Vetted Consumer

io.github.TheBaronofAIPublished by GitHub account TheBaronofAIv1.0.0Updated Oct 24, 2026

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

VerifiedStreamable HTTPWeb executableFinanceAI & ML

Overview

AI-generated overview

A remote MCP endpoint that answers questions about running local LLMs: GGUF quantization, hardware fit, and buy-vs-rent-vs-API cost.

What you can do
Vetted Consumer is a remote MCP server reached over streamable HTTP at its provider's endpoint. According to its registry description, it addresses whether a local LLM will run on given hardware, covering GGUF quantization choices and cost comparisons between buying hardware, renting compute, and paying for API access, including used-GPU prices. No individual tools are listed in the manifest, so the exact tool names and inputs are not documented here.
When to use it
Worth adding when you want an assistant to help size a local model to your machine, pick a GGUF quantization, or weigh buying a GPU against renting or using an API. Less useful if you only need general model or hardware information that does not involve local inference planning.
Requirements
A remote streamable HTTP endpoint; no local runtime, package, environment variable, or header is declared. The manifest declares no authentication, so no account or API key is shown as required.
Before you install
The manifest declares no authentication, environment variables, or headers, so no credentials are requested by name. Because it is a remote third-party service, the questions you send are handled outside your machine; avoid including private hardware inventory, budget, or purchasing details you would not share with that provider.

Installation

In SourceWeft

  1. Open Vetted Consumer in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.TheBaronofAI%2Fvetted-consumer/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

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

Tools

9

can_i_run_it

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.

Parametersmodelmxfp4contexttotal_bunifiedvram_gbactive_bhardwarekv_precisionbandwidth_gbps

recommend_quant

Recommend a 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.

Parametersmodelmxfp4contexttotal_bunifiedvram_gbactive_bhardwarekv_precisionbandwidth_gbps

cheapest_hardware_for_model

Cheapest hardware for a model

The cheapest catalogued, buyable machine that runs a given model at Q4 with the requested context.

Parametersmodelmxfp4contexttotal_bactive_b

list_models

List models

List the local LLM model classes the tools know about (params, dense/MoE, native context).

Takes no parameters.

list_hardware

List hardware

List the machines the tools know about (memory, bandwidth, price, buy link).

Takes no parameters.

cost_compare

Cost: buy vs rent vs API

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/.

Parametersapikwhrenthourstdp_wtokenshardwareprice_usd

recommend_hardware

Recommend hardware

Ranked list of catalogued, buyable machines that run a model at the requested context, cheapest first, with an optional budget cap.

Parametersmodelmxfp4budgetcontexttotal_bactive_bkv_precision

get_used_gpu_prices

Used GPU prices

Current typical used-GPU prices for local-AI rigs (eBay Browse API median asking + hand-verified, monthly).

Parametersgpu

compare_hardware

Compare hardware

Side-by-side memory, bandwidth, price, and (with a model) fit + tok/s for 2 to 4 machines.

Parametersmodelmxfp4contexttotal_bactive_bhardwarerequiredkv_precision

Version history

1
  1. v1.0.0LatestSep 16, 2026

Score

83% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 daysRequired25 pts
  • Auth declaredRequired10 pts
  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts

    Declare a GitHub repository with a README, or serve resource_documentation in the OAuth protected-resource metadata.

  • LicenseNot applicable

    A hosted service is used under its provider's terms, so no license is expected here. This check is left out of the percentage.