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How to size the hardware you need to run a private LLM with Bindwidth

Enter your model and workload and see which limit binds, memory, serving load or session ceiling.

Who it is forAn IT lead asked how many GPUs the company needs to run a chat model in house.

The problemTeams usually size for the wrong limit and overbuy or underbuy.

The outcomeA sized configuration showing the binding constraint and its cost.

What you need first

  • A web browser (or Python to serve it locally)
  • Node.js only for running the tests

Steps

  1. Open the hosted calculator, or run it locally with python3 -m http.server 8080 after cloning
  2. Pick a guided setup preset or enter your model and workload
  3. Read which of the three limits binds
  4. Compare the eligible hardware alternatives

Prompt for your agent

Use juxhinr/bindwidth to size hardware for <model> serving <users> concurrent sessions. Report the binding constraint and the cheapest eligible option, and confirm the numbers by re-running with the preset changed.

Caveat. It sizes one resident text model; multi-model or multimodal setups are outside its scope.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for juxhinr/bindwidth and "Size the hardware you need to run a private LLM"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

Cursor, ChatGPT, others

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