H200 NVL vs Tesla T4

H200's FP16 throughput of 1979 TFLOPS enables training large language models at speeds T4's 8.1 TFLOPS cannot match, cutting epochs from days to hours in real-world scenarios. Its FP32 performance of 67 TFLOPS supports scientific simulations far beyond T4's 8.1 TFLOPS, while FP8 at 3958 TFLOPS optimizes inference for quantized models. The FP16/FP32 delta on H200 favors mixed-precision workflows common in deep learning, whereas T4's parity suits legacy single-precision tasks.

Memory specifications define workload feasibility: H200's 141 GB VRAM accommodates full model loading for billion-parameter LLMs, unlike T4's 16 GB limit that demands heavy quantization or offloading. Bandwidth at 4800 GB/s on H200 sustains large batch sizes without bottlenecks, improving training stability over T4's 320 GB/s. These factors translate to 10x or greater throughput gains for memory-bound applications like diffusion models.

Opt for H200 NVL in demanding AI pipelines such as LLM training or large-scale inference, where 141 GB HBM3e VRAM loads massive models without swapping. Its 4800 GB/s bandwidth and 1979 TFLOPS FP16 handle high-batch training efficiently, ideal for datacenters leveraging NVLink interconnects. Cloud users benefit from FP8 performance of 3958 TFLOPS for quantized serving at scale.

Select T4 for low-power inference on smaller models, like computer vision tasks fitting within 16 GB GDDR6 VRAM. Its 70W TDP minimizes energy costs in edge or multi-GPU setups, contrasting H200's 700W draw. At an average $1.66 per hour across six offers, T4 delivers 8.1 TFLOPS FP16 economically for non-intensive deployments.