B200 SXM vs Tesla T4

Raw compute power sets the B200 apart: its 4500 TFLOPS FP16 performance enables rapid AI model training, while the T4's 8.1 TFLOPS limits it to smaller workloads. The FP16 to FP32 ratio on B200 (4500 to 90 TFLOPS) optimizes for mixed-precision training common in deep learning, whereas T4's equal 8.1 TFLOPS in both suits balanced but low-intensity operations. For inference, B200's 9000 TFLOPS FP8 further accelerates serving large models.

Memory capabilities profoundly impact real-world use: B200's 8000 GB/s bandwidth supports massive batch sizes in training, preventing bottlenecks with 192 GB VRAM for models exceeding 16 GB on T4. T4's 320 GB/s restricts it to modest batches, ideal for low-latency inference on edge-like setups. In training scenarios, B200 processes datasets far quicker; inference on T4 maintains viability for lightweight models due to lower 70W TDP.

Interconnects enhance B200's scalability with NVLink, PCIe 6.0, and InfiniBand in SXM form, versus T4's basic PCIe, making B200 preferable for multi-GPU clusters.

Opt for the B200 SXM in large-scale AI training or inference requiring over 16 GB VRAM. Its 192 GB HBM3e handles massive language models, with 4500 TFLOPS FP16 enabling efficient processing that T4's 8.1 TFLOPS cannot match. High memory bandwidth of 8000 GB/s supports large batch sizes in production environments.

Data center deployments benefit from B200's NVLink and 1000W TDP scalability, ideal for clusters across 13 cloud offers starting at $1.71 per hour.

Select the Tesla T4 for budget-conscious, low-power inference on small models fitting within 16 GB GDDR6. Its 70W TDP suits dense server packing without cooling demands of B200's 1000W, and 320 GB/s bandwidth handles real-time tasks efficiently.

Legacy applications or cost-sensitive clouds favor T4 at $0.53 per hour average $1.66 per hour, where 8.1 TFLOPS suffices without needing B200's overkill performance.