Table of Contents
Qwen VRAM Requirements Overview
Alibaba’s Qwen family has grown into one of the most comprehensive open-weight model lineups, spanning from the tiny 0.5B to the full 72B, plus vision and code variants. This guide covers VRAM requirements for every Qwen model to help you select the right dedicated GPU server for Qwen hosting.
Qwen2.5 models use grouped-query attention and support context lengths up to 128K tokens for some variants. The models are particularly strong for multilingual tasks (especially Chinese and English) and competitive with LLaMA 3 and Mistral at equivalent sizes.
Complete VRAM Table (All Models)
Qwen2.5 Text Models
| Model | Parameters | FP32 | FP16 | INT8 | INT4 |
|---|---|---|---|---|---|
| Qwen2.5 0.5B | 0.5B | ~2 GB | ~1 GB | ~0.6 GB | ~0.4 GB |
| Qwen2.5 1.5B | 1.5B | ~6 GB | ~3 GB | ~1.6 GB | ~1 GB |
| Qwen2.5 3B | 3B | ~12 GB | ~6 GB | ~3.2 GB | ~2 GB |
| Qwen2.5 7B | 7.6B | ~30 GB | ~15 GB | ~8 GB | ~5 GB |
| Qwen2.5 14B | 14.8B | ~59 GB | ~30 GB | ~15 GB | ~9.5 GB |
| Qwen2.5 32B | 32.5B | ~130 GB | ~65 GB | ~33 GB | ~20 GB |
| Qwen2.5 72B | 72.7B | ~291 GB | ~145 GB | ~73 GB | ~39 GB |
Qwen2.5 Coder and Vision Models
| Model | Parameters | FP16 | INT8 | INT4 |
|---|---|---|---|---|
| Qwen2.5-Coder 1.5B | 1.5B | ~3 GB | ~1.6 GB | ~1 GB |
| Qwen2.5-Coder 7B | 7.6B | ~15 GB | ~8 GB | ~5 GB |
| Qwen2.5-Coder 14B | 14.8B | ~30 GB | ~15 GB | ~9.5 GB |
| Qwen2.5-Coder 32B | 32.5B | ~65 GB | ~33 GB | ~20 GB |
| Qwen2-VL 2B | 2.2B | ~5 GB | ~3 GB | ~2 GB |
| Qwen2-VL 7B | 8.3B | ~17 GB | ~9 GB | ~5.5 GB |
| Qwen2-VL 72B | 73.4B | ~148 GB | ~74 GB | ~40 GB |
Vision-language models (Qwen2-VL) require slightly more VRAM than text-only equivalents due to the vision encoder. For comparison with other model families, see our LLaMA 3 VRAM requirements and DeepSeek VRAM requirements pages.
Which GPU Do You Need?
| GPU | VRAM | Best Qwen Model | Precision | Use Case |
|---|---|---|---|---|
| RTX 3050 | 8 GB | Qwen2.5 7B | 4-bit | Dev / testing |
| RTX 4060 | 8 GB | Qwen2.5 7B | 4-bit / INT8 | Dev / personal |
| RTX 4060 Ti | 16 GB | Qwen2.5 7B / 14B | FP16 / 4-bit | Small production |
| RTX 3090 | 24 GB | Qwen2.5 14B / 32B | FP16 / 4-bit | Production |
| 2x RTX 3090 | 48 GB | Qwen2.5 32B / 72B | FP16 / 4-bit | High quality |
Context Length Impact on VRAM
Qwen2.5 models support up to 128K context, but KV cache grows substantially:
| Context | 7B KV Cache | 14B KV Cache | 32B KV Cache | 72B KV Cache |
|---|---|---|---|---|
| 4,096 | ~0.5 GB | ~1 GB | ~2 GB | ~3 GB |
| 8,192 | ~1 GB | ~2 GB | ~4 GB | ~6 GB |
| 32,768 | ~4 GB | ~8 GB | ~16 GB | ~24 GB |
| 131,072 | ~16 GB | ~32 GB | ~64 GB | ~96 GB |
At 128K context, the KV cache alone for Qwen2.5 72B exceeds what most GPU setups can handle. In practice, use 4K-32K context unless you have substantial VRAM headroom.
Batch Size Impact on VRAM
| Model (4-bit, 4K ctx) | Batch 1 | Batch 4 | Batch 8 | Batch 16 |
|---|---|---|---|---|
| Qwen2.5 7B | ~5.5 GB | ~7.5 GB | ~9.5 GB | ~13.5 GB |
| Qwen2.5 14B | ~10.5 GB | ~14.5 GB | ~18.5 GB | ~26.5 GB |
| Qwen2.5 32B | ~22 GB | ~30 GB | ~38 GB | ~54 GB |
Qwen2.5 7B is very batch-friendly at 4-bit quantization, handling 16 concurrent requests within 14 GB. This makes it excellent for production APIs on mid-range GPUs.
Practical Deployment Recommendations
- Personal/dev: Qwen2.5 7B on RTX 4060 (4-bit). 20-25 tok/s, great for testing and prototyping.
- Small production: Qwen2.5 14B on RTX 3090 (4-bit). Strong performance across benchmarks at 25-30 tok/s.
- Code generation: Qwen2.5-Coder 7B or 14B on RTX 3090. Competitive with CodeLlama and StarCoder. See our code model hosting page.
- Vision tasks: Qwen2-VL 7B on RTX 4060 Ti or RTX 3090. See our vision model hosting page.
- Maximum quality: Qwen2.5 72B on multi-GPU clusters. Competes with LLaMA 3 70B.
Compare costs using our cost per 1M tokens analysis and the LLM cost calculator.
Quick Setup Commands
Ollama
curl -fsSL https://ollama.com/install.sh | sh
ollama run qwen2.5:7b
ollama run qwen2.5:14b
ollama run qwen2.5-coder:7bvLLM
# Qwen2.5 14B with AWQ on RTX 3090
vllm serve Qwen/Qwen2.5-14B-Instruct-AWQ \
--quantization awq --max-model-len 8192
# Qwen2.5 7B FP16 on RTX 4060 Ti
vllm serve Qwen/Qwen2.5-7B-Instruct \
--dtype float16 --max-model-len 4096For full deployment guides, see our Ollama hosting and vLLM hosting pages. Compare with other models on our best GPU for LLM inference page and use the benchmark tool for performance data.
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