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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.

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