Gaudi 2 vs P100

Gaudi 2 vastly outpaces P100 in compute: its 420 TFLOPS FP16 and FP32 ratings exceed P100's 9.3 TFLOPS by a factor of 45, enabling faster matrix multiplications central to deep learning. This delta accelerates training epochs and inference latency, particularly for models beyond P100's capacity.

Equal FP16 and FP32 performance on Gaudi 2, at 420 TFLOPS each, supports balanced tensor core and single-precision operations, ideal for mixed-precision training schemes. P100 matches FP16 and FP32 at 9.3 TFLOPS but lacks Gaudi 2's scale, limiting it to smaller models.

Memory differences prove critical: Gaudi 2's 2460 GB/s bandwidth, over 3.3 times P100's 732 GB/s, sustains larger batch sizes by reducing data bottlenecks during gradient computations. Coupled with 96 GB versus 16 GB VRAM, Gaudi 2 handles massive datasets or model parameters without swapping, enhancing throughput in memory-bound scenarios like transformer training.

Select Gaudi 2 for large-scale AI training or inference requiring high VRAM: its 96 GB HBM2e supports models exceeding 16 GB, such as billion-parameter LLMs. The 2460 GB/s bandwidth and 420 TFLOPS FP16/FP32 enable efficient handling of large batches, reducing time-to-results despite 600W TDP.

Ethernet interconnect suits scalable cloud clusters, making Gaudi 2 preferable for production workloads where performance justifies $0.91 per hour starting price.

Choose P100 for budget-sensitive prototyping or legacy codebases: at $0.07 per hour average $0.25, it undercuts Gaudi 2's $1.08 per hour by over 4 times. Its 250W TDP and NVLink interconnect fit low-power or multi-GPU setups with lighter demands.

The 16 GB VRAM and 732 GB/s bandwidth suffice for small models or fine-tuning under 9.3 TFLOPS constraints, ideal for experimentation without high costs.