fitllm

Architecture-aware, read-only LLM fit checks for GPUs, multi-GPU rigs, and Apple Silicon Macs, with full memory breakdowns and no login.

该来源不提供完整文件导出（国内平台多为平台内托管），仅存元数据与原链

接入信息

鉴权方式未标注，请核对官方文档后再接入——不要直接使用以下片段

{
  "mcpServers": {
    "fitllm": {
      "url": "https://fitllm--click6067.run.tools"
    }
  }
}

工具	说明
check_llm_fit	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 full memory breakdown (weights, KV cache, overhead), 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>?". Architecture-aware math (MLA, sliding-window, hybrid attention, MoE) — more accurate than rule-of-thumb estimates.
what_fits_on_hardware	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.
list_supported	List the built-in model names and hardware names this fit-checker knows (for mapping user wording to exact names). Any public HuggingFace model also works via fitllm.run.

纠错与举报（发现条目失效、署名有误或涉及侵权？）

提交举报 / 纠错

侵权举报经核验成立后，我们会即时下线该条目并删除已存的内容副本。