Choosing the right GPU for AI workloads can be overwhelming—especially with the rapid evolution of deep learning, generative models, and edge AI. Whether you're building a rackmount server for training large language models or deploying inference at scale, this guide will help you make an informed decision based on performance, compatibility, and budget.
- Workload Type
- Training vs. Inference: Training requires high memory bandwidth and compute cores (e.g., NVIDIA A100), while inference can be optimized with lower-power GPUs (e.g., RTX 4000).
- Model Size: Larger models like GPT or BERT need more VRAM and tensor cores.
- GPU Architecture
GPU Series Architecture Best For VRAM NVIDIA A100 Ampere Large-scale training 40–80 GB RTX 6000 Ada Ada Lovelace Mixed workloads 48 GB RTX 4000 SFF Ada Lovelace Edge inference 20 GB AMD Instinct MI300 CDNA 3 HPC & AI 128 GB - Form Factor & Compatibility
- Rackmount Size: Ensure GPU fits within 1U, 2U, or 4U chassis.
- Cooling Requirements: High-performance GPUs may need liquid cooling or enhanced airflow.
- Power Consumption
- Check PSU wattage and redundancy.
- Consider GPUs with lower TDP for energy-efficient deployments.
- RTX 6000 Ada Rackmount Server
- A100 80GB Deep Learning Server
- Compact RTX 4000 SFF AI Node
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