GH200 vs MI355X

Memory specifications define a clear advantage for the MI355X: 288 GB HBM3e VRAM surpasses the GH200's 96 GB HBM3, enabling larger models without fragmentation. The 8000 GB/s bandwidth doubles the GH200's 4000 GB/s, which supports bigger batch sizes in training and reduces latency in data-heavy inference. These factors prove essential for LLMs exceeding 100 billion parameters.

Compute performance reveals distinct profiles. The GH200 achieves 1979 TFLOPS in FP16 and only 67 TFLOPS in FP32, reflecting NVIDIA's emphasis on low-precision tensor operations for AI training. In contrast, the MI355X balances at 2300 TFLOPS for both FP16 and FP32, benefiting FP32-dominant scientific simulations or hybrid workloads. FP8 performance reaches 4600 TFLOPS on MI355X versus 3958 TFLOPS on GH200, aiding efficient inference.

Power efficiency favors the MI355X with 750W TDP against the GH200's 900W. Lower TDP implies better density in racks, though real-world throughput depends on software optimization and interconnects like Infinity Fabric versus NVLink-C2C.

The GH200 suits immediate deployments in NVIDIA-centric environments. Its NVLink-C2C and PCIe 5.0 interconnects enable seamless multi-GPU scaling for current AI pipelines. Availability from $1.99 per hour across four providers ensures quick access without delays.

Users prioritizing mature Hopper ecosystem support choose the GH200 for FP16-heavy tasks at 1979 TFLOPS, where software maturity outweighs raw specs.

The MI355X excels in memory-intensive scenarios with 288 GB HBM3e and 8000 GB/s bandwidth. It handles enormous models that exceed the GH200's 96 GB capacity, ideal for next-generation LLMs.

Balanced FP32 at 2300 TFLOPS makes it preferable for scientific computing or training requiring precision, plus lower 750W TDP for efficient scaling.