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ShipItAndPray/mcp-turboquant

by Various

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

S

MCP

ShipItAndPray/mcp-turboquant

Added 1 June 2026

#gguf #llm #mcp #mcp-server #quantization #turboquant

Overview

ShipItAndPray/mcp-turboquant is an MCP server that enables LLM quantization to GGUF, GPTQ, or AWQ formats through a single tool call. It is the first MCP server dedicated to model compression, allowing developers to reduce model size directly from their workflows.

Best for

Best for
Developers experimenting with LLM compression in MCP-driven pipelines

Use cases

  • Quantize a large language model to GGUF for CPU inference
  • Compress a model to GPTQ for GPU memory savings
  • Apply AWQ quantization for latency-sensitive applications

How to use

Install

uvx mcp-turboquant

Tools exposed

  • info
  • check
  • recommend
  • quantize
  • evaluate
  • push

Tested with

Claude Code, Claude Desktop

Example client config

{\n  "mcpServers": {\n    "turboquant": {\n      "command": "uvx",\n      "args": ["mcp-turboquant"]\n    }\n  }\n}

Notes

ShipItAndPray/mcp-turboquant is an MCP server that enables LLM quantization to GGUF, GPTQ, or AWQ formats through a single tool call. It is the first MCP server dedicated to model compression, allowing developers to reduce model size directly from their workflows.

3 stars on GitHub. Last updated 2026-04-02. Licensed MIT.

Use cases

  • Quantize a large language model to GGUF for CPU inference
  • Compress a model to GPTQ for GPU memory savings
  • Apply AWQ quantization for latency-sensitive applications

Pros

  • Supports multiple quantization formats in one server
  • Integrates directly with MCP-based toolchains
  • Reduces manual scripting for model compression

Cons

  • Very early stage with only 3 GitHub stars and limited community
  • Requires MCP-compatible agent or platform to use
  • May lack robustness or error handling for production use

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Supports multiple quantization formats in one server
  • Integrates directly with MCP-based toolchains
  • Reduces manual scripting for model compression

Cons

  • Very early stage with only 3 GitHub stars and limited community
  • Requires MCP-compatible agent or platform to use
  • May lack robustness or error handling for production use

Pairs with

Other entries in the index that connect to this one. Click through to see the chain.

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