EcoCompute Should-I-Quantize Estimator
by FastDrop
Energy impact of LLM quantization, calibrated to measured GPU data. Every answer states its basis.
Energy impact of LLM quantization, calibrated to measured GPU data. Every answer states its basis. Official MCP Registry: io.github.hongping-zh/ecocompute-estimator@1.0.0. Remote endpoint: https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp
Problem it solves
Discoverable MCP server listed in the official MCP Registry.
How it's different
Remote MCP endpoint ingested from the official registry and probed by FastDrop.
Ingested from the official MCP Registry
FastDrop pulled this entry from the official MCP Registry as io.github.hongping-zh/ecocompute-estimator and probed the endpoint. The maker did not submit it, so the outbound link is not endorsed
and the description is not theirs.
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Read directly from the endpoint on 24 Oct 2026. Not supplied by the maker.
estimate_quantization_energyEstimate 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 measured-anchored curves. The answer carries
get_quantization_crossoverGet the model-size crossover for NF4/INT8 per architecture: below it quantization costs energy, above it saves. Crossovers belong to a card + software stack, not an architecture — the answer says so.
list_data_coverageList what is actually measured (GPU architecture × precision × size range, NVML direct), what is borrowed/estimated, and where the gaps are. Use this before trusting an estimate at the edges of covera
search_measurementsSearch 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.
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