MI300X vs Quadro RTX 4000

The FP16 dense figure of 1307.4 TFLOPS on the MI300X exceeds the 57 TFLOPS dense FP16 on the Quadro RTX 4000 by a factor derived solely from those values. This ratio supports larger batch sizes during training and inference because the MI300X also supplies 5300 GB/s bandwidth compared to 416 GB/s. The FP32 value of 163.4 TFLOPS on the MI300X versus 7.1 TFLOPS on the Quadro RTX 4000 indicates that mixed precision paths using dense FP16 reduce computation time relative to pure FP32 paths on either GPU. Memory bandwidth of 5300 GB/s enables the MI300X to sustain higher throughput for sparse FP16 at 2614.9 TFLOPS while the Quadro RTX 4000 lacks published sparse figures. Dense FP8 at 2614.9 TFLOPS on the MI300X further widens the gap for quantized inference workloads against the Quadro RTX 4000.

The MI300X suits LLM training and inference tasks that require 192 GB HBM3 VRAM to hold models exceeding 8 GB capacity. Its 5300 GB/s bandwidth and dense FP16 at 1307.4 TFLOPS allow larger batch processing than the Quadro RTX 4000 can accommodate. Workloads needing Infinity Fabric interconnect and 2614.9 TFLOPS dense FP8 benefit from the MI300X at 750W TDP.

The Quadro RTX 4000 fits scientific computing or fine tuning jobs constrained to 125W TDP and PCIe 3.0 slots. Its 8 GB GDDR6 VRAM and 7.1 TFLOPS FP32 handle smaller models where 416 GB/s bandwidth suffices without exceeding power limits of the MI300X.