GB300 SXM6 vs RTX 4080 SUPER

Compute disparities define capabilities: the GB300 SXM6's 2250 TFLOPS FP16 crushes the RTX 4080 SUPER's 48.7 TFLOPS, slashing training times for neural networks by over 46 times. FP8 at 4500 TFLOPS optimizes inference on quantized models, while FP32's 90 TFLOPS edges out 48.7 TFLOPS for simulation tasks.

Memory transforms real-world use: 288 GB HBM3e versus 16 GB GDDR6X loads full large language models without model parallelism, and 12000 GB/s bandwidth sustains batch sizes 16 times larger, accelerating convergence. RTX 4080 SUPER limits to smaller models or inference with frequent swapping.

Interconnects and power scale deployments: NVLink on GB300 SXM6 enables terascale clusters, but 1400W TDP requires enterprise infrastructure. RTX 4080 SUPER's PCIe and 320W suit edge or multi-GPU via standard slots without specialized cooling.

Select the GB300 SXM6 for hyperscale AI: 288 GB VRAM fits trillion-parameter models intact, and 12000 GB/s bandwidth supports enormous batches in training. NVLink interconnects cluster thousands for exascale inference at 4500 TFLOPS FP8.

Datacenters prioritize it over consumer cards for sustained 2250 TFLOPS FP16 in production ML pipelines.

The RTX 4080 SUPER serves cost-sensitive prototyping: cloud pricing from $0.17 per hour enables accessible 48.7 TFLOPS FP16 for fine-tuning or inference on models under 16 GB.

Its 320W TDP and PCIe form factor integrate easily into workstations or small clusters for gaming, Stable Diffusion, or light scientific computing without datacenter overhead.