H100 SXM5 vs RTX 5080

The H100 SXM5's FP16 performance reaches 1979 TFLOPS compared to the RTX 5080's 56.3 TFLOPS: this gap translates to roughly 35 times faster tensor operations, accelerating deep learning training and inference on large neural networks. For FP32 tasks, the H100's 67 TFLOPS provides a modest edge over the RTX 5080's 56.3 TFLOPS, benefiting general-purpose computing while the H100's FP8 at 3958 TFLOPS optimizes quantized inference for massive language models.

Memory capacity and bandwidth define workload feasibility: the H100's 80 to 94 GB HBM3 versus 16 GB GDDR7 enables training models with billions of parameters without fragmentation, supporting batch sizes up to 10 times larger. Its 3350 GB/s bandwidth reduces data bottlenecks during gradient computations, unlike the RTX 5080's 960 GB/s which suits smaller batches. The 700W TDP on H100 demands robust cooling, while the RTX 5080's 360W fits lighter deployments, impacting density in cloud instances.

Opt for the H100 SXM5 in large-scale AI training scenarios: its 80 to 94 GB VRAM and 3350 GB/s bandwidth handle models exceeding 100 billion parameters, enabling efficient distributed training via NVLink. Cloud users processing petabyte-scale datasets benefit from 1979 TFLOPS FP16, reducing epochs from days to hours at $3.52 per hour average.

Enterprise inference on high-throughput clusters favors the H100: 3958 TFLOPS FP8 supports quantized LLMs serving thousands of queries per second without latency spikes.

Choose the RTX 5080 for budget-conscious graphics and lighter AI tasks: at $0.38 per hour average, its 56.3 TFLOPS FP32 excels in real-time rendering and gaming workloads. The 16 GB GDDR7 and 360W TDP suit single-user cloud desktops or prototyping.

Small-scale inference and fine-tuning thrive on the RTX 5080: 960 GB/s bandwidth processes models under 7 billion parameters swiftly, offering 6 times lower cost than H100 for non-enterprise needs.