B300 SXM6 vs RTX 3090

Raw compute reveals stark contrasts: the B300 delivers 2250 TFLOPS in FP16 for training large models, 63 times the RTX 3090's 35.6 TFLOPS, while FP32 stands at 90 TFLOPS versus 35.6 TFLOPS, more than doubling single-precision needs for scientific simulations. The FP16 to FP32 delta on the B300, 25:1 ratio, optimizes mixed-precision training, reducing memory use without accuracy loss, unlike the RTX 3090's balanced 1:1 ratio suited to graphics.

Memory specs transform real-world usage: 288 GB VRAM on the B300 supports batch sizes up to 12 times larger than the RTX 3090's 24 GB limit, fitting trillion-parameter LLMs entirely in memory. Bandwidth of 12000 GB/s versus 936 GB/s cuts data bottlenecks, speeding inference by enabling 12.8 times faster tensor movements. Power draw hits 1200W for B300 versus 350W for RTX 3090, demanding robust cooling but yielding density in clusters via NVSwitch over PCIe limitations.

Interconnects favor enterprise: B300's NVSwitch and NVLink scale multi-GPU setups beyond RTX 3090's NVLink, ideal for distributed training.

Opt for the B300 in large-scale AI training or inference where models exceed 24 GB VRAM, such as 1T-parameter LLMs leveraging 288 GB HBM3e. Its 2250 TFLOPS FP16 and 12000 GB/s bandwidth handle massive batches and datasets efficiently, with NVSwitch enabling seamless multi-GPU scaling. Cloud users prioritize throughput over cost at $2.45 per hour starting price for production workloads.

Select the RTX 3090 for budget-conscious prototyping, fine-tuning small models under 24 GB, or Stable Diffusion tasks where 35.6 TFLOPS FP16 suffices. At $0.08 per hour starting and 44 cloud offers, it provides accessible entry for individuals or SMBs avoiding 1200W TDP infrastructure. PCIe form factor suits single-node experimentation without NVSwitch complexity.