EcoCompute Should-I-Quantize Estimator
Energy impact of LLM quantization, calibrated to measured GPU data. Every answer states its basis.
| Endpoint | https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp (streamable-http) |
|---|---|
| Registry version | 1.0.0 |
| Now | 4 tools · grade A (100) · pin ts_6fa6c46d68beb099d72e15babbc48035 · protocol 2025-06-18 |
| First / last listed | 2026-10-28 07:59 UTC / 2026-10-28 07:59 UTC |
| Grade over time | 2026-10-28: A (100), 4 tools |
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Tools: estimate_quantization_energy get_quantization_crossover list_data_coverage search_measurements
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 measured-anchored curves. The answer carries basis (measured/interpolated/estimated), confidence and evidence boundaries. params: params_b, arch, precision, batch, ctx, q |
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, not an architecture — the answer says so. params: arch |
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 trusting an estimate at the edges of coverage, and to answer 'is there data for my GPU?' honestly. params: none |
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. params: query* |
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