H200 SXM vs Quadro P4000

The H200 vastly outpaces the Quadro P4000 in compute performance: its FP16 rating reaches 1979 TFLOPS compared to 5.3 TFLOPS, a 373-fold increase ideal for AI training where half-precision accelerates gradient computations. FP32 performance shows the H200 at 67 TFLOPS against 5.3 TFLOPS, benefiting simulation and rendering tasks requiring single-precision accuracy. This delta means training large models on the H200 completes in minutes what takes hours or days on the P4000.

Memory specifications further widen the gap: the H200's 141 GB HBM3e VRAM supports batch sizes up to thousands for LLMs, while the P4000's 8 GB GDDR5 limits batches to dozens, causing out-of-memory errors in modern workflows. Bandwidth at 4800 GB/s on the H200 versus 243 GB/s enables rapid data movement, reducing bottlenecks in inference pipelines. For inference, the H200's FP8 at 3958 TFLOPS allows low-precision serving at scales unattainable on the P4000.

Power efficiency reflects architecture maturity: despite 700W TDP, the H200 delivers over 2.8 TFLOPS per watt in FP16, dwarfing the P4000's 0.05 TFLOPS per watt at 105W.

Choose the H200 for AI model training and large-scale inference: its 141 GB VRAM handles models exceeding 100 billion parameters, and 1979 TFLOPS FP16 throughput accelerates convergence. Datacenter users benefit from NVLink interconnects and PCIe 5.0 for multi-GPU scaling unavailable on the P4000.

Cloud deployments at $1.19 per hour suit high-throughput needs like LLM fine-tuning, where the 4800 GB/s bandwidth supports massive batches without latency spikes.

Select the Quadro P4000 for legacy workstation tasks or ultra-low budgets: at $0.51 per hour and 105W TDP, it fits CAD visualization and light rendering without datacenter infrastructure. Its PCIe form factor integrates easily into older desktops for infrequent use.

Budget-conscious users running small models under 1 GB VRAM prefer it, as 5.3 TFLOPS FP32 suffices for non-AI graphics without the H200's overhead.