GGUF vs GPTQ: which quantization format should you use?
Compared on hardware support, runtime, quality and what the 110-model index actually ships.
At a glance
| GGUF | GPTQ | |
|---|---|---|
| Runs on | Any — CPU / NVIDIA / AMD / Apple | NVIDIA GPU (CUDA) or AMD via vLLM ROCm |
| Runtime | llama.cpp · Ollama | GPTQModel · vLLM · TGI |
| Best for | Local / edge deployment | Legacy server deployment |
| Models in this index | 110 / 110 | 4 / 110 |
GGUF
The most versatile format. CPU, GPU, Apple Silicon — runs everywhere. Supports hybrid inference splitting weights across RAM and VRAM.
Strengths
- Any hardware
- CPU+GPU hybrid
- Huge ecosystem
- Beginner-friendly
Trade-offs
- Slower than GPU-native
- Not ideal for high concurrency
GPTQ
GPT Quantization, one of the first mainstream post-training methods, with wide framework compatibility. AutoGPTQ, its original tooling, is unmaintained, and its README points to GPTQModel. No model in this index ships both a GPTQ and an AWQ build, so this site has no paired figure to say which loses less quality.
Strengths
- Wide compatibility
- Mature ecosystem
- Works with HF transformers
Trade-offs
- Slow quantization process
- AutoGPTQ unmaintained (GPTQModel replaces it)
Models that ship both
These 4 models publish weights in both formats, so the two rows describe the same model rather than two different ones — the only place this comparison is measurable rather than editorial.
| Model | GGUF Level | Quality loss | GPTQ Level | Quality loss |
|---|---|---|---|---|
| Command R 35B | Q4_K_M | 3% | GPTQ INT4 | 4.5% |
| StarCoder2 15B | Q4_K_M | 3.2% | GPTQ INT4 | 4.8% |
| Falcon 3 10B Instruct | Q4_K_M | 3.1% | GPTQ INT4 | 4.8% |
| Granite 3.1 8B Instruct | Q4_K_M | 3% | GPTQ INT4 | 4.5% |
Quality loss is perplexity increase against the unquantized weights — lower is better, and only comparable within one model.
AWQ vs GPTQ, EXL2 vs GPTQ
Neither comparison has anything to compare. Of the 110 models in this index, 4 ship in GPTQ — and not one of them also ships in AWQ or EXL2. You are never really choosing between GPTQ and AWQ or EXL2 for a given model; you are choosing between the model that has a GPTQ build and a different model that has the other one.
If you came here for a verdict anyway: AWQ, on both counts. It quantizes with the activation distribution in hand, protecting the small fraction of weight channels that matter most, and it is a first-class citizen in vLLM and TGI — which is where the throughput case for a server format is actually made. EXL2 is the other live option, and its variable bit-width lets you spend bits where a layer needs them instead of applying one level everywhere; it wins on a single NVIDIA GPU with ExLlamaV2. GPTQ came first and still works, but it is maintained rather than developed, and this index reflects that.