Intel Gaudi 2 vs H100 SXM5

NVIDIA H100 SXM5 excels in low-precision compute with 1979 TFLOPS FP16 and 3958 TFLOPS FP8, accelerating inference workloads that prioritize speed over precision. Gaudi 2 maintains balance at 420 TFLOPS for both FP16 and FP32, benefiting training pipelines requiring FP32 tensor cores for gradient computations. This delta means H100 handles transformer inference faster, while Gaudi 2 sustains stable training loops without precision bottlenecks.

Higher memory bandwidth of 3350 GB/s on H100 supports larger batch sizes in data-intensive tasks, reducing iteration times compared to Gaudi 2's 2460 GB/s. Gaudi 2's 96 GB VRAM edges out H100's 80-94 GB, allowing fuller model loading for memory-constrained fine-tuning. Power draw at 700W for H100 versus 600W for Gaudi 2 impacts cluster density, with H100 demanding more cooling.

Real-world implications favor H100 for throughput-oriented inference at scale, while Gaudi 2 optimizes cost-effective training with its symmetric FP16/FP32 rates.

Select Intel Gaudi 2 for budget-limited projects emphasizing balanced compute. Its 420 TFLOPS FP32 matches FP16, ideal for training where higher precision avoids accumulation errors, and 96 GB VRAM fits large models seamlessly. At an average $1.08 per hour, Ethernet interconnect suits standard data center fabrics without proprietary hardware.

Gaudi 2 thrives in fine-tuning or scientific simulations needing FP32 stability across modest clusters.

Opt for NVIDIA H100 SXM5 in performance-critical deployments leveraging 1979 TFLOPS FP16 and 3958 TFLOPS FP8 for rapid inference. Superior 3350 GB/s bandwidth enables massive batch sizes, cutting latency in production serving. NVLink interconnect scales efficiently to hundreds of GPUs.

H100 dominates LLM inference and high-throughput training across its 32 cloud offers averaging $3.54 per hour.