GB300 SXM6 vs Tesla P100

The GB300's FP16 throughput of 2250 TFLOPS vastly outpaces the P100's 9.3 TFLOPS, accelerating deep learning training where half-precision computations dominate. This gap allows the GB300 to process model updates far quicker, reducing epochs from days to hours on equivalent datasets. FP32 performance at 90 TFLOPS on the GB300 supports single-precision tasks like simulations better than the P100's matching 9.3 TFLOPS, though both favor FP16 for AI.

Memory bandwidth defines practical limits: the GB300's 12000 GB/s sustains large batch sizes in training and inference, minimizing data starvation even with 288 GB VRAM fully utilized. The P100's 732 GB/s constrains batches to smaller sizes, throttling throughput on memory-intensive workloads. In inference, the GB300's FP8 capability at 4500 TFLOPS enables high-volume serving, unavailable on the P100.

Power draw reveals trade-offs: the GB300's 1400W TDP demands robust cooling and infrastructure, while the P100's 250W suits denser, lower-cost deployments. Overall, these specs translate to orders-of-magnitude gains for the GB300 in modern AI pipelines.

Opt for the GB300 in large-scale AI training and inference requiring immense VRAM and compute. Its 288 GB HBM3e handles models exceeding 100 billion parameters without partitioning, and 2250 TFLOPS FP16 speeds convergence. Deploy it via NVSwitch and NVLink for multi-GPU clusters scaling to exaFLOPS.

The GB300 excels where memory bandwidth of 12000 GB/s prevents bottlenecks in high-batch scenarios, ideal for research labs or enterprises pushing generative AI frontiers.

Select the P100 for legacy Pascal-optimized codebases or budget-constrained prototyping. At $0.60 per hour, it provides accessible NVLink interconnects and 16 GB HBM2 for modest deep learning tasks without overprovisioning.

Its 250W TDP enables dense PCIe or SXM2 racks in environments prioritizing power efficiency over peak performance, such as academic inference on pre-2018 models.