MCP MeasureMCP Measure

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StudioTV LLM VRAM Calculator

Does an LLM fit on your GPU? VRAM, KV cache and GPU count for any Hugging Face model.

我該用這個嗎

品質與安全性

A
說明品質
93%
結構描述完整度
97%
命名品質
80%
汙染風險
80%
權限相符程度
100%
協定合規性
100%

發現項目（2）

  • HIGHTool poisoning patterns detected
  • MEDIUMTool description contains URL to non-standard domain在 estimate_vram 中

根據工具定義與協定合規性的自動化分析。

上下文成本

~877Token（工具定義）
~2.2 KB典型回應大小
中等的注意力影響（128k 上下文的 0.69%）

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": {
    "llm-vram": {
      "url": "https://studiotvai.com/api/mcp"
    }
  }
}

遠端端點

https://studiotvai.com/api/mcpstreamable-http

它能做什麼

工具清單

工具（3）

🟢 唯讀🟡 寫入🔴 刪除⚪ 未知
🟢estimate_vram(model, gpu, gpu_count, context, concurrent_requests, ...)

GPU memory, number of GPUs, speed and rental cost to run an open LLM. Works for the models listed at https://studiotvai.com/api/models.json and any Hugging Face model id or link. Uses the real KV cache of each architecture (sliding window, hybrid linear attention, MLA).

輸入結構描述

{
  "type": "object",
  "properties": {
    "model": {
      "type": "string",
      "description": "Model name or id (\"Llama 3.3 70B\", \"qwen3.8-27b\"), Hugging Face id (\"Qwen/Qwen3-32B\") or link."
    },
    "gpu": {
      "type": "string",
      "description": "GPU id or name, e.g. \"rtx-4090\", \"H100\", \"mac-m4-max-128\". Default h100. List: https://studiotvai.com/api/gpus.json"
    },
    "gpu_count": {
      "type": [
        "integer",
        "string"
      ],
      "description": "Number of GPUs, or \"auto\" (default) for the fewest that fit."
    },
    "context": {
      "type": "integer",
      "minimum": 1,
      "description": "Tokens per request (prompt + output). Default 8192."
    },
    "concurrent_requests": {
      "type": "integer",
      "minimum": 1,
      "description": "Requests served at the same time, each with its own KV cache. Default 1."
    },
    "weights": {
      "type": "string",
      "enum": [
        "native",
        "bf16",
        "fp8",
        "int8",
        "nvfp4",
        "mxfp4",
        "int4",
        "q8_0",
        "q6_k",
        "q5_k_m",
        "q4_k_m",
        "q3_k_m",
        "fp32"
      ],
      "description": "Weight format. Default: the official checkpoint (native) or BF16."
    },
    "kv_cache": {
      "type": "string",
      "enum": [
        "bf16",
        "fp8",
        "q8_0",
        "q4_0"
      ],
      "description": "KV cache precision. Default bf16."
    },
    "engine": {
      "type": "string",
      "enum": [
        "vllm",
        "sglang",
        "trtllm",
        "llamacpp",
        "transformers"
      ],
      "description": "Serving engine. Default: vllm on data-center GPUs, llamacpp elsewhere."
    }
  },
  "required": [
    "model"
  ]
}
🟢models_that_fit(gpu, gpu_count, context, concurrent_requests, weights, ...)

Every listed open model that fits on the given GPU(s), largest first, with the most faithful weight format that fits.

輸入結構描述

{
  "type": "object",
  "properties": {
    "gpu": {
      "type": "string",
      "description": "GPU id or name, e.g. \"rtx-4090\"."
    },
    "gpu_count": {
      "type": "integer",
      "minimum": 1,
      "description": "Default 1."
    },
    "context": {
      "type": "integer",
      "minimum": 1,
      "description": "Tokens per request (prompt + output). Default 8192."
    },
    "concurrent_requests": {
      "type": "integer",
      "minimum": 1,
      "description": "Requests served at the same time, each with its own KV cache. Default 1."
    },
    "weights": {
      "type": "string",
      "enum": [
        "native",
        "bf16",
        "fp8",
        "int8",
        "nvfp4",
        "mxfp4",
        "int4",
        "q8_0",
        "q6_k",
        "q5_k_m",
        "q4_k_m",
        "q3_k_m",
        "fp32"
      ],
      "description": "Weight format. Default: the official checkpoint (native) or BF16."
    },
    "kv_cache": {
      "type": "string",
      "enum": [
        "bf16",
        "fp8",
        "q8_0",
        "q4_0"
      ],
      "description": "KV cache precision. Default bf16."
    },
    "engine": {
      "type": "string",
      "enum": [
        "vllm",
        "sglang",
        "trtllm",
        "llamacpp",
        "transformers"
      ],
      "description": "Serving engine. Default: vllm on data-center GPUs, llamacpp elsewhere."
    }
  },
  "required": [
    "gpu"
  ]
}
🟢gpu_prices(gpu)

Cheapest on-demand price per GPU-hour from RunPod, Vast.ai, Verda and Azure, checked every hour.

輸入結構描述

{
  "type": "object",
  "properties": {
    "gpu": {
      "type": "string",
      "description": "GPU id or name. Omit for every GPU."
    }
  }
}

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