jamesob's guide to running SOTA LLMs is a practical guide and configuration repository for running high-end language models on local hardware. It documents one developer’s local LLM setup, including hardware choices, GPU layout, storage, PCIe switches, kernel settings, and serving workflows. The repository compares budget levels ranging from dual RTX 3090 systems to high-end multi-GPU workstations with very large VRAM pools. It includes ready-to-run serving configurations for selected models and local speech-to-text workloads. The guide focuses on real deployment details such as Docker containers, model weight storage, GPU peer-to-peer behavior, and agentic workload performance. It is useful for builders who want concrete local AI infrastructure notes instead of abstract hardware recommendations.
Features
- Local LLM hardware guide
- Budget and high-end GPU planning
- Docker-based model serving configs
- Speech-to-text runner configuration
- GPU peer-to-peer benchmark tooling
- Kernel, BIOS, and PCIe tuning notes