Intel Gaudi 2 vs H200 SXM

H200 demonstrates superior peak performance: 1979 TFLOPS FP16 compared to Gaudi 2's 420 TFLOPS, enabling faster matrix multiplications in transformer models. Gaudi 2's equal 420 TFLOPS FP16 and FP32 supports training phases requiring higher precision, while H200's 67 TFLOPS FP32 indicates optimization for inference over full-precision training. The FP8 capability of 3958 TFLOPS on H200 accelerates quantized inference for LLMs, reducing latency by handling larger effective batch sizes.

Memory differences prove critical: H200's 141 GB HBM3e versus 96 GB HBM2e allows loading models up to 141 billion parameters without sharding, versus Gaudi 2's limit around 96 billion. Bandwidth of 4800 GB/s on H200 doubles Gaudi 2's 2460 GB/s, supporting batch sizes twice as large in memory-bound tasks like fine-tuning, minimizing data transfer bottlenecks.

In real-world terms, H200 suits high-throughput inference clusters, while Gaudi 2 fits balanced training on Ethernet fabrics, where its lower 600W TDP reduces cooling demands compared to 700W.

Select Gaudi 2 for cost-sensitive deployments requiring balanced compute. At $0.91 per hour minimum and $1.08 average, it undercuts H200's $1.19 minimum and $3.70 average by up to 70 percent, ideal for prolonged training runs. Its 420 TFLOPS FP32 matches FP16, benefiting workloads like scientific simulations needing precision without NVLink scaling.

Ethernet interconnect suits non-NVIDIA ecosystems, and 600W TDP lowers operational costs in OAM form factors versus H200's 700W SXM.

Choose H200 for peak inference performance and large models. 3958 TFLOPS FP8 and 1979 TFLOPS FP16 deliver over 4x Gaudi 2's throughput, essential for serving LLMs at scale. 141 GB VRAM and 4800 GB/s bandwidth enable massive batch sizes, reducing per-token latency.

NVLink and InfiniBand support multi-GPU clusters across 23 cloud offers, outperforming Gaudi 2's Ethernet for distributed training.