Quick Verdict: AWQ vs GPTQ vs GGUF vs EXL2
AWQ is the default choice for GPU inference in 2026. It loads fastest, integrates natively with vLLM and TGI, and retains quality comparable to full precision. GPTQ remains widely available but is slower to load and shows marginal quality behind AWQ at the same bit width. GGUF dominates CPU and mixed CPU/GPU inference through Ollama and llama.cpp. EXL2 offers the finest bit-width control (2.0 to 8.0 bits per weight) for squeezing large models into limited VRAM. On dedicated GPU hosting, AWQ is the format to standardise on.
Format Characteristics
AWQ (Activation-Aware Weight Quantisation) identifies salient weights based on activation patterns and preserves them at higher precision. This produces consistently better perplexity than naive round-to-nearest quantisation. Most Hugging Face model repositories now provide AWQ variants by default.
GPTQ (Generative Pre-Trained Transformer Quantisation) uses one-shot weight quantisation calibrated on a small dataset. It was the standard GPU quantisation format in 2023-2024 but has been largely superseded by AWQ for new model releases.
GGUF is the llama.cpp native format supporting mixed-precision quantisation, CPU offloading, and memory-mapped loading. It is the only format that runs efficiently on CPU-only or partial GPU systems.
EXL2 is the ExLlamaV2 native format. It supports arbitrary bit widths (e.g., 3.5, 4.25, 5.0 bits per weight), allowing precise VRAM targeting. If a model almost fits at 4-bit but not quite, EXL2 at 3.75 bits may bridge the gap.
Performance Comparison (Llama 3 70B, RTX 6000 Pro 96 GB)
| Metric | AWQ 4-bit | GPTQ 4-bit | GGUF Q4_K_M | EXL2 4.0bpw |
|---|---|---|---|---|
| VRAM Usage | 38 GB | 38 GB | 38 GB (full GPU) | 37 GB |
| Load Time | 12s | 25s | 18s | 15s |
| Throughput (tok/s) | 52 | 48 | 42 | 55 |
| Perplexity (lower=better) | 5.82 | 5.91 | 5.88 | 5.80 |
| vLLM Support | Native | Native | No | No |
| Ollama Support | No | No | Native | No |
| Arbitrary Bit Width | No (4-bit only) | No (2,3,4,8-bit) | Yes (multiple quant types) | Yes (2.0-8.0 bpw) |
Framework Compatibility
Your choice of serving engine dictates your quantisation format. vLLM supports AWQ and GPTQ natively, making them the production standard on dedicated GPU servers. Ollama and llama.cpp require GGUF. ExLlamaV2 uses EXL2 exclusively. Check vLLM vs Ollama to select your engine first, then pick the matching format. See token speed benchmarks for throughput data across configurations.
Quality Retention
At 4-bit quantisation, all formats lose less than 2% quality compared to FP16 on standard benchmarks. AWQ and EXL2 show the smallest degradation because they preserve important weights at higher precision. GPTQ is marginally worse. GGUF quality depends on the specific quantisation variant (Q4_K_M, Q5_K_M, Q6_K). For detailed quality comparisons, see the benchmarks section. At 3-bit and below, quality drops more noticeably, and upgrading GPU VRAM is preferable to aggressive quantisation.
Recommendation
Use AWQ for production GPU inference with vLLM on GigaGPU dedicated servers. Use GGUF if you need CPU offloading or deploy through Ollama. Use EXL2 when you need precise VRAM management on a specific GPU. Avoid GPTQ for new deployments unless a model is only available in that format. Deploy on multi-GPU clusters for larger models and explore LLM hosting guides and private AI hosting for production setups.
