io.github.hongping-zh/ecocompute-estimator

up checked 2026-11-11T04:38:52.678Z

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Publisher-provided title

EcoCompute Should-I-Quantize Estimator

Publisher-provided description

Energy impact of LLM quantization, calibrated to measured GPU data. Every answer states its basis.

Summary by lookupd

Estimates energy change from weight-only LLM quantization (NF4/INT8) versus FP16, using measured GPU data with stated basis.

Install

Claude Code, remote (streamable-http)

claude mcp add --transport http ecocompute-estimator https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp

Codex, remote (streamable-http)

codex mcp add ecocompute-estimator --url https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp

Cursor, remote (streamable-http) → .cursor/mcp.json

{
  "mcpServers": {
    "ecocompute-estimator": {
      "url": "https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp"
    }
  }
}

Other clients: docs. Or tell your agent: Set up lookupd for me: read https://lookupd.com/agent.md and follow it.

Tools (4 tools)

Publisher-provided text

• estimate_quantization_energy Estimate the task-level energy change (ΔE% vs FP16) of weight-only quantization (NF4 or INT8) for a model of a given size on a GPU architecture class, from meas
• get_quantization_crossover Get the model-size crossover for NF4/INT8 per architecture: below it quantization costs energy, above it saves. Crossovers belong to a card + software stack, no
• list_data_coverage List what is actually measured (GPU architecture × precision × size range, NVML direct), what is borrowed/estimated, and where the gaps are. Use this before tru
• search_measurements Search the measured anchors by model name, GPU name, architecture or size. Returns ΔE% vs FP16 per anchor with run counts — the raw evidence behind the curves.

Details

• Version: 1.0.0
• Transports: streamable-http
• Uptime (7 days): 100%
• Median latency: 472 ms
• First indexed: 2026-11-01T09:33:49.950Z
• Last seen in registry: 2026-11-01T09:33:49.950Z
• Repository: https://github.com/hongping-zh/quant-energy
• Website: https://quantenergy.tech

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