AWQ vs GPTQ: which quantization format should you use?
Compared on hardware support, runtime, quality and what the 79-model index actually ships.
At a glance
| AWQ | GPTQ | |
|---|---|---|
| Runs on | NVIDIA GPU (CUDA 11.8+) | NVIDIA GPU (CUDA) |
| Runtime | vLLM · AutoAWQ · TGI | auto-gptq · vLLM · TGI |
| Best for | High-throughput API server | Legacy server deployment |
| Adoption estimate | 45% | 28% |
| Models in this index | 53 / 79 | 4 / 79 |
AWQ
Activation-Aware Weight Quantization. High-accuracy INT4 for NVIDIA. Pairs perfectly with vLLM for server deployment.
Strengths
- Best accuracy at 4-bit
- Blazing fast with vLLM
- Excellent batch throughput
Trade-offs
- NVIDIA only
- More setup than GGUF
GPTQ
GPT Quantization — one of the first mainstream post-training methods. Wide framework compatibility; being gradually superseded by AWQ.
Strengths
- Wide compatibility
- Mature ecosystem
- Works with HF transformers
Trade-offs
- Slow quantization process
- Lower accuracy than AWQ
Models that ship both
No model in this index ships both formats, so there is no like-for-like row to show. The comparison above is editorial.