H200 SXM vs TITAN Xp

Compute performance reveals stark disparities suited to different eras of workloads. The H200 achieves 1979 TFLOPS in FP16 and 67 TFLOPS in FP32, far exceeding the TITAN Xp's 12.1 TFLOPS in both formats: this FP16 to FP32 ratio on H200 optimizes mixed-precision training, where models use half-precision for speed while maintaining single-precision accuracy, reducing training times by orders of magnitude for large neural networks. The TITAN Xp's equal FP16 and FP32 rates limit it to balanced but low-throughput tasks.

Memory bandwidth dictates handling of large datasets: H200's 4800 GB/s supports massive batch sizes in LLM training, fitting models up to 141 GB without swapping, whereas TITAN Xp's 548 GB/s constrains batches to small scales on 12 GB VRAM, risking out-of-memory errors in modern inference. H200's FP8 at 3958 TFLOPS further accelerates quantized inference, enabling real-time serving of billion-parameter models. Power draw reflects this: 700W TDP for H200 versus 250W for TITAN Xp, trading efficiency for peak datacenter output.

Opt for the H200 in AI-driven enterprise scenarios requiring vast resources. Its 141 GB HBM3e VRAM handles full-parameter fine-tuning of large language models, while 4800 GB/s bandwidth sustains high-throughput training at 1979 TFLOPS FP16. Cloud availability from $1.19 per hour suits scalable deployments via NVLink and InfiniBand.

Select the TITAN Xp for legacy consumer applications or power-constrained local machines. At 250W TDP and 12 GB GDDR5X, it suffices for lightweight inference or gaming on PCIe setups, delivering 12.1 TFLOPS FP32 without cloud dependency. No live offers make it ideal only if hardware exists on-site.