Llama Cpp
技能 已验证 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 cost-effective and accessible LLM inference on diverse consumer hardware, including edge devices and Macs, where high-end GPUs are unavailable or undesirable.
功能
- LLM inference on CPU, Apple Silicon, and consumer GPUs
- Support for GGUF quantization (1.5-8 bit)
- 4-10x speedup vs PyTorch on CPU
- OpenAI-compatible server mode
- Hardware acceleration (Metal, CUDA, ROCm)
使用场景
- Edge device LLM deployment
- Running LLMs on M1/M2/M3 Macs
- Inference on AMD or Intel GPUs
- Development environments where CUDA is unavailable
非目标
- Training LLMs
- Utilizing NVIDIA GPUs with CUDA (use TensorRT-LLM instead)
- Providing a Python-first API for NVIDIA GPUs (use vLLM instead)
安装
请先添加 Marketplace
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skills质量评分
已验证类似扩展
Llama Cpp
85Runs 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.