Top GPUs for AI workloads include:
- NVIDIA H100 – Ideal for large-scale training; available on AWS, Google Cloud, Azure, and DGX Cloud.
- NVIDIA A100 – High memory (up to 80GB) and efficient multi-instance support.
- NVIDIA H200 & B200 – Latest models with cutting-edge performance.
- L40s & RTX 6000 Ada – Great for AI tasks, built on Ada Lovelace architecture.
Key Factors When Choosing a Cloud GPU for AI/LLMs -
Memory (VRAM):
Large models need high memory—look for GPUs like A100 or H100.
Performance:
Newer architectures (e.g., Blackwell, Ada Lovelace) deliver faster results for complex tasks.
Cost:
Platforms like AceCloud, E2e and RunPod offer budget-friendly options; big providers offer enterprise-grade solutions.
Scalability:
Pick a provider that fits your project size—from small experiments to full-scale deployments.
Task Type:
Training and inference have different needs—consider memory, speed, and architecture for your specific use case.