B200 SXM vs Tesla P100

Performance metrics reveal the B200's dominance in compute capabilities. It delivers 4500 TFLOPS in FP16 and 90 TFLOPS in FP32, contrasting sharply with the P100's 9.3 TFLOPS in both formats. This FP16 advantage accelerates deep learning training and inference, where half-precision computations prevail: the B200 processes over 484 times more FP16 operations per second. The FP32 delta, roughly 9.7 times higher, benefits simulation and rendering tasks requiring single-precision arithmetic.

Memory specifications profoundly impact real-world workloads. The B200's 192 GB VRAM supports massive models and large batch sizes without swapping, unlike the P100's 16 GB limit, which constrains model complexity. Bandwidth at 8000 GB/s versus 732 GB/s enables faster data transfers, reducing bottlenecks in memory-intensive operations like transformer training. Higher TDP of 1000W on the B200 demands robust cooling, while the P100's 250W suits efficient setups.

These differences translate to practical gains: B200 handles contemporary LLMs with billions of parameters, whereas P100 struggles beyond modest scales.

Opt for the NVIDIA B200 in demanding AI and HPC environments. Its 4500 TFLOPS FP16 performance excels in training large language models, where the P100's 9.3 TFLOPS falls short. The 192 GB HBM3e VRAM accommodates models exceeding 100 billion parameters, enabling efficient fine-tuning and inference at scale.

High memory bandwidth of 8000 GB/s supports large batch sizes critical for throughput in cloud deployments, justifying the $1.71 per hour starting price for professionals prioritizing speed over cost.

Select the NVIDIA Tesla P100 for budget-limited or legacy applications. At $0.07 per hour, it offers affordability for small-scale tasks where 9.3 TFLOPS FP32 suffices, such as basic scientific simulations or prototyping.

Its 250W TDP fits power-constrained environments, and 16 GB HBM2 handles modest datasets without excess capacity. Compatibility with older NVLink interconnects aids migrations from Pascal-era codebases.