GGUF vs AWQ: which quantization format should you use?
Compared on hardware support, runtime, quality and what the 79-model index actually ships.
At a glance
| GGUF | AWQ | |
|---|---|---|
| Runs on | Any — CPU / NVIDIA / AMD / Apple | NVIDIA GPU (CUDA 11.8+) |
| Runtime | llama.cpp · Ollama | vLLM · AutoAWQ · TGI |
| Best for | Local / edge deployment | High-throughput API server |
| Adoption estimate | 89% | 45% |
| Models in this index | 79 / 79 | 53 / 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
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
Models that ship both
These 53 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 | AWQ Level | Quality loss |
|---|---|---|---|---|
| Llama 3.1 405B Instruct | Q4_K_M | 2.3% | AWQ INT4 | 3.5% |
| Qwen3 235B-A22B Instruct | Q4_K_M | 2.2% | AWQ INT4 | 3.2% |
| Llama 4 Scout 17B (16E) | Q4_K_M | 2.4% | AWQ INT4 | 3.2% |
| Qwen2.5 72B Instruct | Q5_K_M | 1.1% | AWQ INT4 | 3.5% |
| Llama 3.1 70B Instruct | Q5_K_M | 1.2% | AWQ INT4 | 3.9% |
| Llama 3.3 70B Instruct | Q5_K_M | 1% | AWQ INT4 | 3.7% |
| DeepSeek-R1-Distill-Llama-70B | Q4_K_M | 2.4% | AWQ INT4 | 3.5% |
| Mixtral 8x7B Instruct | Q4_K_M | 2.8% | AWQ INT4 | 3.8% |
| Seed-OSS 36B Instruct | Q5_K_M | 1.3% | AWQ INT4 | 3.8% |
| Yi 1.5 34B Chat | Q4_K_M | 2.8% | AWQ INT4 | 4% |
| Qwen3 32B Instruct | Q4_K_M | 2.5% | AWQ INT4 | 3.6% |
| Qwen2.5-Coder 32B Instruct | Q4_K_M | 2.5% | AWQ INT4 | 3.5% |
| Qwen3 30B-A3B Instruct | Q5_K_M | 1.1% | AWQ INT4 | 3.4% |
| Qwen3-Coder 30B-A3B Instruct | Q5_K_M | 1% | AWQ INT4 | 3.2% |
| Qwen3-VL 30B-A3B Instruct | Q8_0 | 0.3% | AWQ INT4 | 3.7% |
| Gemma 3 27B IT | Q4_K_M | 2.8% | AWQ INT4 | 3.8% |
| Gemma 2 27B Instruct | Q5_K_M | 1.3% | AWQ INT4 | 4% |
| Mistral Small 24B Instruct | Q4_K_M | 2.9% | AWQ INT4 | 3.8% |
| Devstral Small 1.1 24B | Q6_K | 0.6% | AWQ INT4 | 4% |
| Magistral Small 1.2 24B | Q6_K | 0.6% | AWQ INT4 | 4% |
Quality loss is perplexity increase against the unquantized weights — lower is better, and only comparable within one model.