Qwen 2.5 72B is Alibaba's flagship dense model, neck-and-neck with Llama 3.1 70B and pulling ahead on Chinese, math, and coding. VRAM footprint mirrors Llama 70B (~158 GB FP16, ~44 GB Q4_K_M). Note the license is Qwen-specific, not Apache, which limits some commercial uses above 100M MAU.
Qwen 2.5 72B needs at minimum 43.6 GB of VRAM at Q4_K_M quantization (158.4 GB at FP16). The cheapest GPU that comfortably fits with KV-cache headroom is the Apple Mac mini (M4 Pro, 64 GB) (64 GB VRAM, $2,199 MSRP). Community benchmark submissions are open. This model fits a single consumer GPU under 48 GB, so a one-card build works.
Weights only. Add ~20-30% for KV cache at typical context lengths.
| Quant | VRAM | Quality | Notes |
|---|---|---|---|
| FP16 | 158.4 GB | Reference | Training-precision weights |
| Q8_0 | 79.2 GB | Near-lossless | 8-bit, ~0.1% perplexity hit |
| Q6_K | 64.2 GB | Very high | 6-bit, near Q8 quality |
| Q5_K_M | 54.6 GB | High | Strong middle ground |
| Q4_K_M | 43.6 GB | Balanced (recommended) | Default for local deployments |
| Q4_0 | 44.6 GB | Legacy 4-bit | Older GGUF, kept for compatibility |
| Q3_K_M | 34.1 GB | Lossy | When VRAM is very tight |
| Q2_K | 25.3 GB | Extreme | Rescue option, quality degrades visibly |
Filtered from MyAIHardware's GPU database. FP16 needs 158.4 GB; Q4_K_M needs 43.6 GB.
We don't have community benchmarks for this exact model yet.
Official weights + popular runtime tags.
ollama pull qwen2.5:72blms get Qwen/Qwen2.5-72B-Instruct-GGUFStep-by-step guides that use this model.
Models in a similar size or capability class.
Use these in papers, blog posts, or internal docs.
@misc{qwen-2-5-72b-2024,
title={Qwen 2.5 72B},
author={Alibaba Cloud Qwen Team},
year={2024},
url={https://huggingface.co/Qwen/Qwen2.5-72B-Instruct}
}Alibaba Cloud Qwen Team (2024). Qwen 2.5 72B [Model card]. Hugging Face. https://huggingface.co/Qwen/Qwen2.5-72B-Instruct
Qwen 2.5 72B (Qwen 2.5, Alibaba Cloud Qwen Team, 2024). Available at https://huggingface.co/Qwen/Qwen2.5-72B-Instruct.