B300 vs RTX 5090

The B300 dominates in AI-specific compute with 2250 TFLOPS FP16 performance versus the RTX 5090's 419 TFLOPS, enabling faster model training on large datasets. Its 4500 TFLOPS FP8 rate doubles the RTX 5090's 838 TFLOPS, accelerating inference for quantized models. The FP32 performance shows the RTX 5090 slightly ahead at 105 TFLOPS over the B300's 90 TFLOPS, which suits graphics or simulation tasks less critical for deep learning. In real-world terms, the B300's 288 GB VRAM supports training LLMs with billions of parameters without offloading, while the RTX 5090's 32 GB limits it to smaller models or lower resolutions. Memory bandwidth impacts batch sizes directly: 12000 GB/s on the B300 allows massive batches for efficient training throughput, whereas 1792 GB/s on the RTX 5090 constrains scaling in memory-bound workloads. Interconnects further differentiate them, as the B300's NVSwitch and NVLink enable seamless multi-GPU clusters, unlike the RTX 5090's PCIe 5.0. Power draw reflects this, with the B300 at 1200W TDP demanding robust cooling versus the RTX 5090's efficient 575W.

The B300 excels in large-scale AI deployments requiring extreme memory capacity. Its 288 GB HBM3e VRAM handles models exceeding 100 billion parameters, such as full LLM training runs, where the RTX 5090's 32 GB falls short. High 12000 GB/s bandwidth supports large batch sizes, reducing training time in production environments. Users in enterprise cloud setups benefit from NVLink interconnects for multi-GPU scaling across SXM form factors.

The RTX 5090 suits cost-conscious prototyping and smaller workloads. At $0.16 per hour from 19 offers, it provides accessible entry for fine-tuning or inference on models fitting within 32 GB GDDR7. Lower 575W TDP fits standard PCIe setups without datacenter infrastructure. Gamers or developers testing Stable Diffusion leverage its 105 TFLOPS FP32 edge over the B300's 90 TFLOPS.