MI355X vs Quadro P4000

The MI355X's 2300 TFLOPS FP16 and FP32 performance enables training of trillion-parameter models, while the P4000's 5.3 TFLOPS limits it to small networks from 2017-era benchmarks. Equal FP16 and FP32 rates on both GPUs indicate tensor core absence in the P4000, restricting mixed-precision training efficiency; the MI355X supports FP8 at 4600 TFLOPS for even faster inference on quantized models.

Memory differences dominate real-world usage: 288 GB HBM3e on the MI355X allows batch sizes exceeding 1000 for large language models, versus the P4000's 8 GB GDDR5 constraining batches to under 10. The 8000 GB/s bandwidth sustains data flow for these batches without stalling, unlike the P4000's 243 GB/s which bottlenecks even modest deep learning tasks.

Power draw further separates them: the MI355X's 750W TDP suits data center cooling, enabling sustained peaks, while the P4000's 105W fits laptops but throttles under prolonged loads.

Select the MI355X for large-scale LLM training or inference requiring over 288 GB VRAM, such as processing models with billions of parameters at 2300 TFLOPS FP16. Its 8000 GB/s bandwidth supports massive batch sizes in HPC simulations, ideal for research clusters using Infinity Fabric interconnects in OAM form factors.

Choose the Quadro P4000 for cost-sensitive CAD rendering or legacy software certified on Pascal GPUs, available at $0.51 per hour. Its 105W TDP and PCIe form factor suit low-power workstations handling 8 GB datasets at 5.3 TFLOPS FP32 for real-time visualization without modern AI demands.