GH200 vs RTX 5080

The GH200 dominates in raw compute: its 1979 TFLOPS FP16 throughput dwarfs the RTX 5080's 56.3 TFLOPS, enabling faster AI training where half-precision dominates. The FP16 to FP32 delta reveals specialization: GH200's FP32 at 67 TFLOPS supports precise simulations, but RTX 5080 balances both at 56.3 TFLOPS, suiting graphics and general compute without Hopper's tensor core emphasis.

Memory specs define real-world viability: GH200's 96 GB HBM3 and 4000 GB/s bandwidth handle enormous batch sizes in LLM training, reducing data movement bottlenecks. RTX 5080's 16 GB GDDR7 at 960 GB/s limits it to smaller models or inference, where lower latency matters over throughput.

Power efficiency shifts contexts: GH200's 900W TDP demands robust cooling for SXM form factors with NVLink-C2C interconnects, ideal for multi-GPU scaling. RTX 5080's 360W in PCIe form suits edge deployments, trading peak performance for accessibility.

Opt for the GH200 in large-scale AI training and inference requiring over 96 GB VRAM, such as training billion-parameter LLMs where 4000 GB/s bandwidth sustains massive batches. Its 1979 TFLOPS FP16 and 3958 TFLOPS FP8 excel in data center environments with NVLink-C2C for multi-node coherence, justifying $1.99 per hour starting price for high-throughput HPC.

Scientific computing benefits from 67 TFLOPS FP32 and PCIe 5.0, enabling complex simulations unattainable on consumer hardware.

Select the RTX 5080 for cost-sensitive tasks like gaming, Stable Diffusion, or small-scale inference at $0.25 per hour. Its 16 GB GDDR7 suffices for models under that threshold, with 56.3 TFLOPS FP16/FP32 balancing creative workloads on 360W TDP.

Prosumer fine-tuning or local development favors PCIe simplicity over enterprise overhead.