io.github.hongping-zh由 GitHub 账号 hongping-zh 发布v1.0.0更新于 Oct 24, 2026
Energy impact of LLM quantization, calibrated to measured GPU data. Every answer states its basis.
远程 MCP 服务器，让助手基于实测 GPU 数据估算 LLM 权重量化（NF4/INT8）相对 FP16 的任务级能耗变化。
Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。
把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。
阅读 https://sourceweft.com/mcp/io.github.hongping-zh%2Fecocompute-estimator/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。把它添加到你客户端的 mcpServers 配置中。
{
"mcpServers": {
"ecocompute-should-i-quantize-estimator": {
"type": "http",
"url": "https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp"
}
}
}该仓库没有 README。
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.
qctxarchbatchparams_bprecisionGet 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.
archList 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.
无需输入参数。
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.
query必填已满足适用检查项的 75%
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请声明包含 README 的 GitHub 仓库，或在 OAuth 受保护资源元数据中提供 resource_documentation。
请在仓库或注册中心条目中声明许可证。