H100 NVL vs MI355X

Compute throughput reveals distinct priorities: the MI355X delivers 2300 TFLOPS in both FP16 and FP32, enabling balanced workloads in training and scientific simulations, whereas the H100 provides 1979 TFLOPS FP16 but only 67 TFLOPS FP32, prioritizing AI-specific precisions like FP8 at 3958 TFLOPS. This FP16 to FP32 delta means H100 accelerates inference on quantized models efficiently, but MI355X handles FP32-dominant tasks such as physics simulations without precision loss. FP8 performance of 4600 TFLOPS on MI355X further boosts low-precision inference scalability. Memory differences profoundly impact real-world usage: 288 GB VRAM on MI355X supports trillion-parameter models in single-GPU setups, avoiding sharding overheads common with H100's 80 to 94 GB. The 8000 GB/s bandwidth on MI355X doubles H100's 3350 GB/s, permitting larger batch sizes in training to achieve faster convergence and higher throughput. These factors reduce iteration times in LLM fine-tuning by minimizing data bottlenecks.

Opt for the H100 NVL in production environments requiring immediate deployment. Nine live cloud offers start at $1.40 per hour, averaging $2.89 per hour, with NVLink and PCIe 5.0 interconnects enabling seamless multi-GPU scaling via InfiniBand. CUDA ecosystem maturity ensures compatibility for current LLM inference pipelines. Its 700W TDP fits existing 700W power envelopes in SXM5 or NVL form factors.

Select the MI355X for forward-looking memory-bound applications. 288 GB HBM3e VRAM accommodates massive models without distribution, and 8000 GB/s bandwidth sustains high-throughput training. 750W TDP in OAM form factor suits next-generation racks with Infinity Fabric for AMD clusters. FP32 at 2300 TFLOPS excels in HPC alongside AI tasks.