3 results
CPU for complex control flow, GPU for large-scale parallel computation, TPU for matrix operations pushed to the extreme. For most engineers, the real decision is cloud inference on GPU vs CPU, and when a TPU rental is worth it.
CUDA OOM errors have five common root causes: oversized batch, gradients accumulating in the computation graph, unreleased intermediate tensors, multi-GPU imbalance, and memory fragmentation. Correct diagnosis beats adding empty_cache() every time.
NVIDIA's latest inference optimizations — FP8/INT4 quantization, 2:4 structured sparsity, and TensorRT-LLM system improvements — dramatically increase throughput and cut deployment cost with negligible accuracy loss.