A30 vs RTX 3090

Compute throughput defines key differences: RTX 3090 achieves 35.6 TFLOPS in FP16 and FP32, over three times the A30's 10.3 TFLOPS, accelerating deep learning training and inference by enabling larger batches or faster epochs in models like transformers. FP16 suits mixed-precision training, where RTX 3090 processes more samples per second; FP32 ensures precise scientific simulations.

Memory bandwidth remains close at 933 GB/s for A30 and 936 GB/s for RTX 3090, supporting similar maximum batch sizes in VRAM-bound tasks such as LLM inference with 24 GB capacity. HBM2 on A30 offers lower latency for certain enterprise workloads, but GDDR6X on RTX 3090 sustains high throughput in graphics-heavy inference like Stable Diffusion.

Power efficiency favors A30 at 165 W TDP versus 350 W, yielding better performance per watt (62.4 GFLOPS/W FP32) over RTX 3090's 101.7 GFLOPS/W, ideal for dense cloud deployments but secondary to raw speed in time-sensitive jobs.

The A30 suits power-constrained datacenters: its 165 W TDP consumes half the RTX 3090's 350 W, enabling denser racks without thermal throttling. Enterprise users prioritize HBM2's reliability for prolonged inference at 933 GB/s bandwidth, where 10.3 TFLOPS suffices for moderate-scale models fitting in 24 GB VRAM.

RTX 3090 excels in performance-critical tasks: 35.6 TFLOPS FP16/FP32 triples A30 speed for training, with cloud pricing from $0.08 per hour across 51 offers versus A30's unavailability. High-bandwidth GDDR6X at 936 GB/s supports bursty consumer workloads like generative AI on 24 GB VRAM.