AI GPU Optimization

We profile your workloads using NVIDIA Nsight Systems and PyTorch Profiler. The profile reveals GPU idle time (waiting for data loading), memory fragmentation, kernel launch overhead, and communication bottlenecks. Most teams find their GPUs spend 40-60% of the time waiting for CPU preprocessing or data I/O. We identify the specific bottlenecks before optimizing.

Gradient checkpointing trades compute for memory — fitting larger batch sizes or bigger models on the same GPU. Mixed precision training (AMP) halves memory usage with minimal accuracy impact. We configure PyTorch memory allocation strategies to reduce fragmentation. For inference, weight quantization and KV cache management free up memory for larger batch sizes.

Flash Attention replaces standard attention with a memory-efficient, IO-aware implementation — 2-4x speedup on transformer models. Compiled models (torch.compile, TensorRT) eliminate Python overhead and fuse operations. Data loader tuning (prefetching, num_workers, pin_memory) ensures GPUs never starve for data. These optimizations compound — 2x from Flash Attention + 1.5x from compilation = 3x total speedup.

Bin-packing algorithms place inference workloads to maximize GPU utilization per node. MIG partitioning on A100s runs multiple small models on one GPU. Time-sharing multiplexes batch and interactive workloads on the same hardware. Priority queues ensure high-priority inference never gets starved by batch training. You get a GPU fleet that runs at 80%+ utilization instead of 30%.