GB300 vs MI355X

Memory bandwidth defines a key divide: GB300's 12000 GB/s supports larger batch sizes in LLM training than MI355X's 8000 GB/s, minimizing stalls when saturating 288 GB HBM3e VRAM with model weights and activations. This advantage accelerates throughput in memory-bound scenarios, such as multi-trillion parameter models.

FP16 throughput is nearly identical, with MI355X at 2300 TFLOPS slightly ahead of GB300's 2250 TFLOPS, favoring both for inference where half-precision dominates. GB300's FP32 lags severely at 90 TFLOPS against MI355X's 2300 TFLOPS, limiting it in FP32-heavy simulations but excelling via FP8 at 4500 TFLOPS close to MI355X's 4600 TFLOPS.

Power efficiency tilts toward MI355X at 750W TDP versus GB300's 1400W, allowing denser deployments. Interconnects differ too: NVSwitch and NVLink on GB300 for multi-GPU scaling, Infinity Fabric on MI355X for AMD clusters. Overall, GB300 prioritizes bandwidth for training scale, MI355X compute balance for mixed precision.

Select the GB300 for workloads demanding peak memory bandwidth, such as training LLMs with batch sizes exceeding 1 million tokens: its 12000 GB/s throughput outperforms MI355X's 8000 GB/s. NVIDIA's NVSwitch and NVLink interconnects enhance multi-GPU setups in SXM form factor, ideal for hyperscale clusters.

NVIDIA ecosystem maturity suits teams leveraging CUDA and TensorRT, where 288 GB HBM3e pairs with 4500 TFLOPS FP8 for inference at scale.

Choose the MI355X in power-constrained environments: its 750W TDP consumes half the energy of GB300's 1400W, enabling twice the GPU density per rack. High FP32 performance at 2300 TFLOPS excels in scientific computing or fine-tuning requiring full precision, unlike GB300's 90 TFLOPS.

AMD's Infinity Fabric and OAM form factor integrate seamlessly in ROCm-based pipelines, with 2300 TFLOPS FP16 matching training needs efficiently.