H100 SXM5 vs RTX 4080

The H100's FP16 performance of 1979 TFLOPS vastly outpaces the RTX 4080's 48.7 TFLOPS, accelerating AI model training by factors of 40 times or more in mixed-precision workflows. FP32 rates show 67 TFLOPS for H100 against 48.7 TFLOPS for RTX 4080, a smaller gap relevant for scientific simulations requiring full precision. The H100's FP8 capability at 3958 TFLOPS enables ultra-fast inference on quantized large language models, reducing latency dramatically compared to the RTX 4080's lack of specified FP8 support.

Memory differences profoundly impact workloads: H100's 80 to 94 GB HBM3 supports massive batch sizes for training models like GPT-scale LLMs, avoiding out-of-memory errors common on RTX 4080's 16 GB GDDR6X. The 3350 GB/s bandwidth versus 717 GB/s ensures sustained throughput during data-intensive operations, allowing larger effective batch sizes and faster convergence in training loops. Power draw underscores efficiency: H100 at 700W handles enterprise scales, while RTX 4080's 320W fits edge or budget clouds.

These specs translate to real-world dominance in AI: H100 clusters via NVLink scale to multi-GPU training, unavailable on RTX 4080's PCIe-only form factor.

Professionals select the H100 SXM5 for large-scale LLM training and inference where 80 to 94 GB VRAM accommodates billion-parameter models without splitting. Its 3350 GB/s bandwidth and 1979 TFLOPS FP16 sustain high batch sizes, cutting training times from weeks to days. Enterprise users leverage NVLink and InfiniBand for clustered scalability, essential in HPC or production AI pipelines costing $1.47 to $3.69 per hour.

Developers choose the RTX 4080 for prototyping, fine-tuning small models, or Stable Diffusion generation on tight budgets at $0.11 to $0.26 per hour. Its 16 GB VRAM and 48.7 TFLOPS FP16 handle sub-7B parameter LLMs or gaming renders efficiently. Low 320W TDP suits single-node clouds without advanced interconnect needs.