Llama Cpp

orchestra-research/ai-research-skills/12-inference-serving/llama-cpp

作者 orchestra-research773a52944ba4MIT13K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫3 個月前更新

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.

僅含說明AI & Agents DevOps & Cloud

AI 產生的概覽

指導在 CPU、Apple Silicon 與非 NVIDIA GPU 上使用 llama.cpp 進行本機 LLM 推論。

功能: 此技能提供安裝與使用 llama.cpp（一套 C/C++ LLM 推論引擎）的說明，包含 Metal、CUDA 與 ROCm 的建置選項。內容涵蓋下載或轉換 GGUF 模型、執行聊天與批次推論、啟動相容於 OpenAI 的伺服器，以及選擇量化格式。它也包含硬體加速說明、效能基準測試與支援的模型系列。
適用情境: 適用於在僅有 CPU 的機器、Apple Silicon Mac、AMD 或 Intel GPU，或沒有 NVIDIA CUDA 硬體的邊緣裝置上執行 LLM 推論。當你需要透過 GGUF 量化降低記憶體用量，或希望不需 Docker 或 Python 就能簡單部署時，也很適合使用。
執行需求: 需要安裝 llama.cpp（透過 Homebrew 或從原始碼建置），並準備 GGUF 模型檔案。選用的加速需要 Metal、CUDA 或 ROCm 建置支援。此技能將 llama-cpp-python 列為相依項目，並引用 Hugging Face 下載模型。它不附帶指令碼，僅為說明文件。

llama.cpp

Pure C/C++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.

When to use llama.cpp

Use llama.cpp when:

Running on CPU-only machines
Deploying on Apple Silicon (M1/M2/M3/M4)
Using AMD or Intel GPUs (no CUDA)
Edge deployment (Raspberry Pi, embedded systems)
Need simple deployment without Docker/Python

Use TensorRT-LLM instead when:

Have NVIDIA GPUs (A100/H100)
Need maximum throughput (100K+ tok/s)
Running in datacenter with CUDA

Use vLLM instead when:

Have NVIDIA GPUs
Need Python-first API
Want PagedAttention

Quick start

Installation

bash

# macOS/Linuxbrew install llama.cpp
# Or build from sourcegit clone https://github.com/ggerganov/llama.cppcd llama.cppmake
# With Metal (Apple Silicon)make LLAMA_METAL=1
# With CUDA (NVIDIA)make LLAMA_CUDA=1
# With ROCm (AMD)make LLAMA_HIP=1

# macOS/Linuxbrew install llama.cpp
# Or build from sourcegit clone https://github.com/ggerganov/llama.cppcd llama.cppmake
# With Metal (Apple Silicon)make LLAMA_METAL=1
# With CUDA (NVIDIA)make LLAMA_CUDA=1
# With ROCm (AMD)make LLAMA_HIP=1

Download model

bash

# Download from HuggingFace (GGUF format)huggingface-cli download \    TheBloke/Llama-2-7B-Chat-GGUF \    llama-2-7b-chat.Q4_K_M.gguf \    --local-dir models/
# Or convert from HuggingFacepython convert_hf_to_gguf.py models/llama-2-7b-chat/

# Download from HuggingFace (GGUF format)huggingface-cli download \    TheBloke/Llama-2-7B-Chat-GGUF \    llama-2-7b-chat.Q4_K_M.gguf \    --local-dir models/
# Or convert from HuggingFacepython convert_hf_to_gguf.py models/llama-2-7b-chat/

Run inference

bash

# Simple chat./llama-cli \    -m models/llama-2-7b-chat.Q4_K_M.gguf \    -p "Explain quantum computing" \    -n 256  # Max tokens
# Interactive chat./llama-cli \    -m models/llama-2-7b-chat.Q4_K_M.gguf \    --interactive

# Simple chat./llama-cli \    -m models/llama-2-7b-chat.Q4_K_M.gguf \    -p "Explain quantum computing" \    -n 256  # Max tokens
# Interactive chat./llama-cli \    -m models/llama-2-7b-chat.Q4_K_M.gguf \    --interactive

Server mode

bash

# Start OpenAI-compatible server./llama-server \    -m models/llama-2-7b-chat.Q4_K_M.gguf \    --host 0.0.0.0 \    --port 8080 \    -ngl 32  # Offload 32 layers to GPU
# Client requestcurl http://localhost:8080/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{    "model": "llama-2-7b-chat",    "messages": [{"role": "user", "content": "Hello!"}],    "temperature": 0.7,    "max_tokens": 100  }'

# Start OpenAI-compatible server./llama-server \    -m models/llama-2-7b-chat.Q4_K_M.gguf \    --host 0.0.0.0 \    --port 8080 \    -ngl 32  # Offload 32 layers to GPU
# Client requestcurl http://localhost:8080/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{    "model": "llama-2-7b-chat",    "messages": [{"role": "user", "content": "Hello!"}],    "temperature": 0.7,    "max_tokens": 100  }'

Quantization formats

GGUF format overview

Format	Bits	Size (7B)	Speed	Quality	Use Case
Q4_K_M	4.5	4.1 GB	Fast	Good	Recommended default
Q4_K_S	4.3	3.9 GB	Faster	Lower	Speed critical
Q5_K_M	5.5	4.8 GB	Medium	Better	Quality critical
Q6_K	6.5	5.5 GB	Slower	Best	Maximum quality
Q8_0	8.0	7.0 GB	Slow	Excellent	Minimal degradation
Q2_K	2.5	2.7 GB	Fastest	Poor	Testing only

Choosing quantization

bash

# General use (balanced)Q4_K_M  # 4-bit, medium quality
# Maximum speed (more degradation)Q2_K or Q3_K_M
# Maximum quality (slower)Q6_K or Q8_0
# Very large models (70B, 405B)Q3_K_M or Q4_K_S  # Lower bits to fit in memory

# General use (balanced)Q4_K_M  # 4-bit, medium quality
# Maximum speed (more degradation)Q2_K or Q3_K_M
# Maximum quality (slower)Q6_K or Q8_0
# Very large models (70B, 405B)Q3_K_M or Q4_K_S  # Lower bits to fit in memory

Hardware acceleration

Apple Silicon (Metal)

bash

# Build with Metalmake LLAMA_METAL=1
# Run with GPU acceleration (automatic)./llama-cli -m model.gguf -ngl 999  # Offload all layers
# Performance: M3 Max 40-60 tokens/sec (Llama 2-7B Q4_K_M)

# Build with Metalmake LLAMA_METAL=1
# Run with GPU acceleration (automatic)./llama-cli -m model.gguf -ngl 999  # Offload all layers
# Performance: M3 Max 40-60 tokens/sec (Llama 2-7B Q4_K_M)

NVIDIA GPUs (CUDA)

bash

# Build with CUDAmake LLAMA_CUDA=1
# Offload layers to GPU./llama-cli -m model.gguf -ngl 35  # Offload 35/40 layers
# Hybrid CPU+GPU for large models./llama-cli -m llama-70b.Q4_K_M.gguf -ngl 20  # GPU: 20 layers, CPU: rest

# Build with CUDAmake LLAMA_CUDA=1
# Offload layers to GPU./llama-cli -m model.gguf -ngl 35  # Offload 35/40 layers
# Hybrid CPU+GPU for large models./llama-cli -m llama-70b.Q4_K_M.gguf -ngl 20  # GPU: 20 layers, CPU: rest

AMD GPUs (ROCm)

Common patterns

Batch processing

bash

# Process multiple prompts from filecat prompts.txt | ./llama-cli \    -m model.gguf \    --batch-size 512 \    -n 100

# Process multiple prompts from filecat prompts.txt | ./llama-cli \    -m model.gguf \    --batch-size 512 \    -n 100

Constrained generation

bash

# JSON output with grammar./llama-cli \    -m model.gguf \    -p "Generate a person: " \    --grammar-file grammars/json.gbnf
# Outputs valid JSON only

# JSON output with grammar./llama-cli \    -m model.gguf \    -p "Generate a person: " \    --grammar-file grammars/json.gbnf
# Outputs valid JSON only

Context size

bash

# Increase context (default 512)./llama-cli \    -m model.gguf \    -c 4096  # 4K context window
# Very long context (if model supports)./llama-cli -m model.gguf -c 32768  # 32K context

# Increase context (default 512)./llama-cli \    -m model.gguf \    -c 4096  # 4K context window
# Very long context (if model supports)./llama-cli -m model.gguf -c 32768  # 32K context

Performance benchmarks

CPU performance (Llama 2-7B Q4_K_M)

GPU acceleration (Llama 2-7B Q4_K_M)

Supported models

LLaMA family:

Llama 2 (7B, 13B, 70B)
Llama 3 (8B, 70B, 405B)
Code Llama

Mistral family:

Mistral 7B
Mixtral 8x7B, 8x22B

Other:

Falcon, BLOOM, GPT-J
Phi-3, Gemma, Qwen
LLaVA (vision), Whisper (audio)

Find models: https://huggingface.co/models?library=gguf

References

Quantization Guide [blocked] - GGUF formats, conversion, quality comparison
Server Deployment [blocked] - API endpoints, Docker, monitoring
Optimization [blocked] - Performance tuning, hybrid CPU+GPU

Resources

GitHub: https://github.com/ggerganov/llama.cpp
Models: https://huggingface.co/models?library=gguf
Discord: https://discord.gg/llama-cpp

來源與署名

來源：orchestra-research/ai-research-skills位於12-inference-serving/llama-cpp提交773a529

授權條款： MIT

內容歸原作者所有。SourceWeft 從公開儲存庫中收錄這些內容。

檢舉或申請下架

CPU	Threads	Speed	Cost
Apple M3 Max	16	50 tok/s	$0 (local)
AMD Ryzen 9 7950X	32	35 tok/s	$0.50/hour
Intel i9-13900K	32	30 tok/s	$0.40/hour
AWS c7i.16xlarge	64	40 tok/s	$2.88/hour

GPU	Speed	vs CPU	Cost
NVIDIA RTX 4090	120 tok/s	3-4×	$0 (local)
NVIDIA A10	80 tok/s	2-3×	$1.00/hour
AMD MI250	70 tok/s	2×	$2.00/hour
Apple M3 Max (Metal)	50 tok/s	~Same	$0 (local)

Llama Cpp

llama.cpp

When to use llama.cpp

Quick start

Installation

Download model

Run inference

Server mode

Quantization formats

GGUF format overview

Choosing quantization

Hardware acceleration

Apple Silicon (Metal)

NVIDIA GPUs (CUDA)

AMD GPUs (ROCm)

Common patterns

Batch processing

Constrained generation

Context size

Performance benchmarks

CPU performance (Llama 2-7B Q4_K_M)

GPU acceleration (Llama 2-7B Q4_K_M)

Supported models

References

Resources

來源與署名

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