H100 SXM5 vs Tesla T4

The H100 SXM5 dominates in compute throughput: its 1979 TFLOPS FP16 vastly exceeds the T4's 8.1 TFLOPS, accelerating deep learning training where half-precision dominates. FP32 performance shows 67 TFLOPS for H100 versus 8.1 TFLOPS for T4, benefiting simulations and precise inference. FP8 at 3958 TFLOPS on H100 further optimizes quantized inference, unavailable on T4.

Memory bandwidth profoundly impacts workloads: H100's 3350 GB/s supports massive batch sizes in transformer models, preventing bottlenecks in LLM training. T4's 320 GB/s limits it to smaller batches, suitable for real-time inference but not large-scale processing. VRAM disparity, 80 GB versus 16 GB, determines model capacity: H100 loads billion-parameter LLMs fully, while T4 requires heavy quantization or offloading.

Power efficiency differs sharply with H100's 700W TDP versus T4's 70W. This makes T4 viable for dense deployments but positions H100 for raw speed in data centers.

The H100 SXM5 excels in large-scale AI training and inference where 1979 TFLOPS FP16 and 80 GB HBM3 VRAM handle models exceeding 16 GB. It supports high batch sizes via 3350 GB/s bandwidth, ideal for LLM fine-tuning or Stable Diffusion at scale. Cloud users prioritize its NVLink interconnect for multi-GPU setups at $0.80 per hour starting price.

The T4 fits low-power inference tasks with its 70W TDP and 8.1 TFLOPS FP16, consuming far less energy than H100's 700W. It serves real-time applications like video analytics on 16 GB VRAM at $0.53 per hour. Budget-conscious deployments favor its PCIe simplicity over H100's SXM5 complexity.