AI Quantization Explained (GGUF vs GPTQ vs AWQ)

Quantization in 2025: Fit Bigger Models on Everyday Hardware

Published on March 12, 2025 • 16 min read

Quantization transforms huge neural networks into compact formats that run locally without $20/month cloud fees. It is the single most important technique for fitting 70B-class intelligence into 8GB–24GB of VRAM. This guide demystifies the three dominant approaches—GGUF, GPTQ, and AWQ—so you can pick the right format for your GPU, workflow, and quality targets.

Table of Contents

1. Quantization Basics
2. GGUF vs GPTQ vs AWQ Overview
3. Quality Impact Benchmarks
4. Hardware Compatibility Matrix
5. Choosing the Right Format
6. Conversion & Testing Workflow
7. FAQ
8. Next Steps

Quantization Basics {#basics}

Quantization reduces model precision from 16-bit floating point to lower bit widths (typically 4–8 bits). This:

• Shrinks file size by 2–4×, letting 70B models fit on consumer GPUs.
• Decreases memory bandwidth requirements, increasing tokens per second.
• Introduces small rounding error—quality depends on calibration and rounding strategies.

Key principle: Lower bits = smaller models + faster inference, but also more approximation error. The art of quantization is controlling that error.

Bit Depth Cheatsheet

GGUF vs GPTQ vs AWQ Overview {#format-overview}

Quality Impact Benchmarks {#quality-benchmarks}

We measured accuracy vs original weights using our evaluation suite (MMLU, GSM8K, HumanEval).

Hardware Compatibility Matrix {#hardware-compatibility}

Choosing the Right Format {#choosing-format}

Use this quick decision tree:

1. Need universal compatibility? → Choose GGUF.
2. Prioritize raw throughput on NVIDIA GPUs? → Use GPTQ (or ExLlama v2).
3. Care about creative writing or coding fidelity? → Deploy AWQ.
4. Still unsure? Download both GGUF and AWQ, run a 10-prompt eval, and compare latency + quality.

ollama run llama3.1:8b-q4_k_m <<'PROMPT'
Explain vector databases in 3 bullet points.
PROMPT
ollama run llama3.1:8b-awq <<'PROMPT'
Write Python code that adds streaming to FastAPI.
PROMPT

Conversion & Testing Workflow {#conversion-workflow}

1. Download the original safetensors or GGUF model.
2. Run calibration prompts (10–50) using high-quality datasets matching your use case.
3. Quantize using the appropriate tool:
   • python convert.py --format gguf --bits 4
   • python gptq.py --bits 4 --act-order
   • python awq.py --wbits 4 --true-sequential
4. Validate outputs with your evaluation template above.
5. Store both quantized model and calibration metadata for future retraining.

Tip: Keep a notebook or Git repo with evaluation scores and hardware notes so you can compare quantizations across GPUs.

FAQ {#faq}

• What quantization should I use for daily chat? GGUF Q4_K_M is the best balance of fidelity and efficiency for 8GB–16GB rigs.
• Does GPTQ still matter? Yes, when you run CUDA-only inference servers or need ExLlama throughput.
• When should I pick AWQ? Choose AWQ for coding/creative assistants where coherence matters slightly more than raw speed.

Next Steps {#next-steps}

• Ready to deploy? Compare compatible GPUs in our 2025 hardware guide.
• Need models already quantized? Browse the models directory with GGUF and GPTQ filters.
• Want lightweight defaults? Start with our 8GB RAM recommendations.