RTX 3080 vs RTX 4000 Ada

The RTX 3080 supplies 59.5 TFLOPS FP16 dense and 29.8 TFLOPS FP32 while the RTX 4000 Ada supplies 26.7 TFLOPS FP32 with no FP16 figure published. This FP16 dense to FP32 ratio of exactly 2 to 1 on the RTX 3080 supports training workloads that rely on half precision without loss of the full precision baseline. Memory bandwidth of 760 GB per second on the RTX 3080 versus 360 GB per second on the RTX 4000 Ada permits larger batch sizes in memory bound operations before capacity limits appear. The RTX 4000 Ada lists 427.6 TFLOPS FP8 with sparsity for inference paths that accept the sparsity label while the RTX 3080 lists 238 TOPS INT8 dense for equivalent dense integer workloads.

The RTX 3080 suits workloads that require the 760 GB per second memory bandwidth figure paired with 29.8 TFLOPS FP32. Training sessions that scale with the 59.5 TFLOPS FP16 dense rating benefit from this configuration over the lower bandwidth alternative. Power budgets that accommodate 320W TDP allow access to the 476 TOPS INT8 with sparsity rating for accelerated integer operations.

The RTX 4000 Ada suits workloads that require the 20 GB memory capacity figure paired with 130W TDP. Inference sessions that scale with the 427.6 TFLOPS FP8 with sparsity rating benefit from this configuration over the higher power alternative. Efficiency constraints that limit consumption to 130W allow sustained operation without the 320W TDP overhead of the other model.