Home / Knowledge Base / Quantization
Implementation Guide

LLM Quantization: GPTQ vs AWQ vs GGUF

16 min read
Jan 2, 2026
Quantization is how you fit bigger models into smaller hardware budgets. But the win is not always “free”: you trade memory for accuracy, stability, and sometimes throughput. This guide helps you pick the right approach for production.
Quantization VRAM Serving On-Prem

What quantization changes (and what it doesn’t)

  • Weights: most quantization methods reduce weight memory. This is the main win.
  • KV cache: often remains FP16/FP8 and can become the bottleneck at long context + high concurrency.
  • Latency: can improve, stay flat, or even regress depending on kernels and batching.
  • Quality: the risk is domain-dependent; measure on your golden set.

GPTQ (weight-only, post-training)

GPTQ is a popular approach for compressing weights to 4-bit with good quality retention. It is widely used for on-GPU weight-only inference.

  • Good for: serving on GPUs with limited VRAM when you want to keep model capacity.
  • Watch out: outliers and domain-specific tokens can degrade accuracy.

AWQ (activation-aware)

AWQ optimizes quantization with awareness of activation distributions, often improving quality at the same bit-width.

  • Good for: high-quality 4-bit deployments, especially when you can use optimized kernels.
  • Watch out: “paper wins” don’t always translate to your runtime; measure throughput and p95.

GGUF (llama.cpp ecosystem)

GGUF is a model file format (and tooling ecosystem) widely used in CPU-first and edge deployments. It also supports many quantization variants.

  • Good for: CPU deployments, laptop/offline use, and environments where GPU availability is constrained.
  • Watch out: CPU throughput can be insufficient for enterprise concurrency unless carefully scoped.

Decision matrix

Your constraint Most likely fit Why
GPU VRAM is tight AWQ / GPTQ Weight memory drops while keeping GPU execution.
CPU-first / offline GGUF Optimized for llama.cpp runtimes and portability.
Long context + concurrency Quantization + KV strategy KV cache becomes dominant; measure memory per request.
Regulated outputs Conservative quantization Prefer higher precision if error cost is high.

Production checklist

  • Measure on your golden set: accuracy, groundedness, refusal rate.
  • Measure performance: TTFT, p95, tok/s, concurrency saturation.
  • Track memory: weights vs KV cache; validate worst-case context length.
  • Keep a rollback: switch back to higher precision if regressions appear.

Related Articles

vLLM vs TensorRT‑LLM

Serving trade-offs and benchmarks

LLM Observability Stack

What to track in production
Executive brief

LLM Quantization: GPTQ vs AWQ vs GGUF

Antonio Brundo
Jan 02, 2026
16 min read

Quantization decisions: GPU vs CPU paths, quality trade-offs, and how to benchmark GPTQ/AWQ/GGUF for your latency and cost targets.

Want the full technical deep dive?

This page includes an executive brief in your language. Switch to English to read the full technical version with implementation details.

Read full technical version (EN) Request Strategic Assessment

Key takeaways

  • Quantization cuts cost but can degrade quality; match method to deployment target.
  • GPTQ/AWQ are GPU-oriented; GGUF is great for CPU/edge constraints.
  • Decide by latency target, VRAM budget, model size, and acceptable quality loss.
  • Always benchmark on your eval set and keep rollback paths.

30-day plan

  • Pick candidate models and define baseline quality + SLOs (TTFT, p95).
  • Generate quant variants and benchmark latency, throughput, and memory.
  • Run regression/eval with real prompts; validate safety and numeric accuracy.
  • Roll out gradually with guardrails and observability, then iterate.