H100 SXM5 vs RTX 4080 SUPER

The NVIDIA H100 SXM5 outperforms the NVIDIA GeForce RTX 4080 SUPER dramatically in AI-relevant precisions, with 1979 TFLOPS FP16 and 3958 TFLOPS FP8 enabling up to 40 times faster matrix operations for deep learning compared to the RTX 4080 SUPER's 48.7 TFLOPS FP16. This FP16 and FP8 advantage accelerates neural network training and inference, where the H100 SXM5 processes larger models without precision loss. FP32 performance stands at 67 TFLOPS for the H100 SXM5 versus 48.7 TFLOPS for the RTX 4080 SUPER, a smaller gap suited to scientific simulations but still favoring the datacenter GPU. Memory bandwidth of 3350 GB/s on the H100 SXM5 supports massive batch sizes and high-throughput data movement, preventing bottlenecks in transformer models that exceed the RTX 4080 SUPER's 717 GB/s limit. The H100 SXM5's 80 to 94 GB HBM3 VRAM handles models over 70 billion parameters, while the RTX 4080 SUPER's 16 GB GDDR6X restricts it to smaller batches or quantized inference. In real-world terms, the H100 SXM5 scales for enterprise training runs, whereas the RTX 4080 SUPER suits prototyping with 320W efficiency.

The NVIDIA H100 SXM5 excels in large-scale AI training and inference requiring over 16 GB VRAM, such as full fine-tuning of 70B parameter LLMs, leveraging its 80 to 94 GB HBM3 and 3350 GB/s bandwidth for batch sizes impossible on the RTX 4080 SUPER. Datacenter environments benefit from its 1979 TFLOPS FP16 and NVLink interconnect for multi-GPU clusters. Cloud users facing deadlines prioritize its 3958 TFLOPS FP8 for rapid deployment at $0.80 per hour starting price.

The NVIDIA GeForce RTX 4080 SUPER fits budget prototyping, Stable Diffusion generation, or inference on models under 16 GB VRAM, offering 48.7 TFLOPS FP16 at $0.17 per hour. Its 320W TDP and PCIe form factor suit single-user workstations or gaming-adjacent tasks without datacenter overhead. Experimenters value its 717 GB/s bandwidth for quick iterations where H100 SXM5 costs average $3.58 per hour.