H100 SXM5 vs RTX 3080

Compute throughput defines their capabilities for AI workloads: the H100 SXM5 achieves 1979 TFLOPS in FP16, enabling accelerated model training with mixed precision, while the RTX 3080 manages only 29.8 TFLOPS in FP16. This gap means training large neural networks on the H100 completes in fractions of the time required on the RTX 3080. FP32 performance follows suit at 67 TFLOPS for H100 versus 29.8 TFLOPS for RTX 3080, benefiting simulations needing full precision.

Memory specifications profoundly impact real-world usage: H100's 80-94 GB HBM3 VRAM supports enormous models and batch sizes that exceed the RTX 3080's 10-12 GB GDDR6X capacity. The H100's 3350 GB/s bandwidth versus 760 GB/s on the RTX 3080 allows sustained high throughput, minimizing stalls during data-intensive inference or training epochs. Larger batches on H100 reduce per-sample overhead, optimizing GPU utilization for production-scale deployments.

Power and interconnects further differentiate them: H100's 700W TDP and NVLink support multi-GPU scaling, contrasting the RTX 3080's 320W PCIe-only design. These traits make H100 ideal for clustered environments, while RTX 3080 fits single-node prototypes.

Opt for the H100 SXM5 in large-scale AI training scenarios: its 1979 TFLOPS FP16 and 80-94 GB VRAM handle billion-parameter LLMs that overwhelm the RTX 3080's 10-12 GB limits. Multi-GPU setups via NVLink excel for distributed training, achieving speeds unattainable on PCIe-bound alternatives.

Inference at production volumes favors H100: 3350 GB/s bandwidth supports massive batches, reducing latency compared to RTX 3080's 760 GB/s constraints.

Select the RTX 3080 for budget-conscious prototyping: at $0.06 per hour average $0.13, it delivers 29.8 TFLOPS FP16 for small model fine-tuning or inference, far cheaper than H100's $3.56 average.

Light workloads like single-image Stable Diffusion or entry-level scientific simulations thrive on its 10-12 GB VRAM and 320W efficiency, avoiding H100's 700W overkill.