FastGPU

endpoint

https://fastgpu.co/api/mcp

door code

bad2aa772ae2b730

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 FastGPU live?

Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.

Is FastGPU free to use?

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

What tools does FastGPU have?

2 tools: match_workload, list_gpu_prices.

Is FastGPU 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.

• list_gpu_prices reads open 1h ago

  One entry per GPU model with its cheapest live on-demand cloud rental price in USD per GPU-hour, the provider offering it, and how many providers rent it, from FastGPU's live price feed across marketplaces, neoclouds and hyperscalers (RunPod, Vast.ai, Lambda, AWS and more). Use it to compare GPU rental prices or to find where a named GPU is cheapest to rent. Each price is the provider's own per-GPU rate: cheapest_min_gpu_count says when it is only sold as a multi-GPU instance, and cheapest_billed_separately names what the provider bills on top of it (CPU, memory, storage), null when the rate includes CPU and memory. page_url links to the GPU's live price page with every offer. Rental prices only: it does not rent, reserve or launch GPUs. No key required.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "tier": {
        "enum": [
          "flagship",
          "datacenter",
          "prosumer",
          "entry"
        ],
        "type": "string",
        "description": "Return only one class of GPU (case-insensitive; combines with vendor). flagship: the current top training and inference accelerators (H100, H200, B200, GB200, MI300X, MI355X). datacenter: earlier or mid-range data-center and workstation cards (A100, L40S, L4, A10, RTX 6000 Ada, V100, MI210). prosumer: desktop consumer and workstation cards (RTX 5090, RTX 4090, RTX 3090, RTX A4000). entry: small inference cards (T4, A2, A16, P4). Omit for every tier."
      },
      "vendor": {
        "enum": [
          "NVIDIA",
          "AMD"
        ],
        "type": "string",
        "description": "Return only GPUs made by this vendor (case-insensitive). Combines with tier: both must match. Omit for every vendor."
      }
    }
  }

  arguments 23 lines copy
• match_workload reads unknown 1h ago

  Ranks up to eight GPU configurations that can run a job at the lowest live price. Describe the job in query (an open-weight model to serve or fine-tune, a model size, or a GPU need) or with the structured fields. Each match gives the GPU count, the USD per hour for the whole configuration and a one-line reason; the answer also states the VRAM the job needs and how much cheaper the top match is than a hyperscaler running the same job. Use it to decide where to run a workload; use list_gpu_prices for the price of a named GPU. Open-weight models only: an API-only model such as GPT-4 returns no matches and a note. Between otherwise equal offers, a small disclosed tie-break favors providers that pay FastGPU a referral (each match reports partner). Hard limits (budget_usd_hr, max_total_usd with duration_hours, max_quote_age_minutes) drop every configuration that breaks them; limits reports what was dropped. Read-only: it does not rent or launch GPUs. No key required.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "spot": {
        "enum": [
          "true",
          "false"
        ],
        "type": "string",
        "description": "Set true to include interruptible spot capacity for a cheaper rate."
      },
      "task": {
        "enum": [
          "inference",
          "finetune-lora",
          "finetune-full",
          "generate",
          "transcribe",
          "embed"
        ],
        "type": "string",
        "description": "What the job does."
      },
      "model": {
        "type": "string",
        "description": "Open model name to size against, e.g. \"Llama 3 70B\", \"Qwen 72B\", \"Mixtral\"."
      },
      "query": {
        "type": "string",
        "description": "Plain-language job, e.g. \"cheapest to serve Llama 3 70B\", \"2x H100 for fine-tuning\" or \"a GPU with 80GB of memory\". Provide this OR a structured spec below."
      },
      "region": {
        "enum": [
          "US",
          "EU",
          "ASIA"
        ],
        "type": "string",
        "description": "Restrict to a data-residency region."
      },
      "vram_gb": {
        "type": "integer",
        "description": "Rough VRAM the job needs, in GB, if you already know it."
      },
      "params_b": {
        "type": "number",
        "description": "Model size in billions of parameters when no exact model is named."
      },
      "reserved": {
        "enum": [
          "true",
          "false"
        ],
        "type": "string",
        "description": "Set true to include reserved / committed-term capacity for a lower rate."
      },
      "gpu_count": {
        "type": "integer",
        "description": "Exact positive GPU count. Overrides a count in query text. Returns no matches if no supported configuration fits; omit for automatic sizing."
      },
      "precision": {
        "enum": [
          "fp16",
          "int8",
          "int4"
        ],
        "type": "string",
        "description": "Numeric precision to size the model at."
      },
      "weights_gb": {
        "type": "number",
        "description": "Measured size of the weights as stored, in GB: the file you will load (for example a GGUF Q4_K_M build, which holds more bits per parameter than its nominal 4). Replaces parameters times bytes per parameter in the memory estimate. Used for a model sized by its parameter count; the answer's workload.weights_gb is null when it was not (an image or speech model sized as a whole pipeline)."
      },
      "budget_usd_hr": {
        "type": "number",
        "description": "Hard limit on the hourly price of the whole configuration, in USD. Configurations above it are not returned."
      },
      "max_total_usd": {
        "type": "number",
        "description": "Hard limit on estimated_total_usd, the GPU rental cost of the whole job, in USD. Needs duration_hours. Configurations above it are not returned."
      },
      "duration_hours": {
        "type": "number",
        "description": "How long the job will run, in hours (fractions are fine). When sent, each match carries billed_hours and estimated_total_usd."
      },
      "min_gpu_memory_gb": {
        "type": "integer",
        "description": "Memory each GPU card must have, in GB (e.g. 80 for 80GB-class cards such as the H100 or A100 80GB). Smaller cards are never returned. On its own it lists every card that size, cheapest first."
      },
      "max_quote_age_minutes": {
        "type": "number",
        "description": "Hard limit on how old a price quote may be, in minutes. Configurations whose price was fetched from the provider longer ago are not returned."
      }
    }
  }

  arguments 95 lines copy

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