QuantizeLab MCP Server

Quantize and publish Hugging Face models as GGUF — from any MCP-capable AI assistant.

This directory describes the hosted QuantizeLab MCP server. The server itself is operated by QuantizeLab at https://quantizelab.dev/api/public/mcp. Anyone with a QuantizeLab account can connect to it for free from their own machine — Claude, Cursor, or any MCP client. There is no self-hosted edition: the service runs on our side only.

• Live endpoint: https://quantizelab.dev/api/public/mcp
• Models: up to 33B parameters, GGUF, four quantization levels (Q4_K_M default, Q5_K_M, Q6_K, Q8_0)
• Pricing: previewed in credits before anything runs; failed jobs refund automatically
• Confirmation is mandatory — the AI can suggest, you decide

Quick start

1. Sign up at quantizelab.dev — new accounts get 10 free credits.
2. Open API & MCP from the sidebar and create an API key (ql_...). It is shown once, so keep it safe.
3. Add the server to your client (configs below).
4. Ask your assistant: "Quantize cognitivecomputations/dolphin-2.6-phi-2 to Q4_K_M and publish it to my profile."

Client setup

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "quantizelab": {
      "url": "https://quantizelab.dev/api/public/mcp",
      "headers": { "Authorization": "Bearer ql_YOUR_API_KEY" }
    }
  }
}

Claude Code

claude mcp add quantizelab \
  --transport http \
  --url https://quantizelab.dev/api/public/mcp \
  --header "Authorization: Bearer ql_YOUR_API_KEY"

Cursor

Settings -> MCP -> Add new MCP server:

Field	Value
Type	URL
Name	quantizelab
URL	https://quantizelab.dev/api/public/mcp
Header	Authorization: Bearer ql_YOUR_API_KEY

Any other MCP client

Point it at https://quantizelab.dev/api/public/mcp (Streamable HTTP / JSON-RPC 2.0) with the Authorization: Bearer ql_... header. A plain GET returns server metadata, so most clients discover the tools automatically.

Direct from the terminal (no AI)

MCP is plain JSON-RPC over HTTP — drive it with curl or any script. Initialize once per session, then call tools:

curl -X POST https://quantizelab.dev/api/public/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "Authorization: Bearer ql_YOUR_API_KEY" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"terminal","version":"1.0"}}}'

# then call a tool, e.g. balance
curl -X POST https://quantizelab.dev/api/public/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "Authorization: Bearer ql_YOUR_API_KEY" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"get_credits","arguments":{}}}'

If a call returns "not initialized", send {"jsonrpc":"2.0","method":"notifications/initialized"} first.

Tools

Tool	Description	Key args
get_credits	Current credit balance	—
get_pricing	Tier table, quantization levels, credit packs	—
browse_model	Price an HF repo URL without starting anything	hf_model_url, quant_type
quantize_model	Quantize + publish — requires confirm: true	hf_model_url, target_format, quant_type, confirm
get_job_status	Poll a submitted job	job_id

Pricing (1 credit = $0.10)

Tier	Model size	Credits	USD
1B	<=1.1B	5	$0.50
3B	<=3B	8	$0.80
8B	<=8B	12	$1.20
15B	<=15B	15	$1.50
33B	<=33B	22	$2.20

• Tier prices are for the default Q4_K_M level. Q5_K_M and Q6_K cost the same; Q8_0 (highest fidelity, ~70% larger output) adds 5 credits.
• Models larger than 33B are rejected before anything is charged.
• Top-up packs: Starter $5 = 50 cr / Standard $10 = 100 cr / Pro $25 = 250 cr / Studio $50 = 500 cr.
• Credits are deducted only when a job actually starts and are refunded automatically if dispatch fails.

Safety (enforced server-side)

• quantize_model returns a cost preview first; only a second call with confirm: true queues the job.
• Every run goes through the same pipeline as the website: rate limit, HF token check, model preflight, abuse guard, GPU budget, atomic credit deduction, automatic refund on failure. An API key cannot bypass any of it.
• Your Hugging Face token is stored encrypted server-side and never touches the client.
• Keys are stored as one-way hashes — revoke any key from the API & MCP page and it stops working immediately.

Hosted and operated by QuantizeLab

The MCP server is part of the QuantizeLab service and is not released for self-hosting. Connect to the hosted endpoint — it is free for personal use.

License

MIT