B300 SXM6 vs RTX 5080

The B300's FP16 throughput of 2250 TFLOPS vastly outpaces the RTX 5080's 56.3 TFLOPS: this advantage accelerates deep learning training and inference, where half-precision computations dominate. Its FP32 performance of 90 TFLOPS also leads the RTX 5080's 56.3 TFLOPS, though the B300's FP16-to-FP32 ratio of 25:1 signals AI specialization over general graphics. The RTX 5080 maintains parity in FP16 and FP32 at 56.3 TFLOPS each, suiting balanced workloads like gaming or simulations.

Memory capacity and bandwidth reshape practical limits: the B300's 288 GB HBM3e supports models with hundreds of billions of parameters or enormous batch sizes, while 16 GB GDDR7 on the RTX 5080 restricts it to smaller datasets. The 12000 GB/s bandwidth on B300 enables rapid data movement for high-throughput training, compared to 960 GB/s on RTX 5080 which bottlenecks large-scale operations. These specs translate to the B300 handling 30 times more memory traffic.

Power and form factors further differentiate deployment: B300's 1200W TDP and SXM form with NVSwitch/NVLink interconnects fit cluster environments, whereas RTX 5080's 360W TDP and PCIe slot integrate into single-node setups.

The B300 excels in large-scale AI model training: its 288 GB VRAM accommodates models over 100 billion parameters without fragmentation, and 2250 TFLOPS FP16 speeds convergence. Multi-GPU setups via NVLink and NVSwitch scale to thousands of GPUs seamlessly.

For high-volume inference, 4500 TFLOPS FP8 and 12000 GB/s bandwidth minimize latency on enterprise deployments, justifying $2.45 per hour starting costs.

The RTX 5080 fits budget-conscious development and gaming: at $0.25 per hour, it delivers 56.3 TFLOPS FP16 for prototyping small models or creative tasks like image generation. Its 360W TDP and PCIe form factor simplify workstation integration without datacenter infrastructure.

Solo users prefer it for fine-tuning under 10 billion parameters or real-time graphics, where 16 GB VRAM and 960 GB/s bandwidth suffice.