H200 NVL vs Quadro P4000

The H200's FP16 performance of 1979 TFLOPS vastly exceeds the P4000's 5.3 TFLOPS, accelerating deep learning training where half-precision computations dominate. FP32 rates further highlight the gap: 67 TFLOPS on H200 versus 5.3 TFLOPS on P4000, benefiting single-precision scientific simulations and rendering. This delta means training times shrink from days to hours on H200 for large models. Memory capacity defines feasibility: 141 GB HBM3e on H200 supports batch sizes for billion-parameter LLMs, while 8 GB GDDR5 on P4000 limits to small models or low batches. Bandwidth at 4800 GB/s on H200 sustains high throughput without bottlenecks, unlike 243 GB/s on P4000, which throttles data movement in memory-intensive inference. Overall, H200 handles modern AI scales; P4000 fits basic tasks.

Choose the H200 for AI training, large-scale inference, or HPC where 141 GB VRAM and 4800 GB/s bandwidth enable massive models. Its 1979 TFLOPS FP16 suits LLM fine-tuning or Stable Diffusion at scale. At average $2.39 per hour, it justifies cost for workloads needing NVLink interconnects and 700W TDP capacity.

Select the Quadro P4000 for light CAD, legacy visualization, or entry-level compute on tight budgets, with average pricing at $0.51 per hour. Its 105W TDP fits low-power edge deployments or simple FP32 tasks at 5.3 TFLOPS. The 8 GB VRAM handles basic rendering without overkill.