FitLLM
Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only.
我該用這個嗎
品質與安全性
根據工具定義與協定合規性的自動化分析。
上下文成本
這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高，可用於其他工作的注意力就越少。
安裝
一鍵安裝
將以下內容加入你的 `claude_desktop_config.json` 檔案：
{
"mcpServers": {
"fitllm": {
"url": "https://fitllm.run/api/mcp"
}
}
}遠端端點
https://fitllm.run/api/mcpstreamable-http它能做什麼
工具清單
工具（3）
🟢check_llm_fit(model, gpu, gpu_count, mac_ram_gb, quant, ...)
Check whether a specific local LLM fits in the memory of a specific GPU or Apple Silicon Mac. Returns fits/tight/won't-fit verdict with the memory breakdown (weights, KV cache, linear-attention state when present, runtime overhead, reserve), max context, and a concrete fix if it doesn't fit. Use this whenever a user asks anything like "can I run <model> on my <GPU/Mac>?", "will <model> fit in <N>GB?", or "what do I need to run <model>?". Estimates using curated, config-derived architecture fields (MLA, sliding-window, hybrid attention, MoE modeled).
輸入結構描述
{
"type": "object",
"properties": {
"model": {
"type": "string",
"description": "LLM name, fuzzy — e.g. \"GLM-4.7-Flash\", \"gpt-oss-20b\", \"gemma 31b\""
},
"gpu": {
"type": "string",
"description": "GPU name, fuzzy — e.g. \"RTX 4090\", \"RX 7900 XTX\", \"A100 80GB\". Multi-GPU rigs: join with + — e.g. \"RTX 5090 + RTX 3090\" (VRAM pools across cards). Provide gpu OR mac_ram_gb."
},
"gpu_count": {
"type": "integer",
"minimum": 1,
"maximum": 8,
"description": "Number of identical copies of the gpu (e.g. gpu=\"RTX 3090\", gpu_count=2 for a 2×3090 rig). Default 1."
},
"mac_ram_gb": {
"type": "integer",
"minimum": 8,
"maximum": 2048,
"description": "Apple Silicon unified memory in GB — e.g. 16, 64, 512. Provide gpu OR mac_ram_gb."
},
"quant": {
"type": "string",
"description": "Weight quantization. GPU: Q4_K_M(default)/Q5_K_M/Q6_K/Q8_0/FP16. Mac: 4/8(default)/16 (bits)."
},
"context_tokens": {
"type": "integer",
"minimum": 1024,
"description": "Context length in tokens (default 8192). Alias: ctx (same field as the REST API)."
},
"ctx": {
"type": "integer",
"minimum": 1024,
"description": "Alias of context_tokens — accepted because the REST API uses this name. Do not pass both with different values."
},
"kv_bits": {
"type": "number",
"enum": [
16,
8,
4
],
"description": "KV-cache quantization bits (default 16 = F16)"
}
},
"required": [
"model"
],
"additionalProperties": false,
"$schema": "http://json-schema.org/draft-07/schema#"
}🟢what_fits_on_hardware(gpu, gpu_count, mac_ram_gb)
Rank which popular local LLMs fit on a given GPU or Apple Silicon Mac (at ~4-bit quantization, 8K context) — models that fit come first, biggest first, with max context each. Use when a user asks "what can I run on my <GPU/Mac/N GB>?", "best local model for my machine?", or gives hardware without naming a model.
輸入結構描述
{
"type": "object",
"properties": {
"gpu": {
"type": "string",
"description": "GPU name, fuzzy. Multi-GPU rigs: join with + (e.g. \"RTX 5090 + RTX 3090\"). Provide gpu OR mac_ram_gb."
},
"gpu_count": {
"type": "integer",
"minimum": 1,
"maximum": 8,
"description": "Number of identical copies of the gpu. Default 1."
},
"mac_ram_gb": {
"type": "integer",
"minimum": 8,
"maximum": 2048,
"description": "Apple Silicon unified memory GB. Provide gpu OR mac_ram_gb."
}
},
"additionalProperties": false,
"$schema": "http://json-schema.org/draft-07/schema#"
}🟢list_supported
List the built-in model names and hardware names this fit-checker knows (for mapping user wording to exact names). Standard text-only HuggingFace transformer configs can also be checked via fitllm.run; unsupported architectures are rejected.
輸入結構描述
{
"type": "object",
"properties": {},
"$schema": "http://json-schema.org/draft-07/schema#"
}建議的提示詞
list_supportedlist_supported社群
證據