H200 SXM vs RTX 5090

H200 SXM excels in FP16 compute at 1979 TFLOPS, ideal for training large neural networks where half-precision accelerates matrix multiplications without significant accuracy loss. RTX 5090 trails at 419 TFLOPS in FP16 but leads in FP32 with 105 TFLOPS over H200 SXM's 67 TFLOPS, suiting graphics rendering or scientific simulations reliant on single-precision arithmetic. FP8 performance follows suit: H200 SXM delivers 3958 TFLOPS for ultra-efficient quantized inference, doubling RTX 5090's 838 TFLOPS. Memory bandwidth defines real-world limits: H200 SXM's 4800 GB/s supports enormous batch sizes in model training, enabling faster convergence on datasets exceeding 100 GB, whereas RTX 5090's 1792 GB/s constrains it to smaller batches prone to out-of-memory errors. This bandwidth edge allows H200 SXM to process larger models in inference pipelines. Power draw differs at 700W for H200 SXM versus 575W for RTX 5090, impacting density in racks or desktops.

Choose NVIDIA H200 SXM for large-scale LLM training or inference requiring over 100 GB VRAM, as its 141 GB HBM3e capacity handles models like 1T-parameter giants without partitioning. Multi-GPU clusters benefit from NVLink and InfiniBand interconnects, scaling to thousands of GPUs for distributed training at 1979 TFLOPS FP16 per unit. Enterprise environments prioritize its 4800 GB/s bandwidth for high-throughput batch processing in production inference.

NVIDIA GeForce RTX 5090 suits cost-sensitive personal or small-team workflows, with pricing from $0.13 per hour enabling experimentation on 32 GB GDDR7 VRAM. Gamers and creators leverage its 105 TFLOPS FP32 for real-time rendering or Stable Diffusion tasks, where PCIe form factor fits desktops. FP8 inference at 838 TFLOPS supports efficient local deployment without datacenter overhead.