/optimization-gguf

name: optimization-gguf
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.
license: MIT
metadata:
  version: 1.0.0
  author: Orchestra Research
  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)
• **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

# 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

# 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

# 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

# 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

# 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)

# 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

#!/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

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

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", "mis