AWQ vs GGUF (Q4_K_M): VRAM, Quality and Speed
Q4_K_M uses about 14% more memory than AWQ / GPTQ 4-bit (4.85 vs 4.25 bits per weight). On Qwen 3 32B with an 8K context that is 21.4 GB versus 19.1 GB. Pick by runtime, not by quality.
VRAM on popular models
Total memory with an 8K context and FP16 KV cache. The last column is the smallest standard VRAM size that holds each version.
| Model | AWQ / GPTQ 4-bit | Q4_K_M | Difference | Smallest tier |
|---|---|---|---|---|
| Llama 3.1 8B | 5.8 GB | 6.3 GB | +0.6 GB | 8 GB / 8 GB |
| Qwen 3.5 9B | 5.8 GB | 6.4 GB | +0.6 GB | 8 GB / 8 GB |
| Qwen 3 14B | 9.4 GB | 10.5 GB | +1.0 GB | 12 GB / 12 GB |
| Mistral Small 3.1 24B | 14.0 GB | 15.6 GB | +1.7 GB | 16 GB / 20 GB |
| Qwen 3.6 27B | 15.0 GB | 16.9 GB | +1.9 GB | 16 GB / 20 GB |
| Gemma 4 31B | 17.7 GB | 19.8 GB | +2.1 GB | 20 GB / 24 GB |
| Qwen 3 32B | 19.1 GB | 21.4 GB | +2.3 GB | 24 GB / 24 GB |
| Qwen 3.6 35B-A3B | 18.5 GB | 21.0 GB | +2.4 GB | 20 GB / 24 GB |
| Llama 3.3 70B | 38.3 GB | 43.2 GB | +4.9 GB | 48 GB / 48 GB |
| Qwen 3.5 122B-A10B | 61.7 GB | 70.2 GB | +8.5 GB | 80 GB / 80 GB |
Largest model each VRAM size can run
The biggest model released since 2025 that fits fully at 8K context.
Context you can fit
Longer contexts need room for the KV cache. The longest context that fits fully at each format, with an FP16 cache:
| Model | GPU | AWQ / GPTQ 4-bit | Q4_K_M |
|---|---|---|---|
| Qwen 3 14B | 16 GB | 40K | 37K |
| Qwen 3.6 27B | 24 GB | 128K | 98K |
| Qwen 3 32B | 24 GB | 21K | 12K |
| Gemma 4 31B | 32 GB | 166K | 139K |
| Llama 3.3 70B | 48 GB | 29K | 14K |
Download size
File sizes in decimal GB, as Hugging Face shows them. Use the GGUF size calculator for any other model.
| Model | AWQ / GPTQ 4-bit | Q4_K_M |
|---|---|---|
| Llama 3.1 8B | 4.3 GB | 4.9 GB |
| Qwen 3 14B | 7.9 GB | 9.0 GB |
| Qwen 3.6 27B | 14.3 GB | 16.3 GB |
| Qwen 3 32B | 17.4 GB | 19.9 GB |
| Llama 3.3 70B | 37.5 GB | 42.8 GB |
Quality
AWQ and GPTQ are 4-bit GPU formats for vLLM, SGLang and Transformers, at about 4.25 bits per weight including scales. GGUF Q4_K_M, used by llama.cpp, Ollama and LM Studio, averages about 4.85 bits because it keeps some tensors at higher precision. Quality is similar; Q4_K_M is often slightly ahead because of those extra bits.
Speed
Text generation reads every weight once per token, so smaller formats generate faster on the same GPU. Estimated on an RTX 4090 24GB:
| Model | AWQ / GPTQ 4-bit | Q4_K_M |
|---|---|---|
| Llama 3.1 8B | ~131 tok/s | ~117 tok/s |
| Qwen 3 32B | ~35 tok/s | ~31 tok/s |
Verdict
Pick by runtime, not by quality. AWQ or GPTQ suits vLLM and batched serving on NVIDIA GPUs, where they are fast and memory-efficient. GGUF suits single-user local use, Macs, AMD and Intel GPUs, and partial CPU offload, which AWQ does not support.
Frequently asked questions
Is AWQ faster than GGUF?
For batched serving in vLLM, usually yes. For a single user on one GPU, llama.cpp with Q4_K_M is competitive, and it is the only one of the two that can offload layers to system RAM.
Can Ollama run AWQ models?
No. Ollama and LM Studio use GGUF (LM Studio also supports MLX on Macs). Use vLLM, SGLang or Transformers for AWQ and GPTQ.
How much more VRAM does Q4_K_M need than AWQ / GPTQ 4-bit?
About 14% more for the weights (4.85 vs 4.25 bits per weight). For Qwen 3 32B with an 8K context that is 21.4 GB versus 19.1 GB.