fitllm

endpoint

https://fitllm.run/api/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

Is fitllm live?

Yes — it answered the hub's last check (checked 7h ago). It answered 100% of checks over the last 30 days.

Is fitllm free to use?

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

What tools does fitllm have?

3 tools: check_llm_fit, what_fits_on_hardware, list_supported.

Is fitllm 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.

• what_fits_on_hardware reads open 28h ago

  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.

  mcp-tool

  1234567891011121314151617181920212223

  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "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",
        "maximum": 8,
        "minimum": 1,
        "description": "Number of identical copies of the gpu. Default 1."
      },
      "mac_ram_gb": {
        "type": "integer",
        "maximum": 2048,
        "minimum": 8,
        "description": "Apple Silicon unified memory GB. Provide gpu OR mac_ram_gb."
      }
    },
    "additionalProperties": false
  }

  arguments 23 lines copy
• list_supported reads open 28h ago

  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.

  mcp-tool

  12345

  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "properties": {}
  }

  arguments 5 lines copy
• check_llm_fit reads unknown 28h ago

  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).

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162

  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "model"
    ],
    "properties": {
      "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."
      },
      "env": {
        "enum": [
          "linux-headless",
          "linux-display",
          "windows-display"
        ],
        "type": "string",
        "description": "Discrete-GPU OS reserve: windows-display (default, 2 GB/card) · linux-display (1.2 GB) · linux-headless (0.6 GB, a second card with no display). Not for Macs or DGX Spark-class systems."
      },
      "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."
      },
      "model": {
        "type": "string",
        "description": "LLM name, fuzzy — e.g. \"GLM-4.7-Flash\", \"gpt-oss-20b\", \"gemma 31b\""
      },
      "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)."
      },
      "kv_bits": {
        "enum": [
          16,
          8,
          4
        ],
        "type": "number",
        "description": "KV-cache quantization bits (default 16 = F16)"
      },
      "gpu_count": {
        "type": "integer",
        "maximum": 8,
        "minimum": 1,
        "description": "Number of identical copies of the gpu (e.g. gpu=\"RTX 3090\", gpu_count=2 for a 2×3090 rig; gpu=\"DGX Spark 128GB\", gpu_count=2 or 4 for linked units under vLLM+Ray or TensorRT-LLM). Default 1."
      },
      "mac_ram_gb": {
        "type": "integer",
        "maximum": 2048,
        "minimum": 8,
        "description": "Apple Silicon unified memory in GB — e.g. 16, 64, 512. Provide gpu OR mac_ram_gb."
      },
      "context_tokens": {
        "type": "integer",
        "minimum": 1024,
        "description": "Context length in tokens (default 8192). Alias: ctx (same field as the REST API)."
      }
    },
    "additionalProperties": false
  }

  arguments 62 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.