FitLLM
@click6067-ship-it
FitLLM について
Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Remote server: https://fitllm.run/api/mcp
基本情報
設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"fitllm-engine": {
"command": "npx",
"args": [
"fitllm",
"GLM-4.7-Flash",
"--gpu",
"4090",
"#",
"✓",
"FITS",
"—",
"21.9/24",
"GB,",
"free",
"2.1",
"GB"
]
}
}
}ツール
ツールは検出されませんでした
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
What is Fitllm Engine?
Will this LLM fit your GPU or Mac? npx fitllm — accurate memory math for MLA/sliding-window/hybrid/MoE architectures that naive VRAM calculators get wrong by up to 18x. Single file, zero deps, conformance-vector tested. MIT.
How to use Fitllm Engine?
The README includes setup instructions such as npx fitllm "GLM-4.7-Flash" --gpu 4090 # ✓ FITS — 21.9/24 GB, free 2.1 GB.
Key features of Fitllm Engine
- Architecture values checked against official HuggingFace config.json
- Calibration: Qwen 3.6 35B-A3B @128K, 8-bit ≈ 54 GB (matches real local runs)
- MLA per-token cost: GLM-4.7-Flash = (512 + 64) × 2 B × 47 layers = 54,144 B/token — pinned by conformance vectors
- Claude Code: claude mcp add --transport http fitllm https://fitllm.run/api/mcp
- Cursor / Windsurf: add to mcp.json → { "mcpServers": { "fitllm": { "url": "https://fitllm.run/api/mcp" } } }
Use cases of Fitllm Engine
- Connect an MCP-compatible client to this repository's service.
- Review the README-backed setup before enabling it in production.
FAQ from Fitllm Engine
Where is the source code for Fitllm Engine?
The source code is linked from the repository URL on this page.
Does Fitllm Engine include a standard MCP config?
If the README contains a parseable MCP configuration block, it is shown in the Config tab.
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