H100 NVL vs RTX 4080

H100 NVL dominates AI workloads due to its 1979 TFLOPS FP16 performance, enabling faster model training than RTX 4080's 48.7 TFLOPS: training large neural networks processes batches in minutes rather than hours. The FP32 rating of 67 TFLOPS on H100 NVL slightly exceeds RTX 4080's 48.7 TFLOPS, benefiting simulations requiring single-precision math. FP8 at 3958 TFLOPS positions H100 NVL for efficient inference on quantized models, reducing latency for real-time applications. Memory bandwidth of 3350 GB/s on H100 NVL supports massive batch sizes and large datasets without bottlenecks, unlike 717 GB/s on RTX 4080 which limits scale in memory-intensive tasks. H100 NVL's 80 to 94 GB VRAM handles models exceeding 16 GB on RTX 4080, preventing out-of-memory errors in LLM fine-tuning or diffusion models.

Select H100 NVL for enterprise AI training, large-scale inference, or scientific simulations demanding over 80 GB VRAM. Its 3350 GB/s bandwidth and 1979 TFLOPS FP16 accelerate workflows like training billion-parameter LLMs, where RTX 4080 falls short. Cloud users prioritizing throughput over cost favor H100 NVL at $1.40 per hour starting price.

Choose RTX 4080 for cost-sensitive tasks like gaming, video rendering, or small-model inference under $0.11 per hour. Its 48.7 TFLOPS FP16 suffices for Stable Diffusion or fine-tuning models fitting in 16 GB VRAM. Lower 320W TDP suits edge deployments or budgets avoiding H100 NVL's $2.89 average hourly rate.