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io.github.TheBaronofAI/vetted-consumer

io.github.TheBaronofAI/vetted-consumer · v1.0.0

Measured by MCP Station

Description written by the publisher, not verified

Will a local LLM run on your hardware? GGUF quant, buy-vs-rent-vs-API cost, used-GPU prices.

Connect

claude mcp add --transport http vetted-consumer https://vettedconsumer.com/mcp

{
  "mcpServers": {
    "vetted-consumer": {
      "url": "https://vettedconsumer.com/mcp"
    }
  }
}

Tools as reported by the server

• can_i_run_it
  Will a given local LLM run on given hardware? Returns fit, the best quant that fits, theoretical tok/s, and real owner-measured tok/s where available.
• recommend_quant
  Which GGUF quantization to download for a model on given hardware: the full quant ladder with file size, max context, and tok/s for each, plus the recommended pick.
• cheapest_hardware_for_model
  The cheapest catalogued, buyable machine that runs a given model at Q4 with the requested context.
• list_models
  List the local LLM model classes the tools know about (params, dense/MoE, native context).
• list_hardware
  List the machines the tools know about (memory, bandwidth, price, buy link).
• cost_compare
  Buy vs rent vs API cost to run a model locally: monthly/1y/3y totals, break-even months, and the energy cost per 1M tokens. Same math as /cost-calculator/.
• recommend_hardware
  Ranked list of catalogued, buyable machines that run a model at the requested context, cheapest first, with an optional budget cap.
• get_used_gpu_prices
  Current typical used-GPU prices for local-AI rigs (eBay Browse API median asking + hand-verified, monthly).
• compare_hardware
  Side-by-side memory, bandwidth, price, and (with a model) fit + tok/s for 2 to 4 machines.

Website · JSON · Text for agents · Registry updated 2026-06-22