GB300 vs Quadro P4000

Floating-point performance reveals vast disparities suited to different eras. The GB300 achieves 2250 TFLOPS in FP16 for accelerated AI training, compared to the P4000's 5.3 TFLOPS, limiting the latter to modest model sizes. FP32 rates show GB300 at 90 TFLOPS against P4000's 5.3 TFLOPS, impacting precision-bound simulations. FP8 on GB300 reaches 4500 TFLOPS, ideal for inference on quantized models, a capability absent in P4000. Memory specs transform real-world usage: 288 GB HBM3e on GB300 supports massive batch sizes in LLM training, preventing out-of-memory errors common on P4000's 8 GB GDDR5. Bandwidth of 12000 GB/s enables rapid data throughput for large-scale inference, versus 243 GB/s on P4000, which bottlenecks high-volume workloads. Power draw reflects scale: GB300's 1400W TDP suits rack-scale systems, while P4000's 105W fits desktops.

The GB300 stands out for AI research and enterprise training pipelines requiring extreme scale. Its 288 GB HBM3e VRAM accommodates models exceeding hundreds of billions of parameters, and 2250 TFLOPS FP16 accelerates convergence. Datacenter interconnects like NVSwitch and NVLink enable multi-GPU scaling unavailable on P4000. Users in hyperscale cloud or on-premises HPC select GB300 for FP8 inference at 4500 TFLOPS.

The Quadro P4000 fits cost-sensitive, low-power professional workflows such as CAD rendering or legacy visualization software. At 105W TDP and $0.51 per hour average pricing across six providers, it runs efficiently in PCIe workstations without high cooling demands. Its 8 GB GDDR5 suffices for single-user tasks avoiding modern AI scale.