studiotv-llm-vram

endpoint

https://studiotvai.com/api/mcp

protocol

streamable-http ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is studiotv-llm-vram live?

Yes — it answered the hub's last check (checked 42m ago). It answered 100% of checks over the last 30 days.

Is studiotv-llm-vram free to use?

Yes — the hub reached it with no key and no payment.

What tools does studiotv-llm-vram have?

3 tools: estimate_vram, gpu_prices, models_that_fit.

Is studiotv-llm-vram safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• gpu_prices open 42m ago

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

  mcp-tool

  123456789

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

  arguments 9 lines copy
• models_that_fit open 42m ago

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

  mcp-tool

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

  arguments 67 lines copy
• estimate_vram unknown 42m ago

  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).

  mcp-tool

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

  arguments 73 lines copy

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