Managing GPU Resources

Overview

Efficient GPU resource management is crucial for optimizing performance and controlling costs in AI workloads. This guide covers best practices for managing GPU resources on the Transium platform.

Available GPU Types

NVIDIA A100

High-performance GPU for large-scale training and inference workloads.

  • 40GB or 80GB HBM2e memory
  • Tensor Core acceleration
  • Best for: Large language models, computer vision

NVIDIA H100

Latest generation GPU with enhanced performance for transformer models.

  • 80GB HBM3 memory
  • 4th Gen Tensor Cores
  • Best for: Large-scale transformer training

NVIDIA V100

Cost-effective option for smaller workloads and development.

  • 16GB or 32GB HBM2 memory
  • Tensor Core support
  • Best for: Development, smaller models

Resource Allocation

Configure GPU resources for your workloads:

# Request specific GPU configuration
job = client.training.create(
model_config="./config.yaml",
gpu_type="A100",
gpu_count=4,
memory_per_gpu="40GB"
)

Monitoring and Optimization

Monitor GPU utilization and optimize performance:

  • Use the dashboard to monitor real-time GPU utilization
  • Set up alerts for underutilized resources
  • Implement automatic scaling based on workload demands
  • Use mixed precision training to optimize memory usage
# Monitor GPU usage
metrics = client.monitoring.get_gpu_metrics(
job_id="your-job-id"
)

print(f"GPU Utilization: {metrics.utilization}%")

Cost Optimization

Best practices for managing GPU costs:

  • Use spot instances for non-critical workloads
  • Implement automatic shutdown for idle resources
  • Choose the right GPU type for your workload
  • Use batch processing for inference workloads
  • Monitor and set budget alerts

Troubleshooting

Out of Memory Errors

Reduce batch size, use gradient checkpointing, or upgrade to higher memory GPUs.

Low GPU Utilization

Check data loading bottlenecks, increase batch size, or use multiple GPUs.

Resource Allocation Failures

Try different regions, use alternative GPU types, or schedule jobs during off-peak hours.