B300 SXM6 vs RTX 5090

The B300's 288 GB HBM3e VRAM dwarfs the RTX 5090's 32 GB GDDR7, allowing the B300 to handle models with billions more parameters without offloading to system RAM. This VRAM gap directly supports larger batch sizes in training, where the B300's 12000 GB/s bandwidth sustains high data flow compared to 1792 GB/s on the RTX 5090. In FP16 performance critical for AI training, the B300 achieves 2250 TFLOPS versus 419 TFLOPS on the RTX 5090, enabling up to 5 times faster iterations on large datasets. FP32 rates show the RTX 5090 at 105 TFLOPS slightly ahead of the B300's 90 TFLOPS, but this matters less in modern AI dominated by lower precisions. For inference, FP8 throughput on the B300 reaches 4500 TFLOPS against 838 TFLOPS, accelerating serving of quantized models. The B300's 1200W TDP and NVLink/NVSwitch interconnects facilitate multi-GPU clusters, unlike the RTX 5090's 575W and PCIe 5.0, limiting scale-out efficiency.

The B300 excels in enterprise scenarios requiring extreme scale, such as training LLMs with over 100 billion parameters that demand 288 GB VRAM per GPU. Datacenter users benefit from its 12000 GB/s bandwidth for massive batch sizes and NVLink for seamless multi-GPU communication. Cloud deployments averaging $6.44 per hour justify the cost for production workloads where the 2250 TFLOPS FP16 speed reduces training time significantly.

The RTX 5090 suits budget-conscious developers handling models under 30 GB, leveraging its 32 GB GDDR7 VRAM at a fraction of the cost from $0.20 per hour. Prototyping, fine-tuning smaller networks, or gaming-adjacent tasks like Stable Diffusion thrive on its 105 TFLOPS FP32 and PCIe compatibility for single-node setups. Lower 575W TDP eases desktop or small cluster integration without specialized cooling.