Compute Options for AI Workloads Intermediate

AI workloads demand specialized compute resources. Choosing the right instance type, accelerator, and deployment model can mean the difference between a training job that takes hours versus weeks, and an inference endpoint that costs $100/month versus $10,000/month. This lesson covers the full spectrum of compute options available in the cloud.

GPU Instance Families

Custom AI Accelerators

• Google TPU v5p - Custom tensor processing units optimized for TensorFlow and JAX workloads with high memory bandwidth
• AWS Trainium - Purpose-built for training with up to 50% cost savings over GPU instances for supported frameworks
• AWS Inferentia2 - Optimized for inference with high throughput and low latency at reduced cost
• Azure Maia 100 - Microsoft's custom AI accelerator designed for cloud-scale AI workloads

Serverless Inference Options

For variable or unpredictable inference workloads, serverless options eliminate idle costs:

• AWS SageMaker Serverless Inference - Pay-per-request model serving with automatic scaling
• Google Cloud Run - Container-based serverless with GPU support for inference
• Azure Container Instances - On-demand container execution with GPU support

Choosing the Right Compute

Training (Large Models, LLMs):
  GPU: H100, A100 (multi-GPU, multi-node)
  TPU: v5p pods for TensorFlow/JAX
  Custom: Trainium for supported frameworks

Training (Small/Medium Models):
  GPU: A10G, L4, T4
  Spot/Preemptible: 60-90% cost savings

Inference (Real-time, Low Latency):
  GPU: L4, T4, A10G (single GPU)
  Custom: Inferentia2 for throughput
  Serverless: Low-traffic endpoints

Inference (Batch):
  GPU: Any available (spot instances)
  CPU: For small models with batch processing