Qwen 2.5 32B is the strongest model that reliably fits in a single 24 GB consumer GPU at Q4_K_M. It outperforms Llama 3.1 70B on Chinese benchmarks and is competitive on English reasoning. Combined with the Apache 2.0 license, it's the go-to model for prosumer workstation deployments.
Qwen 2.5 32B needs at minimum 19.4 GB of VRAM at Q4_K_M quantization (70.4 GB at FP16). The cheapest GPU that comfortably fits with KV-cache headroom is the Apple Mac mini (M4, 32 GB) (32 GB VRAM, $999 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 | 70.4 GB | Reference | Training-precision weights |
| Q8_0 | 35.2 GB | Near-lossless | 8-bit, ~0.1% perplexity hit |
| Q6_K | 28.5 GB | Very high | 6-bit, near Q8 quality |
| Q5_K_M | 24.3 GB | High | Strong middle ground |
| Q4_K_M | 19.4 GB | Balanced (recommended) | Default for local deployments |
| Q4_0 | 19.8 GB | Legacy 4-bit | Older GGUF, kept for compatibility |
| Q3_K_M | 15.1 GB | Lossy | When VRAM is very tight |
| Q2_K | 11.3 GB | Extreme | Rescue option, quality degrades visibly |
Filtered from MyAIHardware's GPU database. FP16 needs 70.4 GB; Q4_K_M needs 19.4 GB.
We don't have community benchmarks for this exact model yet.
Official weights + popular runtime tags.
ollama pull qwen2.5:32blms get Qwen/Qwen2.5-32B-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-2-5-32b-2024,
title={Qwen 2.5 32B},
author={Alibaba Cloud Qwen Team},
year={2024},
url={https://huggingface.co/Qwen/Qwen2.5-32B-Instruct}
}Alibaba Cloud Qwen Team (2024). Qwen 2.5 32B [Model card]. Hugging Face. https://huggingface.co/Qwen/Qwen2.5-32B-Instruct
Qwen 2.5 32B (Qwen 2.5, Alibaba Cloud Qwen Team, 2024). Available at https://huggingface.co/Qwen/Qwen2.5-32B-Instruct.