H200 SXM vs Tesla V100 32GB

H200's FP16 throughput of 1979 TFLOPS vastly outpaces V100's 125 TFLOPS, accelerating deep learning training by enabling faster iterations on large datasets. FP32 performance follows suit at 67 TFLOPS versus 15.7 TFLOPS, benefiting general-purpose computing and simulations. The FP8 capability of 3958 TFLOPS on H200 further optimizes inference for modern quantized models, an area where V100 lacks direct equivalence.

Memory specifications transform real-world usability: 141 GB HBM3e VRAM on H200 supports models exceeding 32 GB, preventing out-of-memory errors common on V100. Bandwidth at 4800 GB/s versus 900 GB/s sustains larger batch sizes during training, reducing per-iteration time and improving throughput.

Power draw differs at 700W TDP for H200 compared to 300W for V100, implying higher infrastructure costs but justified by performance density. Interconnects like PCIe 5.0 and InfiniBand on H200 enhance multi-GPU scaling over V100's PCIe 3.0 and NVLink.

Opt for H200 SXM in scenarios demanding massive scale, such as training large language models requiring over 32 GB VRAM. Its 141 GB capacity and 4800 GB/s bandwidth handle enormous batch sizes, while 1979 TFLOPS FP16 speeds convergence. Current pricing from $1.19 per hour suits enterprises prioritizing throughput over initial cost.

Select V100 32GB for budget-constrained projects with modest memory needs under 32 GB HBM2. At $0.29 per hour average $1.01, it delivers 125 TFLOPS FP16 for prototyping or inference on smaller models. Lower 300W TDP fits power-sensitive environments without sacrificing basic NVLink interconnects.