Your AI, Your Rules: Running a Local LLM with GPU Acceleration on Proxmox

The guide you've provided outlines an intricate setup for running Claude Code, an interactive terminal application designed for conversing with large language models (LLMs), on a machine that hosts llama.cpp, a C++ implementation of LLMs like Anthropic's Claude model. The process involves several steps and considerations to ensure seamless communication between the client-side application and the server-side LLM.

Key Points from the Guide

1. System Setup:

   • A Linux system with Docker is used as a starting point.
   • llama.cpp is compiled for CPU-only execution initially due to memory constraints on GPU.

2. Model Configuration:

   • The guide uses a 15.9 GB model, which exceeds the VRAM capacity of an RTX 2000 Ada Lovelace GPU with 8GB VRAM. Thus, a hybrid approach is necessary where some layers are executed on CPU while others use GPU.

3. Hybrid Execution:

   • The optimal number of transformer layers to be processed by the GPU is determined experimentally (12 out of 30 in this case), leaving enough memory for compute buffers.

4. Proxy Service:

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