Qwen 2.5 14B sits in the 'serious assistant' sweet spot — clearly better than 7B on multi-step reasoning while still fitting comfortably in 16 GB VRAM at Q4_K_M. It's a good choice for a single RTX 4070 Ti Super or 4080 build. Trade-off vs 7B is roughly 2x slower tok/s.
Qwen 2.5 14B needs at minimum 8.5 GB of VRAM at Q4_K_M quantization (30.8 GB at FP16). The cheapest GPU that comfortably fits with KV-cache headroom is the Intel Arc A580 12GB (variant) [VERIFY] (12 GB VRAM, $219 MSRP). Measured throughput hits 1080 tok/s on 8× NVIDIA H100 SXM5 80GB (DGX H100). 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 | 30.8 GB | Reference | Training-precision weights |
| Q8_0 | 15.4 GB | Near-lossless | 8-bit, ~0.1% perplexity hit |
| Q6_K | 12.5 GB | Very high | 6-bit, near Q8 quality |
| Q5_K_M | 10.6 GB | High | Strong middle ground |
| Q4_K_M | 8.5 GB | Balanced (recommended) | Default for local deployments |
| Q4_0 | 8.7 GB | Legacy 4-bit | Older GGUF, kept for compatibility |
| Q3_K_M | 6.6 GB | Lossy | When VRAM is very tight |
| Q2_K | 4.9 GB | Extreme | Rescue option, quality degrades visibly |
Filtered from MyAIHardware's GPU database. FP16 needs 30.8 GB; Q4_K_M needs 8.5 GB.
44 measurement(s) for this model from MyAIHardware's benchmark database.
| Device | Speed | Quant | Context | Power |
|---|---|---|---|---|
| 8× NVIDIA H100 SXM5 80GB (DGX H100) | 1080 tok/s | Q4_K_M | 8192 | 5600W |
| Groq LPU (8-chip rack) | 480 tok/s | FP16 | 8192 | 1720W |
| NVIDIA B200 192GB | 285 tok/s | Q4_K_M | 8192 | 1000W |
| NVIDIA B200 192GB | 268 tok/s | Q4_K_M | 8192 | 1000W |
| Google TPU v5p (Trillium) | 195 tok/s | INT8 | 8192 | 300W |
| NVIDIA H200 141GB | 178 tok/s | Q4_K_M | 8192 | 700W |
| NVIDIA H100 SXM5 80GB | 168 tok/s | Q4_K_M | 8192 | 700W |
| NVIDIA H100 SXM5 80GB | 165 tok/s | Q4_K_M | 8192 | 700W |
| AWS Trainium2 Trn2 | 158 tok/s | INT8 | 8192 | 350W |
| NVIDIA H100 SXM5 80GB | 145 tok/s | Q4_K_M | 8192 | 700W |
| AMD Instinct MI300X 192GB | 142 tok/s | Q4_K_M | 8192 | 750W |
| AMD Instinct MI300X 192GB | 142 tok/s | Q4_K_M | 8192 | 750W |
| AMD Instinct MI300X 192GB | 128 tok/s | Q4_K_M | 8192 | 750W |
| NVIDIA GeForce RTX 5090 32GB | 115 tok/s | Q4_K_M | 8192 | 575W |
| NVIDIA A100 SXM4 80GB | 108 tok/s | Q4_K_M | 8192 | 400W |
| NVIDIA GeForce RTX 5090 32GB | 105 tok/s | Q4_K_M | 8192 | 575W |
| NVIDIA GeForce RTX 5090 32GB | 92 tok/s | Q4_K_M | 8192 | 575W |
| NVIDIA L40S 48GB | 92 tok/s | Q4_K_M | 8192 | 350W |
| 2× NVIDIA RTX 4090 24GB | 92 tok/s | Q4_K_M | 8192 | 900W |
| NVIDIA GeForce RTX 5080 16GB | 86 tok/s | Q4_K_M | 8192 | 360W |
Official weights + popular runtime tags.
ollama pull qwen2.5:14blms get Qwen/Qwen2.5-14B-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-2-5-14b-2024,
title={Qwen 2.5 14B},
author={Alibaba Cloud Qwen Team},
year={2024},
url={https://huggingface.co/Qwen/Qwen2.5-14B-Instruct}
}Alibaba Cloud Qwen Team (2024). Qwen 2.5 14B [Model card]. Hugging Face. https://huggingface.co/Qwen/Qwen2.5-14B-Instruct
Qwen 2.5 14B (Qwen 2.5, Alibaba Cloud Qwen Team, 2024). Available at https://huggingface.co/Qwen/Qwen2.5-14B-Instruct.