Qwen VRAM & GPU Calculator
Calculate VRAM requirements and GPU count for Qwen deployment. Support for NVIDIA, AMD, Apple, and Huawei
235 GB
23.5 GB
258.5 GB
4 x NVIDIA H100
Quick Start Examples:
Click these examples to quickly configure popular model deployment scenarios!
GPU Selection Guide for LLM Deployment
💰 Budget-Friendly Options (Under $10k)
- RTX 4090 (24GB): Best for 7B-13B models, single card setup
- RTX 3090 (24GB): Good value for smaller models and experimentation
- Multiple RTX 4060 Ti (16GB): Cost-effective for distributed inference
🏢 Enterprise Solutions ($50k+)
- NVIDIA H100 (80GB): Industry standard for production LLM deployment
- NVIDIA A100 (80GB): Proven reliability, good for 70B+ models
- AMD MI300X (192GB): Highest memory capacity, excellent for largest models
⚡ Pro Tips for Optimization
- Use FP8/INT8: Reduce memory usage by 50-75% with minimal quality loss
- Consider MoE Models: Qwen3-235B-A22B offers flagship performance with 4x H100 (vs 10x for DeepSeek-R1)
- Model Parallelism: Split large models across multiple GPUs
- Mixed Precision: Combine FP16 inference with FP32 gradients for training
- Memory Mapping: Use CPU RAM for model storage, GPU for active layers
Popular AI Models GPU Requirements
🆕 Qwen2.5 & Qwen3 GPU Requirements
Qwen2.5-72B & Qwen3-235B-A22B are the latest flagship models. Qwen2.5-72B needs 2x H100 with FP8, while Qwen3-235B-A22B (MoE) needs 4x H100. The Qwen2.5 series offers excellent multilingual capabilities with efficient deployment.
DeepSeek R1 GPU Requirements
DeepSeek R1 (671B parameters) requires substantial GPU memory. With FP8 precision, you'll need approximately 10x NVIDIA H100 GPUs or equivalent high-memory configurations for optimal inference performance.
Llama 3.1 70B GPU Requirements
Llama 3.1 70B is more accessible. With FP16 precision, you'll need 2x NVIDIA A100 (80GB) or H100. For consumer hardware, you'll need 7x RTX 4090 cards (24GB each).
Llama 3.1 405B GPU Requirements
Llama 3.1 405B requires high-end infrastructure. With FP8 precision, you'll need 6x H100 GPUs. With FP16 precision, you'll need 11x A100 GPUs for deployment.
Use this calculator to get precise memory requirements for your specific use case and budget planning.
Qwen Deployment Tips
💡 Optimization Recommendations:
- • Precision: FP8 recommended for optimal performance/memory balance
- • Parameters: ~235B parameters require careful memory planning
- • Deployment: Consider using model parallelism for large models