GH200 vs Quadro P4000

The GH200's compute prowess dominates: its 1979 TFLOPS FP16 vastly outpaces the Quadro P4000's 5.3 TFLOPS, enabling faster AI model training where half-precision arithmetic accelerates matrix operations. In inference tasks, the GH200's 3958 TFLOPS FP8 performance supports ultra-efficient deployment of large language models, while the P4000 struggles with even modest workloads due to equivalent FP16 and FP32 at 5.3 TFLOPS.

Memory specifications further widen the gap. The GH200's 96 GB HBM3 and 4000 GB/s bandwidth handle massive batch sizes in deep learning, reducing data loading bottlenecks for models exceeding 8 GB, the P4000's limit. This allows the GH200 to process datasets 12 times larger without swapping, whereas the P4000's 243 GB/s bandwidth constrains it to small batches in training or inference.

Power efficiency reveals trade-offs: the GH200's 900 W TDP suits data centers, delivering 67 TFLOPS FP32 from high power, compared to the P4000's 105 W for 5.3 TFLOPS FP32. Real-world training times on GH200 shrink by orders of magnitude for FP16-heavy workloads.

The GH200 excels in large-scale AI and HPC workloads. Its 96 GB HBM3 VRAM and 4000 GB/s bandwidth support training LLMs with billions of parameters, where the 1979 TFLOPS FP16 throughput halves iteration times compared to older GPUs. Cloud users pay from $1.99 per hour for access to NVLink-C2C interconnects ideal for multi-GPU scaling in inference at 3958 TFLOPS FP8.

The Quadro P4000 suits budget visualization and light CAD tasks. At $0.51 per hour with 105 W TDP and PCIe form factor, it handles 8 GB datasets efficiently for FP32 workloads at 5.3 TFLOPS without excessive power draw. Legacy software optimized for Pascal architecture favors it over high-cost alternatives for non-AI professional rendering.