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GGUF 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.
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GGUF 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. 📦 Ships with claude-code-templates
</> SKILL.md
optimization-gguf.SKILL.md
---
name : gguf-quantization
description : GGUF 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.
version : 1.0.0
author : Orchestra Research
license : MIT
tags : [GGUF, Quantization, llama.cpp, CPU Inference, Apple Silicon, Model Compression, Optimization]
dependencies : [llama-cpp-python>=0.2.0]
---
# GGUF - Quantization Format for llama.cpp
The GGUF (GPT-Generated Unified Format) is the standard file format for llama.cpp, enabling efficient inference on CPUs, Apple Silicon, and GPUs with flexible quantization options.
## When to use GGUF
**Use GGUF when:**
- Deploying on consumer hardware (laptops, desktops)
- Running on Apple Silicon (M1/M2/M3) with Metal acceleration
- Need CPU inference without GPU requirements
- Want flexible quantization (Q2_K to Q8_0)
- Using local AI tools (LM Studio, Ollama, text-generation-webui)
**Key advantages:**
- **Universal hardware**: CPU, Apple Silicon, NVIDIA, AMD support
- **No Python runtime**: Pure C/C++ inference
- **Flexible quantization**: 2-8 bit with various methods (K-quants)
Flowy AI Flows that just work. Hand-picked Claude Code plugins that fire the right skill as you prompt.
- **Ecosystem support**: LM Studio, Ollama, koboldcpp, and more
- **imatrix**: Importance matrix for better low-bit quality
**Use alternatives instead:**
- **AWQ/GPTQ**: Maximum accuracy with calibration on NVIDIA GPUs
- **HQQ**: Fast calibration-free quantization for HuggingFace
- **bitsandbytes**: Simple integration with transformers library
- **TensorRT-LLM**: Production NVIDIA deployment with maximum speed
## Quick start
### Installation
```bash
# Clone llama.cpp
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
# Build (CPU)
make
# Build with CUDA (NVIDIA)
make GGML_CUDA=1
# Build with Metal (Apple Silicon)
make GGML_METAL=1
# Install Python bindings (optional)
pip install llama-cpp-python
```
### Convert model to GGUF
```bash
# Install requirements
pip install -r requirements.txt
# Convert HuggingFace model to GGUF (FP16)
python convert_hf_to_gguf.py ./path/to/model --outfile model-f16.gguf
# Or specify output type
python convert_hf_to_gguf.py ./path/to/model \
--outfile model-f16.gguf \
--outtype f16
```
### Quantize model
```bash
# Basic quantization to Q4_K_M
./llama-quantize model-f16.gguf model-q4_k_m.gguf Q4_K_M
# Quantize with importance matrix (better quality)
./llama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix
./llama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M
```
### Run inference
```bash
# CLI inference
./llama-cli -m model-q4_k_m.gguf -p "Hello, how are you?"
# Interactive mode
./llama-cli -m model-q4_k_m.gguf --interactive
# With GPU offload
./llama-cli -m model-q4_k_m.gguf -ngl 35 -p "Hello!"
```
## Quantization types
### K-quant methods (recommended)
| Type | Bits | Size (7B) | Quality | Use Case |
|------|------|-----------|---------|----------|
| Q2_K | 2.5 | ~2.8 GB | Low | Extreme compression |
| Q3_K_S | 3.0 | ~3.0 GB | Low-Med | Memory constrained |
| Q3_K_M | 3.3 | ~3.3 GB | Medium | Balance |
| Q4_K_S | 4.0 | ~3.8 GB | Med-High | Good balance |
| Q4_K_M | 4.5 | ~4.1 GB | High | **Recommended default** |
| Q5_K_S | 5.0 | ~4.6 GB | High | Quality focused |
| Q5_K_M | 5.5 | ~4.8 GB | Very High | High quality |
| Q6_K | 6.0 | ~5.5 GB | Excellent | Near-original |
| Q8_0 | 8.0 | ~7.2 GB | Best | Maximum quality |
### Legacy methods
| Type | Description |
|------|-------------|
| Q4_0 | 4-bit, basic |
| Q4_1 | 4-bit with delta |
| Q5_0 | 5-bit, basic |
| Q5_1 | 5-bit with delta |
**Recommendation**: Use K-quant methods (Q4_K_M, Q5_K_M) for best quality/size ratio.
## Conversion workflows
### Workflow 1: HuggingFace to GGUF
```bash
# 1. Download model
huggingface-cli download meta-llama/Llama-3.1-8B --local-dir ./llama-3.1-8b
# 2. Convert to GGUF (FP16)
python convert_hf_to_gguf.py ./llama-3.1-8b \
--outfile llama-3.1-8b-f16.gguf \
--outtype f16
# 3. Quantize
./llama-quantize llama-3.1-8b-f16.gguf llama-3.1-8b-q4_k_m.gguf Q4_K_M
# 4. Test
./llama-cli -m llama-3.1-8b-q4_k_m.gguf -p "Hello!" -n 50
```
### Workflow 2: With importance matrix (better quality)
```bash
# 1. Convert to GGUF
python convert_hf_to_gguf.py ./model --outfile model-f16.gguf
# 2. Create calibration text (diverse samples)
cat > calibration.txt << 'EOF'
The quick brown fox jumps over the lazy dog.
Machine learning is a subset of artificial intelligence.
Python is a popular programming language.
# Add more diverse text samples...
EOF
# 3. Generate importance matrix
./llama-imatrix -m model-f16.gguf \
-f calibration.txt \
--chunk 512 \
-o model.imatrix \
-ngl 35 # GPU layers if available
# 4. Quantize with imatrix
./llama-quantize --imatrix model.imatrix \
model-f16.gguf \
model-q4_k_m.gguf \
Q4_K_M
```
### Workflow 3: Multiple quantizations
```bash
#!/bin/bash
MODEL="llama-3.1-8b-f16.gguf"
IMATRIX="llama-3.1-8b.imatrix"
# Generate imatrix once
./llama-imatrix -m $MODEL -f wiki.txt -o $IMATRIX -ngl 35
# Create multiple quantizations
for QUANT in Q4_K_M Q5_K_M Q6_K Q8_0; do
OUTPUT="llama-3.1-8b-${QUANT,,}.gguf"
./llama-quantize --imatrix $IMATRIX $MODEL $OUTPUT $QUANT
echo "Created: $OUTPUT ($(du -h $OUTPUT | cut -f1))"
done
```
## Python usage
### llama-cpp-python
```python
from llama_cpp import Llama
# Load model
llm = Llama(
model_path="./model-q4_k_m.gguf",
n_ctx=4096, # Context window
n_gpu_layers=35, # GPU offload (0 for CPU only)
n_threads=8 # CPU threads
)
# Generate
output = llm(
"What is machine learning?",
max_tokens=256,
temperature=0.7,
stop=["</s>", "\n\n"]
)
print(output["choices"][0]["text"])
```
### Chat completion
```python
from llama_cpp import Llama
llm = Llama(
model_path="./model-q4_k_m.gguf",
n_ctx=4096,
n_gpu_layers=35,
chat_format="llama-3" # Or "chatml", "mistral", etc.
)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is Python?"}
]
response = llm.create_chat_completion(
messages=messages,
max_tokens=256,
temperature=0.7
)
print(response["choices"][0]["message"]["content"])
```
### Streaming
```python
from llama_cpp import Llama
llm = Llama(model_path="./model-q4_k_m.gguf", n_gpu_layers=35)
# Stream tokens
for chunk in llm(
"Explain quantum computing:",
max_tokens=256,
stream=True
):
print(chunk["choices"][0]["text"], end="", flush=True)
```
## Server mode
### Start OpenAI-compatible server
```bash
# Start server
./llama-server -m model-q4_k_m.gguf \
--host 0.0.0.0 \
--port 8080 \
-ngl 35 \
-c 4096
# Or with Python bindings
python -m llama_cpp.server \
--model model-q4_k_m.gguf \
--n_gpu_layers 35 \
--host 0.0.0.0 \
--port 8080
```
### Use with OpenAI client
```python
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8080/v1",
api_key="not-needed"
)
response = client.chat.completions.create(
model="local-model",
messages=[{"role": "user", "content": "Hello!"}],
max_tokens=256
)
print(response.choices[0].message.content)
```
## Hardware optimization
### Apple Silicon (Metal)
```bash
# Build with Metal
make clean && make GGML_METAL=1
# Run with Metal acceleration
./llama-cli -m model.gguf -ngl 99 -p "Hello"
# Python with Metal
llm = Llama(
model_path="model.gguf",
n_gpu_layers=99, # Offload all layers
n_threads=1 # Metal handles parallelism
)
```
### NVIDIA CUDA
```bash
# Build with CUDA
make clean && make GGML_CUDA=1
# Run with CUDA
./llama-cli -m model.gguf -ngl 35 -p "Hello"
# Specify GPU
CUDA_VISIBLE_DEVICES=0 ./llama-cli -m model.gguf -ngl 35
```
### CPU optimization
```bash
# Build with AVX2/AVX512
make clean && make
# Run with optimal threads
./llama-cli -m model.gguf -t 8 -p "Hello"
# Python CPU config
llm = Llama(
model_path="model.gguf",
n_gpu_layers=0, # CPU only
n_threads=8, # Match physical cores
n_batch=512 # Batch size for prompt processing
)
```
## Integration with tools
### Ollama
```bash
# Create Modelfile
cat > Modelfile << 'EOF'
FROM ./model-q4_k_m.gguf
TEMPLATE """{{ .System }}
{{ .Prompt }}"""
PARAMETER temperature 0.7
PARAMETER num_ctx 4096
EOF
# Create Ollama model
ollama create mymodel -f Modelfile
# Run
ollama run mymodel "Hello!"
```
### LM Studio
1. Place GGUF file in `~/.cache/lm-studio/models/`
2. Open LM Studio and select the model
3. Configure context length and GPU offload
4. Start inference
### text-generation-webui
```bash
# Place in models folder
cp model-q4_k_m.gguf text-generation-webui/models/
# Start with llama.cpp loader
python server.py --model model-q4_k_m.gguf --loader llama.cpp --n-gpu-layers 35
```
## Best practices
1. **Use K-quants**: Q4_K_M offers best quality/size balance
2. **Use imatrix**: Always use importance matrix for Q4 and below
3. **GPU offload**: Offload as many layers as VRAM allows
4. **Context length**: Start with 4096, increase if needed
5. **Thread count**: Match physical CPU cores, not logical
6. **Batch size**: Increase n_batch for faster prompt processing
## Common issues
**Model loads slowly:**
```bash
# Use mmap for faster loading
./llama-cli -m model.gguf --mmap
```
**Out of memory:**
```bash
# Reduce GPU layers
./llama-cli -m model.gguf -ngl 20 # Reduce from 35
# Or use smaller quantization
./llama-quantize model-f16.gguf model-q3_k_m.gguf Q3_K_M
```
**Poor quality at low bits:**
```bash
# Always use imatrix for Q4 and below
./llama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix
./llama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M
```
## References
- **[Advanced Usage](references/advanced-usage.md)** - Batching, speculative decoding, custom builds
- **[Troubleshooting](references/troubleshooting.md)** - Common issues, debugging, benchmarks
## Resources
- **Repository**: https://github.com/ggml-org/llama.cpp
- **Python Bindings**: https://github.com/abetlen/llama-cpp-python
- **Pre-quantized Models**: https://huggingface.co/TheBloke
- **GGUF Converter**: https://huggingface.co/spaces/ggml-org/gguf-my-repo
- **License**: MIT
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