Gaudi 2 vs GB300

Memory specifications highlight a clear disparity: GB300's 288 GB HBM3e exceeds Gaudi 2's 96 GB HBM2e by threefold, enabling larger models without partitioning. The 12000 GB/s bandwidth of GB300 surpasses Gaudi 2's 2460 GB/s by nearly fivefold, supporting bigger batch sizes in training and reducing data movement bottlenecks during inference.

Compute metrics reveal GB300's FP16 at 2250 TFLOPS dwarfs Gaudi 2's 420 TFLOPS, accelerating matrix multiplications central to deep learning. Gaudi 2 maintains parity in FP32 at 420 TFLOPS over GB300's 90 TFLOPS, benefiting precision-sensitive simulations, though GB300's FP8 at 4500 TFLOPS optimizes low-precision inference for LLMs. This FP16/FP32 delta favors GB300 for training throughput while Gaudi 2 suits balanced precision tasks.

Power and interconnects impact deployments: GB300's 1400W TDP demands robust cooling versus Gaudi 2's 600W, and NVLink/NVSwitch enables faster multi-GPU scaling than Ethernet.

Gaudi 2 suits immediate deployments requiring cost-effective scaling. At $0.91 per hour average, it undercuts future GB300 pricing expectations, with 96 GB VRAM handling models up to 70B parameters in Ethernet clusters.

Lower 600W TDP fits power-constrained environments, and balanced 420 TFLOPS FP16/FP32 performance excels in fine-tuning or scientific computing where Ethernet suffices.

GB300 excels in demanding AI workloads needing extreme scale. Its 288 GB VRAM and 12000 GB/s bandwidth manage models exceeding 500B parameters with large batches, leveraging 2250 TFLOPS FP16 for rapid training.

FP8 at 4500 TFLOPS optimizes high-volume inference, while NVLink/NVSwitch supports dense multi-GPU fabrics despite 1400W TDP.