_ registry / mcp http-sse · checked 3h ago

nodegrove-vram

https://mcp.nodegrove.io

Registry code: 36e8a6d72bbfce6f

api record

Answers whether an open-weight LLM fits a GPU, with the formulas nodegrove.io publishes. Use can_i_run for "can my GPU run this model", what_fits for "what can my GPU run", estimate_vram for memory at each quantisation, estimate_from_hf_repo to read any Hugging Face repo, and list_models / list_gpus to find ids. Figures are estimates from stated formulas over config.json values and makers' specs, never benchmarks, and speeds are upper bounds: say so when you quote them, and give the page link from the result. The data is CC BY 4.0: credit Nodegrove (nodegrove.io).

endpoint
https://mcp.nodegrove.io/mcp
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
365ms

last good check

priced tools
0

of 6 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 6 tools
2 open 4 never probed 2 of 6 classified

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_models open 3h ago

    The open-weight LLMs nodegrove.io has verified against their config.json (data version 2026-09-25): id, size, attention design, native context, licence, memory at Q4 with 8k context and each model's page.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "search": {
      "type": "string",
      "maxLength": 100,
      "description": "Words to filter by, e.g. \"qwen\" or \"24 GB\"."
    }
  }
}
    arguments 11 lines
  • list_gpus open 3h ago

    The GPUs and machines nodegrove.io covers: memory, the memory a runtime can use and bandwidth, from the makers' specs, with each one's page.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "search": {
      "type": "string",
      "maxLength": 100,
      "description": "Words to filter by, e.g. \"qwen\" or \"24 GB\"."
    }
  }
}
    arguments 11 lines
  • can_i_run unknown never probed

    Can this GPU run this open-weight LLM? Returns fits, tight or no, the memory split (weights, KV cache, overhead), a decode-speed ceiling, the longest context that fits and, on a no, every change that would make it fit: quantisation, KV cache, context, another card or a smaller model. Model: a name or id from list_models, any Hugging Face repo id, or its architecture. GPU: a name or id from list_gpus, or vram_gb for any other card.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "gpu": {
      "type": "string",
      "maxLength": 100,
      "minLength": 1,
      "description": "A GPU from list_gpus (id or name, e.g. \"rtx-4090\", \"4090\" or \"M4 Max\")."
    },
    "model": {
      "type": "string",
      "maxLength": 200,
      "minLength": 1,
      "description": "A model from list_models (id or name, e.g. \"llama-3.3-70b\" or \"Llama 3.3 70B\"), or any Hugging Face repo id (e.g. \"Qwen/Qwen3-8B\"), read live from its config.json."
    },
    "quant": {
      "enum": [
        "fp16",
        "q8",
        "q6",
        "q5",
        "q4",
        "q3"
      ],
      "type": "string",
      "default": "q4",
      "description": "Weight quantisation: fp16 (FP16 / BF16), q8 (Q8_0), q6 (Q6_K), q5 (Q5_K_M), q4 (Q4_K_M), q3 (Q3_K_M). q4 is the common default."
    },
    "context": {
      "type": "integer",
      "default": 8192,
      "maximum": 10000000,
      "minimum": 1,
      "description": "Tokens held in context: prompt plus conversation."
    },
    "vram_gb": {
      "type": "number",
      "maximum": 4096,
      "description": "Memory of a card not in list_gpus, GB. For a Mac, its unified memory with apple_silicon: true.",
      "exclusiveMinimum": 0
    },
    "kv_cache": {
      "enum": [
        "fp16",
        "q8"
      ],
      "type": "string",
      "default": "fp16",
      "description": "KV cache precision. fp16 is what most runtimes use; q8 halves the cache."
    },
    "architecture": {
      "type": "object",
      "required": [
        "params_b",
        "layers",
        "kv_heads",
        "head_dim"
      ],
      "properties": {
        "layers": {
          "type": "integer",
          "maximum": 1000,
          "description": "num_hidden_layers",
          "exclusiveMinimum": 0
        },
        "head_dim": {
          "type": "integer",
          "maximum": 4096,
          "description": "head_dim, or hidden_size ÷ num_attention_heads",
          "exclusiveMinimum": 0
        },
        "kv_heads": {
          "type": "integer",
          "maximum": 1024,
          "description": "num_key_value_heads",
          "exclusiveMinimum": 0
        },
        "params_b": {
          "type": "number",
          "maximum": 10000,
          "description": "Total parameters, billions; all experts for a mixture-of-experts model.",
          "exclusiveMinimum": 0
        },
        "kv_groups": {
          "type": "array",
          "items": {
            "type": "object",
            "required": [
              "layers",
              "values_per_token"
            ],
            "properties": {
              "layers": {
                "type": "integer",
                "maximum": 9007199254740991,
                "exclusiveMinimum": 0
              },
              "window_tokens": {
                "type": "integer",
                "maximum": 9007199254740991,
                "description": "Sliding window: these layers keep only this many tokens.",
                "exclusiveMinimum": 0
              },
              "values_per_token": {
                "type": "number",
                "description": "Values each layer caches per token: 2 × KV heads × head dim, or the latent width for MLA.",
                "exclusiveMinimum": 0
              }
            }
          },
          "maxItems": 8,
          "description": "Only for non-standard attention: one entry per group of layers that cache the same way. Replaces layers × kv_heads × head_dim."
        },
        "fixed_state_gb": {
          "type": "number",
          "maximum": 100,
          "minimum": 0,
          "description": "Fixed recurrent state of linear-attention or Mamba layers, GB."
        },
        "native_context": {
          "type": "integer",
          "maximum": 9007199254740991,
          "description": "The context window the model supports, tokens.",
          "exclusiveMinimum": 0
        },
        "active_params_b": {
          "type": "number",
          "maximum": 10000,
          "description": "Parameters read per token, billions (mixture-of-experts only).",
          "exclusiveMinimum": 0
        }
      },
      "description": "A model described by its config.json values instead of a name."
    },
    "apple_silicon": {
      "type": "boolean",
      "description": "vram_gb is Apple unified memory; the GPU can use about 75% of it by default."
    },
    "bandwidth_gb_s": {
      "type": "number",
      "maximum": 100000,
      "description": "Memory bandwidth from the maker's spec, GB/s, for a speed ceiling.",
      "exclusiveMinimum": 0
    },
    "active_params_b": {
      "type": "number",
      "maximum": 10000,
      "description": "Parameters read per token, billions, for a mixture-of-experts model read from Hugging Face (from its model card). Sets the speed ceiling.",
      "exclusiveMinimum": 0
    }
  }
}
    arguments 153 lines
  • estimate_from_hf_repo unknown never probed

    Reads any Hugging Face model repo's config.json and parameter count and estimates its memory: the attention layout found (standard, sliding-window, hybrid or latent), how much each 1,000 tokens of context costs, and weights + KV cache + overhead at every quantisation. For models nodegrove.io has not reviewed; anything the reader cannot model is listed in warnings.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "repo"
  ],
  "properties": {
    "repo": {
      "type": "string",
      "maxLength": 200,
      "minLength": 3,
      "description": "Hugging Face repo id, e.g. \"Qwen/Qwen3-8B\", or its huggingface.co URL."
    },
    "context": {
      "type": "integer",
      "default": 8192,
      "maximum": 10000000,
      "minimum": 1,
      "description": "Tokens held in context: prompt plus conversation."
    },
    "kv_cache": {
      "enum": [
        "fp16",
        "q8"
      ],
      "type": "string",
      "default": "fp16",
      "description": "KV cache precision. fp16 is what most runtimes use; q8 halves the cache."
    },
    "active_params_b": {
      "type": "number",
      "maximum": 10000,
      "description": "Parameters read per token, billions, for a mixture-of-experts model read from Hugging Face (from its model card). Sets the speed ceiling.",
      "exclusiveMinimum": 0
    }
  }
}
    arguments 37 lines
  • what_fits unknown never probed

    Which open-weight LLMs fit this GPU: every model in list_models checked at one quantisation and context, with a recommended everyday model (the biggest class that fits with room for context at conversational speed), the largest that fits, the best at Q8 and the first out of reach. GPU: a name or id from list_gpus, or vram_gb for any other card.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "gpu": {
      "type": "string",
      "maxLength": 100,
      "minLength": 1,
      "description": "A GPU from list_gpus (id or name, e.g. \"rtx-4090\", \"4090\" or \"M4 Max\")."
    },
    "quant": {
      "enum": [
        "fp16",
        "q8",
        "q6",
        "q5",
        "q4",
        "q3"
      ],
      "type": "string",
      "default": "q4",
      "description": "Weight quantisation: fp16 (FP16 / BF16), q8 (Q8_0), q6 (Q6_K), q5 (Q5_K_M), q4 (Q4_K_M), q3 (Q3_K_M). q4 is the common default."
    },
    "context": {
      "type": "integer",
      "default": 8192,
      "maximum": 10000000,
      "minimum": 1,
      "description": "Tokens held in context: prompt plus conversation."
    },
    "vram_gb": {
      "type": "number",
      "maximum": 4096,
      "description": "Memory of a card not in list_gpus, GB. For a Mac, its unified memory with apple_silicon: true.",
      "exclusiveMinimum": 0
    },
    "apple_silicon": {
      "type": "boolean",
      "description": "vram_gb is Apple unified memory; the GPU can use about 75% of it by default."
    },
    "bandwidth_gb_s": {
      "type": "number",
      "maximum": 100000,
      "description": "Memory bandwidth from the maker's spec, GB/s, for a speed ceiling.",
      "exclusiveMinimum": 0
    }
  }
}
    arguments 48 lines
  • estimate_vram unknown 3h ago

    How much memory an LLM needs: weights + KV cache + overhead at each quantisation (or one), at a given context, and the smallest common card class that holds each. Model: a name or id from list_models, any Hugging Face repo id, or its architecture (params_b, layers, kv_heads, head_dim).

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "model": {
      "type": "string",
      "maxLength": 200,
      "minLength": 1,
      "description": "A model from list_models (id or name, e.g. \"llama-3.3-70b\" or \"Llama 3.3 70B\"), or any Hugging Face repo id (e.g. \"Qwen/Qwen3-8B\"), read live from its config.json."
    },
    "quant": {
      "enum": [
        "fp16",
        "q8",
        "q6",
        "q5",
        "q4",
        "q3"
      ],
      "type": "string",
      "description": "Weight quantisation: fp16 (FP16 / BF16), q8 (Q8_0), q6 (Q6_K), q5 (Q5_K_M), q4 (Q4_K_M), q3 (Q3_K_M). Omit it for all 6."
    },
    "context": {
      "type": "integer",
      "default": 8192,
      "maximum": 10000000,
      "minimum": 1,
      "description": "Tokens held in context: prompt plus conversation."
    },
    "kv_cache": {
      "enum": [
        "fp16",
        "q8"
      ],
      "type": "string",
      "default": "fp16",
      "description": "KV cache precision. fp16 is what most runtimes use; q8 halves the cache."
    },
    "architecture": {
      "type": "object",
      "required": [
        "params_b",
        "layers",
        "kv_heads",
        "head_dim"
      ],
      "properties": {
        "layers": {
          "type": "integer",
          "maximum": 1000,
          "description": "num_hidden_layers",
          "exclusiveMinimum": 0
        },
        "head_dim": {
          "type": "integer",
          "maximum": 4096,
          "description": "head_dim, or hidden_size ÷ num_attention_heads",
          "exclusiveMinimum": 0
        },
        "kv_heads": {
          "type": "integer",
          "maximum": 1024,
          "description": "num_key_value_heads",
          "exclusiveMinimum": 0
        },
        "params_b": {
          "type": "number",
          "maximum": 10000,
          "description": "Total parameters, billions; all experts for a mixture-of-experts model.",
          "exclusiveMinimum": 0
        },
        "kv_groups": {
          "type": "array",
          "items": {
            "type": "object",
            "required": [
              "layers",
              "values_per_token"
            ],
            "properties": {
              "layers": {
                "type": "integer",
                "maximum": 9007199254740991,
                "exclusiveMinimum": 0
              },
              "window_tokens": {
                "type": "integer",
                "maximum": 9007199254740991,
                "description": "Sliding window: these layers keep only this many tokens.",
                "exclusiveMinimum": 0
              },
              "values_per_token": {
                "type": "number",
                "description": "Values each layer caches per token: 2 × KV heads × head dim, or the latent width for MLA.",
                "exclusiveMinimum": 0
              }
            }
          },
          "maxItems": 8,
          "description": "Only for non-standard attention: one entry per group of layers that cache the same way. Replaces layers × kv_heads × head_dim."
        },
        "fixed_state_gb": {
          "type": "number",
          "maximum": 100,
          "minimum": 0,
          "description": "Fixed recurrent state of linear-attention or Mamba layers, GB."
        },
        "native_context": {
          "type": "integer",
          "maximum": 9007199254740991,
          "description": "The context window the model supports, tokens.",
          "exclusiveMinimum": 0
        },
        "active_params_b": {
          "type": "number",
          "maximum": 10000,
          "description": "Parameters read per token, billions (mixture-of-experts only).",
          "exclusiveMinimum": 0
        }
      },
      "description": "A model described by its config.json values instead of a name."
    },
    "active_params_b": {
      "type": "number",
      "maximum": 10000,
      "description": "Parameters read per token, billions, for a mixture-of-experts model read from Hugging Face (from its model card). Sets the speed ceiling.",
      "exclusiveMinimum": 0
    }
  }
}
    arguments 130 lines
_ try it through the hub, ceiling 0

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_ record

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