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ShipItAndPray/mcp-turboquant

MCP server for LLM quantization. Compress any model to GGUF/GPTQ/AWQ in one tool call. First MCP server for model compression.

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Config template (adjust for your client)

claude mcp add mcp-turboquant -- npx -y mcp-turboquant

A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.

Star count: 5 ★ · ShipItAndPray/mcp-turboquant · read from the GitHub search API · (checked 2026-10-16 20:00:20)
Source: collected from the punkpeye/awesome-mcp-servers list · type: MCP server
Harvest history: first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
Score: score 703 · 5 ★ · 7 sources · mcp

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repository ShipItAndPray/mcp-turboquant；first catalogued 2026-10-09；last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.

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mcp-turboquant

以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →

Self-contained Python MCP server for LLM quantization. Compress any HuggingFace model to **GGUF**, **GPTQ**, or **AWQ** format in a single tool call.
No external CLI required -- all quantization logic is embedded.
Install
Or run directly with uvx:
Optional backends
The info, check, and recommend tools work out of the box. For actual quantization, install the backend you need:
Configure
Claude Code
Add to ~/.claude/settings.json:
Or with uvx (no install needed):
Claude Desktop
Add to claude_desktop_config.json:
Tools
| Tool | Description | Heavy deps? |
|------|-------------|-------------|
| info | Get model info from HuggingFace (params, size, architecture) | No |
| check | Check available quantization backends on the system | No |
| recommend | Hardware-aware recommendation for best format + bits | No |
| quantize | Quantize a model to GGUF/GPTQ/AWQ | Yes |
| evaluate | Run perplexity evaluation on a quantized model | Yes |
| push | Push quantized model to HuggingFace Hub | No |
Examples
Once configured, ask Claude:
> "Get info on meta-llama/Llama-3.1-8B-Instruct"
> "What quantization format should I use for Mistral-7B on my machine?"
> "Quantize meta-llama/Llama-3.1-8B to 4-bit GGUF"
> "Check which quantization backends I have installed"
> "Evaluate the perplexity of my quantized model at /path/to/model.gguf"
> "Push my quantized model to myuser/model-GGUF on HuggingFace"
How it works
All quantization logic runs in-process. No external CLI tools needed.
Run directly
License
MIT