GB300 SXM6 vs RTX 5090

The GB300 SXM6 vastly outpaces the RTX 5090 in AI-relevant compute: its 2250 TFLOPS FP16 rate is over five times the RTX 5090's 419 TFLOPS, accelerating deep learning training and inference where half-precision dominates. FP8 performance follows suit at 4500 TFLOPS versus 838 TFLOPS, suiting quantized inference for large language models. FP32 rates are comparable, with GB300 at 90 TFLOPS and RTX 5090 at 105 TFLOPS, so general-purpose computing shows less disparity. Memory capacity defines the real-world gap: 288 GB HBM3e on GB300 supports models exceeding 32 GB GDDR7 on RTX 5090, preventing out-of-memory errors in large-batch training. Bandwidth of 12000 GB/s on GB300 enables batch sizes up to seven times larger than the RTX 5090's 1792 GB/s, reducing training epochs and improving throughput in memory-bound tasks. The GB300's 1400W TDP reflects its scale, contrasting the RTX 5090's efficient 575W for smaller deployments.

The GB300 SXM6 excels in hyperscale AI training for models requiring over 288 GB VRAM, such as trillion-parameter LLMs, where its 12000 GB/s bandwidth sustains massive batches. NVLink and NVSwitch interconnects make it essential for multi-GPU clusters scaling to thousands of units. Datacenter operators prioritize it for production inference at 4500 TFLOPS FP8, unavailable on consumer cards.

The RTX 5090 suits cost-sensitive users with its cloud pricing from $0.13 per hour, averaging $0.63 per hour across 30 offers, far below enterprise datacenter costs. It handles fine-tuning or inference for models under 32 GB VRAM efficiently at 419 TFLOPS FP16. PCIe form factor and 575W TDP fit workstations or small clusters without NVLink infrastructure.