Gaudi 2 vs H100

Peak compute reveals stark differences suited to AI paradigms: H100's 1979 TFLOPS FP16 vastly exceeds Gaudi 2's 420 TFLOPS, accelerating mixed-precision training for large models. Gaudi 2 matches its FP16 with 420 TFLOPS FP32, outperforming H100's 67 TFLOPS FP32 for precision-sensitive simulations. H100's 3958 TFLOPS FP8 enables ultra-efficient inference on quantized models.

Memory bandwidth impacts real-world throughput: H100's 3350 GB/s supports larger batch sizes in transformer training compared to Gaudi 2's 2460 GB/s, reducing data movement bottlenecks for models exceeding 80 GB VRAM. Gaudi 2's 96 GB HBM2e edges H100's 80-94 GB HBM3 for memory-bound tasks like fine-tuning with massive datasets.

Interconnect options enhance scalability: H100's NVLink and InfiniBand facilitate multi-GPU setups with lower latency than Gaudi 2's Ethernet, critical for distributed training at scale. Higher 700W TDP on H100 correlates with its performance density, while Gaudi 2's 600W suits power-constrained environments.

Opt for Gaudi 2 in cost-sensitive deployments requiring high FP32 performance. Its 420 TFLOPS FP32 surpasses H100's 67 TFLOPS, benefiting scientific computing and simulations needing full precision. Average pricing of $1.08/hr across offers provides value over H100's $3.17/hr average.

The 96 GB HBM2e VRAM and 600W TDP fit memory-intensive tasks in Ethernet-based clusters with fewer nodes.

Select H100 for peak AI training and inference speed. Its 1979 TFLOPS FP16 and 3958 TFLOPS FP8 deliver up to 4.7 times higher throughput than Gaudi 2's 420 TFLOPS in low-precision deep learning.

Superior 3350 GB/s bandwidth and NVLink interconnect enable efficient scaling across 56 cloud offers starting at $0.80/hr, ideal for large-scale LLM workloads.