Hardware LLM Optimizer v2 (llmfit)

AI硬件LLM推荐工具 - 基于llmfit内核。自动检测CPU/GPU/RAM/VRAM → 智能推荐最适合的大模型 + 量化方案 + 速度估算。支持100+模型库，内置TUI界面和硬件模拟。

Security Scan

The skill's functionality (hardware detection + model recommendations) matches its description, but the documentation encourages installing 'llmfit' via a curl|sh from an untrusted domain and asserts an installed binary path without an install spec — this is disproportionate and risky.

Purpose & Capability

The skill claims to detect hardware and recommend LLMs/quantization, and the included detect.py implements that behavior (psutil, nvidia-smi checks, /proc/version). However, the SKILL.md centers runtime usage on an external tool 'llmfit' (commands like 'llmfit recommend') while the package provides no install mechanism for llmfit and even asserts llmfit is already at /usr/local/bin; relying on an external binary that isn't provided weakens coherence.

Instruction Scope

Runtime instructions and detect.py stay within the stated purpose: they inspect local system state (CPU, RAM, nvidia-smi, /proc/version), produce recommendations, and reference running local model runtimes (ollama, llama.cpp). The SKILL.md does suggest running network-facing installs and model downloads, but it does not instruct reading unrelated secrets or sending detected data to external endpoints.

Install Mechanism

There is no formal install spec, but SKILL.md recommends installing llmfit with: curl -fsSL https://llmfit.axjns.dev/install.sh | sh. That is a direct download-and-execute from an unrecognized domain (axjns.dev) — high-risk practice. The skill itself does not include code to fetch that URL, but recommending it without provenance is disproportionate and potentially dangerous.

Credentials

The skill requests no environment variables, no credentials, and detect.py only queries local system info. There is no inappropriate credential access requested.

Persistence & Privilege

The skill does not request always:true and is not marked to be force-included. It does not attempt to modify other skills or system-wide configs. Normal autonomous invocation is allowed (platform default).

What to consider before installing

This skill's detection code (detect.py) appears benign and aligned with the description: it inspects local hardware via psutil and nvidia-smi and prints recommendations. However, the SKILL.md asks you to install 'llmfit' by piping a script from https://llmfit.axjns.dev/install.sh directly into sh — that pattern is high-risk because it runs arbitrary code from an unvetted host. Before installing or running anything: (1) do NOT run curl ... | sh without inspecting the script; fetch the URL and review its contents first; (2) prefer installing llmfit from a known official source (GitHub releases, vendor homepage) or verify the domain and script integrity; (3) run detect.py locally in a restricted environment if you only want hardware info (it has no network calls); (4) ensure you understand any model downloads (GGUF/ollama) and avoid running unfamiliar binaries as root. If the maintainer can provide a verified upstream URL (official project repo/releases) or an explicit install spec using a reputable package host, that would reduce risk and could change this assessment.

Hardware LLM Optimizer v2.0

基于 llmfit 的智能硬件LLM推荐工具

安装状态

llmfit 已安装在: /usr/local/bin/llmfit

快速使用

当用户问"能跑什么大模型"、"推荐LLM"、"硬件检测"时使用：

1. 查看推荐模型

llmfit recommend

2. 查看所有推荐（JSON格式，便于解析）

llmfit recommend --json

3. 按用途筛选

llmfit recommend --use-case coding
llmfit recommend --use-case chat
llmfit recommend --use-case general
llmfit recommend --use-case embedding

4. 硬件模拟（模拟不同配置）

# 模拟 16GB 显存
llmfit recommend --memory 16G

# 模拟 32GB 显存 + 64GB RAM
llmfit recommend --memory 32G --ram 64G

5. 交互式TUI（需要终端）

llmfit

输出字段说明

字段	含义
name	模型名称
parameter_count	参数量
best_quant	推荐量化方案
score	综合评分（越高越好）
estimated_tps	预估速度（tok/s）
memory_required_gb	所需显存
run_mode	运行模式（GPU/CPU/MoE）
fit_level	匹配度（Perfect/Good/Marginal）

量化方案参考

量化	质量	速度	适用场景
FP16	最高	最慢	大显存GPU
Q8_0	很高	较快	中等显存
Q6_K	高	快	6-8GB显存
Q4_K_M	中高	最快	4-6GB显存
Q2_K	中	最快	<4GB显存

本地运行命令

安装Ollama模型

ollama run <model-name>

使用llama.cpp

# 下载GGUF后
./llama.cpp -m <model.gguf> --rompt <prompt>

最低配置参考（来自llmfit）

显存	推荐模型	量化
2GB	Phi-3-mini, Gemma-2B	Q4
4GB	Llama3-8B, Qwen-7B	Q4
6GB	Llama2-13B, Mistral-7B	Q4/Q6
8GB	Llama2-13B, Yi-9B	Q5/Q6
12GB	Llama2-34B	Q4
16GB	Llama2-34B, Qwen-72B	Q4
24GB+	70B大模型	Q4/Q8

安装llmfit（如需）

curl -fsSL https://llmfit.axjns.dev/install.sh | sh

优势对比

功能	v1.0	v2.0 (llmfit)
模型库	手动查表	100+自动匹配
量化推荐	简单估算	智能最优
速度估算	❌	✅
下载源	❌	✅ GGUF
硬件模拟	❌	✅
TUI界面	❌	✅
多GPU	❌	✅
MoE支持	❌	✅

Powered by llmfit | Updated: 2026-04-17