Gaudi 2 vs GH200

Compute specifications highlight GH200's dominance in mixed-precision AI tasks: its 1979 TFLOPS FP16 exceeds Gaudi 2's 420 TFLOPS by over 4x, accelerating neural network training where half-precision dominates. Gaudi 2's balanced 420 TFLOPS FP16 and FP32 suits workloads requiring single-precision accuracy, unlike GH200's reduced 67 TFLOPS FP32. GH200's 3958 TFLOPS FP8 further boosts inference latency for large language models.

Memory bandwidth disparities impact real-world scalability: GH200's 4000 GB/s versus Gaudi 2's 2460 GB/s enables larger batch sizes, fitting more samples per iteration and shortening training epochs on memory-bound models. This advantage persists despite identical 96 GB VRAM, as HBM3 sustains higher data throughput.

Power draw reflects these differences, with GH200's 900W TDP demanding robust cooling compared to Gaudi 2's 600W, influencing deployment density in data centers.

Gaudi 2 excels in cost-sensitive projects: its pricing from $0.91/hr averaging $1.08/hr undercuts GH200's $1.99/hr to $3.59/hr average, delivering strong value at 420 TFLOPS FP16 and FP32. The 600W TDP simplifies integration into power-constrained clusters versus GH200's 900W. Ethernet interconnect supports scalable Ethernet-based training without proprietary hardware.

Balanced performance favors Gaudi 2 for FP32-heavy simulations or fine-tuning where 420 TFLOPS matches or exceeds GH200's 67 TFLOPS.

GH200 is optimal for high-throughput AI training and inference: 1979 TFLOPS FP16 processes models 4.7x faster than Gaudi 2's 420 TFLOPS, ideal for large-scale LLM development. The 4000 GB/s bandwidth handles massive batches, reducing iteration times.

NVLink-C2C and PCIe 5.0 enable efficient multi-GPU scaling, outperforming Ethernet for distributed workloads, despite higher $3.59/hr average cost.