H100 NVL vs H200 SXM

Compute performance remains identical between the H100 NVL and H200 SXM: 1979 TFLOPS in FP16 and 67 TFLOPS in FP32, meaning training and inference speeds match for memory-unconstrained workloads. The key distinction lies in memory: H200 SXM's 141 GB HBM3e VRAM versus H100 NVL's 80 to 94 GB HBM3 enables larger batch sizes in LLM training, reducing iterations and wall-clock time for models exceeding 80 GB. Higher 4800 GB/s bandwidth on the H200 SXM accelerates data movement, cutting latency in inference for long-context sequences compared to the H100 NVL's 3350 GB/s. FP8 at 3958 TFLOPS supports both equally for quantized inference. In practice, memory-bound tasks like fine-tuning billion-parameter models see 40 to 50 percent throughput gains on H200 SXM due to doubled effective capacity. Bandwidth improvements minimize stalls in diffusion models or scientific simulations handling terabyte-scale inputs.

Opt for the H100 NVL in cost-sensitive deployments where models fit within 80 to 94 GB VRAM. Its average cloud pricing of $2.89 per hour undercuts the H200 SXM's $3.70, across nine live offers starting at $1.40 per hour. PCIe form factor availability suits flexible on-premises or smaller cloud instances without needing SXM density. Workloads like Stable Diffusion or fine-tuning under 70B parameters leverage identical 1979 TFLOPS FP16 without memory overhead.

Select the H200 SXM for memory-intensive AI tasks demanding over 94 GB VRAM. The 141 GB HBM3e and 4800 GB/s bandwidth handle massive LLMs or large-batch training, enabling 1.5x larger contexts than H100 NVL. Despite higher average pricing at $3.70 per hour, 23 offers from $1.19 per hour provide scalability in NVLink clusters for production inference.