MCP server

EcoCompute Should-I-Quantize Estimator

io.github.hongping-zh/ecocompute-estimator

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Working at last checkAbout 616 tokens per request once connected4 tools

Website Source

https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp

At a glance

Type: MCP server
Publisher: Not verified
Status: Working at last check
Tools: 4
Token cost: About 616 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to Cursor Add to VS Code VS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.ecocompute-estimator]
url = "https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp"

Gemini CLI

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

Cursor

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

VS Code

{"servers":{"ecocompute-estimator":{"type":"http","url":"https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp"}}}

Antigravity

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

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"ecocompute-estimator","server_url":"https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"ecocompute-estimator","server_url":"https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp"}

Tools

estimate_quantization_energy ~280 tokens
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.
get_quantization_crossover ~124 tokens
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.
list_data_coverage ~100 tokens
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.
search_measurements ~112 tokens
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.

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