Llama Cpp
技能 活跃Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
To enable efficient and accessible LLM inference on hardware lacking NVIDIA GPUs, making local and edge LLM deployments feasible.
功能
- CPU-only inference
- Apple Silicon (M1/M2/M3) optimization
- AMD/Intel GPU support (non-CUDA)
- GGUF quantization (1.5-8 bit)
- OpenAI-compatible API server mode
使用场景
- Running LLMs on personal Macs or Linux machines
- Edge deployments on resource-constrained devices
- Local LLM development and testing without GPU hardware
- Utilizing models when CUDA is unavailable
非目标
- Maximizing throughput on high-end NVIDIA GPUs
- Providing a Python-first API like vLLM or TensorRT-LLM
- Managing cloud infrastructure for LLM serving
Trust
- warning:Issues AttentionIn the last 90 days, 17 issues were opened and 4 were closed, indicating a low closure rate (approx. 23.5%) and potentially slow maintainer response.
安装
npx skills add davila7/claude-code-templates通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
类似扩展
Llama Cpp
95Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
GGUF Quantization
95GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
GGUF Quantization
98GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
VLLM High Performance LLM Serving
97Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
Hugging Face Local Models
95Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
Cli Anything Quietshrink
99Compress macOS screen recordings with zero CPU stress using Apple Silicon's hardware HEVC encoder. Typically reduces file size 70-90% while staying visually lossless. Computer stays silent during encoding.