B300 vs RTX 3090

The B300 dominates in compute throughput: its 2250 TFLOPS FP16 capability vastly outpaces the RTX 3090's 35.6 TFLOPS, enabling faster model training where half-precision arithmetic prevails. The FP32 performance shows a narrower gap at 90 TFLOPS for the B300 against 35.6 TFLOPS for the RTX 3090, yet still doubles throughput for precision-sensitive tasks. This delta translates to training large language models in hours rather than days on the B300.

Memory specifications reshape workloads profoundly: 288 GB HBM3e VRAM on the B300 supports enormous batch sizes and models that exceed 24 GB GDDR6X limits on the RTX 3090. The 12000 GB/s bandwidth versus 936 GB/s minimizes data bottlenecks, accelerating inference and reducing latency in memory-bound scenarios. For FP8 tasks, the B300's 4500 TFLOPS further amplifies inference speed.

Power demands reflect these disparities: the B300's 1200W TDP suits datacenter cooling, while the RTX 3090's 350W fits consumer setups.

The B300 excels in enterprise-scale AI deployments: its 288 GB VRAM handles massive models for LLM training, and 12000 GB/s bandwidth supports high-throughput inference. Choose it when workloads demand 2250 TFLOPS FP16 or 4500 TFLOPS FP8, such as training billion-parameter models across NVSwitch interconnects at $6.94 per hour.

The RTX 3090 suits budget-conscious prototyping: at $0.08 per hour, its 35.6 TFLOPS FP16 suffices for small-scale fine-tuning or Stable Diffusion on 24 GB VRAM. It thrives in PCIe form factors for individual developers avoiding the B300's 1200W TDP and $7.11 average cost.