H100 NVL vs MI300X

H100 NVL excels in FP16 and FP8 workloads, delivering 1979 TFLOPS FP16 versus MI300X's 1307 TFLOPS: this translates to faster deep learning training where half-precision computations dominate matrix operations. FP8 at 3958 TFLOPS on H100 supports efficient quantized inference, outpacing MI300X's 2614 TFLOPS for serving models at scale. FP32 performance reverses, with MI300X's 163 TFLOPS suiting simulations over H100's 67 TFLOPS.

MI300X's 192 GB VRAM dwarfs H100 NVL's 80 to 94 GB, enabling single-GPU handling of massive models and larger batch sizes without sharding. Its 5300 GB/s bandwidth, compared to 3350 GB/s, minimizes stalls in memory-intensive tasks like LLM inference, boosting effective throughput. TDP stands at 700W for H100 NVL and 750W for MI300X, with similar power envelopes.

Select the H100 NVL for FP16-heavy training workloads, where 1979 TFLOPS outperforms MI300X's 1307 TFLOPS, reducing iteration times in transformer models. NVLink interconnect and PCIe 5.0 enable seamless multi-GPU scaling in distributed setups common for large-scale AI development.

Choose MI300X for memory-constrained inference on enormous LLMs: 192 GB HBM3 VRAM fits models that exceed H100 NVL's 80 to 94 GB capacity. Infinity Fabric and 5300 GB/s bandwidth, plus $0.50 per hour starting price, optimize cost for high-throughput serving.