B200 SXM vs Quadro P4000

The B200's FP16 performance reaches 4500 TFLOPS, dwarfing the Quadro P4000's 5.3 TFLOPS: this gap accelerates AI training and inference where half-precision computations dominate. The B200's FP32 at 90 TFLOPS still exceeds the P4000's 5.3 TFLOPS, but the pronounced FP16 advantage means faster matrix multiplications in deep learning frameworks. FP8 capability on the B200 hits 9000 TFLOPS, enabling even higher throughput for quantized inference not available on the older P4000.

Memory specifications transform real-world usability: 192 GB HBM3e on the B200 supports massive models and batch sizes up to thousands, limited only by 8 GB GDDR5 on the P4000 which restricts to small datasets. Bandwidth of 8000 GB/s versus 243 GB/s ensures the B200 sustains high data throughput during training epochs, reducing bottlenecks in large-scale simulations. Consequently, tasks like LLM fine-tuning complete orders of magnitude faster on the B200.

Power and interconnects amplify differences: the B200's 1000W TDP demands robust cooling, paired with NVLink and PCIe 6.0 for multi-GPU scaling, while the P4000's 105W fits low-power setups without advanced networking.

The B200 excels in demanding AI workloads: LLM training benefits from 4500 TFLOPS FP16 and 192 GB VRAM to handle models exceeding 100 billion parameters. Data centers running inference at scale leverage 9000 TFLOPS FP8 and 8000 GB/s bandwidth for low-latency serving of large batches. High-performance computing simulations require the B200's interconnects like NVLink for distributed processing across nodes.

The Quadro P4000 suits budget visualization tasks: its 5.3 TFLOPS FP32 handles CAD rendering or light video editing within 8 GB VRAM constraints. Low-power environments favor its 105W TDP for desktops without high electricity costs at $0.51 per hour. Legacy software certified for Pascal architecture runs efficiently without needing modern upgrades.