B200 vs Quadro P4000

Compute performance reveals a profound gap: the B200 achieves 4500 TFLOPS in FP16 compared to 5.3 TFLOPS on the P4000, accelerating AI training and inference by orders of magnitude. The B200's FP32 rate of 90 TFLOPS surpasses the P4000's 5.3 TFLOPS, benefiting general-purpose simulations. FP16 dominance on the B200 supports mixed-precision training for large language models, reducing time from days to hours on equivalent workloads. Memory specifications amplify this: 192 GB HBM3e versus 8 GB GDDR5 allows the B200 to process models exceeding 100 billion parameters without swapping, while the P4000 limits users to small datasets. Bandwidth of 8000 GB/s on the B200 versus 243 GB/s enables larger batch sizes in training, minimizing data loading bottlenecks and improving throughput by over 30 times. The B200's 1000W TDP reflects its scale, contrasting the P4000's efficient 105W for low-demand scenarios.

Select the B200 for large-scale AI training and inference requiring immense compute and memory. Its 4500 TFLOPS FP16 and 192 GB VRAM handle models like GPT-scale transformers, while 8000 GB/s bandwidth supports batch sizes infeasible on older hardware. Cloud deployments benefit from NVLink and InfiniBand for clustering, justifying $1.71 per hour starting pricing in high-throughput environments.

Opt for the Quadro P4000 in budget-constrained visualization or CAD workflows. Its 5.3 TFLOPS FP32 and 8 GB GDDR5 suffice for rendering moderate scenes, with 105W TDP enabling dense workstation packing. At $0.51 per hour, it delivers value for legacy software incompatible with modern architectures.