GGUF 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
| GGUF | GPTQ | |
|---|---|---|
| Runs on | Any — CPU / NVIDIA / AMD / Apple | NVIDIA GPU (CUDA) |
| Runtime | llama.cpp · Ollama | auto-gptq · vLLM · TGI |
| Best for | Local / edge deployment | Legacy server deployment |
| Adoption estimate | 89% | 28% |
| Models in this index | 79 / 79 | 4 / 79 |
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. Wide framework compatibility; being gradually superseded by AWQ.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
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.