H200 NVL vs Tesla P100

Superior FP16 performance defines the H200 NVL's edge in deep learning: 1979 TFLOPS enables rapid training of large models, while the P100's 9.3 TFLOPS suits only small-scale or legacy tasks. The FP32 rating of 67 TFLOPS on the H200 NVL supports precise scientific computations and inference, exceeding the P100's 9.3 TFLOPS by over sevenfold. This disparity means training epochs complete in minutes rather than hours for equivalent workloads on the H200 NVL. Memory specs transform practical usability: 141 GB VRAM on the H200 NVL accommodates enormous batch sizes in LLM training, avoiding out-of-memory issues common on the P100's 16 GB limit. Bandwidth of 4800 GB/s on the H200 NVL minimizes data stalls during intensive operations, compared to 732 GB/s on the P100 which constrains throughput for large datasets. Power draw at 700W TDP for the H200 NVL versus 250W for the P100 influences cluster efficiency, though performance gains offset higher consumption in demanding scenarios.

The H200 NVL dominates modern machine learning pipelines. Its 141 GB HBM3e VRAM and 1979 TFLOPS FP16 performance excel in LLM training and fine-tuning, handling models beyond the P100's 16 GB capacity. High 4800 GB/s bandwidth and FP8 at 3958 TFLOPS optimize inference at scale, especially in NVLink or InfiniBand setups. Cloud users prioritizing speed select it despite averages of $2.39 per hour.

The P100 fits niche legacy applications tied to Pascal-era CUDA code. Its 16 GB HBM2 suffices for modest simulations or older ML models where recompilation proves costly. At 250W TDP and $0.60 per hour average pricing, it delivers economical runtime for non-urgent tasks insensitive to the H200 NVL's superior 1979 TFLOPS FP16 or 141 GB VRAM.