GB300 SXM6 vs RTX 5080

The GB300's FP16 performance of 2250 TFLOPS vastly outpaces the RTX 5080's 56.3 TFLOPS, enabling faster AI model training where half-precision computations dominate. Its FP32 rate of 90 TFLOPS slightly exceeds the RTX 5080's 56.3 TFLOPS, benefiting general-purpose computing tasks. FP8 at 4500 TFLOPS on the GB300 accelerates inference for quantized large language models, a capability absent in the consumer card. Memory bandwidth defines workload feasibility: the GB300's 12000 GB/s supports batch sizes for models exceeding 100 billion parameters, while the RTX 5080's 960 GB/s limits it to smaller batches around 1-10 million parameters. In real-world terms, the GB300 handles enterprise training runs in hours that would take days on the RTX 5080, though the latter suffices for prototyping due to lower latency in single-GPU setups.

Opt for the GB300 in large-scale LLM training or inference deployments requiring over 288 GB VRAM per GPU. Its 12000 GB/s bandwidth and NVLink interconnect enable multi-GPU clusters for models like those with trillions of parameters. Datacenter environments with NVSwitch benefit from its 1400W TDP efficiency at scale.

Select the RTX 5080 for cost-sensitive tasks like gaming, Stable Diffusion, or small-scale fine-tuning, available from $0.25 per hour in cloud providers. Its 360W TDP and PCIe form factor suit single-user workstations or edge inference with 16 GB VRAM. Prototyping fits perfectly within its 56.3 TFLOPS FP16 limits.