/llama-cpp

description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
dependencies:
- llama-cpp-python
metadata:
  catalog:
    tags:
    - Inference Serving
    - Llama.cpp
    - CPU Inference
    - Apple Silicon
    - Edge Deployment
    - GGUF
    - Quantization
    - Non-NVIDIA
    - AMD GPUs
    - Intel GPUs
    - Embedded

Kheish Compatibility

This skill is repo-local and stays inactive until explicitly activated.

When the original instructions refer to legacy tool names, use these Kheish mappings:

• `terminal` => `bash`
• `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
• `search_files` => `grep_search` and `glob_search`
• `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly

When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.

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

# macOS/Linux
brew install llama.cpp

# Or build from source
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

# With Metal (Apple Silicon)
make LLAMA_METAL=1

# With CUDA (NVIDIA)
make LLAMA_CUDA=1

# With ROCm (AMD)
make LLAMA_HIP=1

Download model

# 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 HuggingFace
python convert_hf_to_gguf.py models/llama-2-7b-chat/

Run inference

# 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

# 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 request
curl 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

# 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)

# Build with Metal
make 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)

# Build with CUDA
make 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)

# Build with ROCm
make LLAMA_HIP=1

# Run with AMD GPU
./llama-cli -m model.gguf -ngl 999

Common patterns

Batch processing

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

Constrained generation

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

# Outputs valid JSON only

Context size

# 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)

| 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 acceleration (Llama 2-7B Q4_K_M)

| 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) |

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
• L