $55K for a capable configuration.
Users caution that local models often require quantization techniques, which may compromise performance and lead to unexpected limitations in capabilities.
The performance of large models like GLM 5.2 can be severely limited by hardware constraints, with some configurations needing upwards of $500K for effective use.