EXL2 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
| EXL2 | GPTQ | |
|---|---|---|
| Runs on | NVIDIA GPU (Ampere+ recommended) | NVIDIA GPU (CUDA) |
| Runtime | ExLlamaV2 · TabbyAPI | auto-gptq · vLLM · TGI |
| Best for | Max single-GPU performance | Legacy server deployment |
| Adoption estimate | 32% | 28% |
| Models in this index | 21 / 79 | 4 / 79 |
EXL2
ExLlamaV2 format. Mixed-precision per-layer quantization — best accuracy-per-bit ratio. Fastest single-GPU inference available.
Strengths
- Fastest GPU inference
- Best accuracy per bit
- Ultra-low 2bpw option
Trade-offs
- NVIDIA only
- Steeper learning curve
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 Wide framework compatibility; being gradually superseded by AWQ.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.