A10 vs RTX 3080

FP16 performance measures 31.2 TFLOPS on the A10 and 29.8 TFLOPS on the RTX 3080, giving the A10 a 4.7 percent advantage for half-precision operations common in deep learning training and inference. FP32 throughput matches at 31.2 TFLOPS and 29.8 TFLOPS respectively, supporting single-precision scientific computing effectively on both. This close compute parity means workloads scale similarly, but real-world speed hinges on memory.

The A10's 24 GB GDDR6 VRAM enables batch sizes up to twice those of the RTX 3080's 10 to 12 GB GDDR6X, crucial for training large language models without splitting across GPUs. RTX 3080 counters with 760 GB/s bandwidth, 26.7 percent above A10's 600 GB/s, accelerating data-heavy inference and image generation where memory access dominates. Lower TDP of 150 W on A10 reduces cooling needs versus 320 W on RTX 3080, aiding dense cloud deployments.

In training scenarios, A10 handles memory-bound models better; RTX 3080 excels in bandwidth-limited tasks like high-throughput inference.

The A10 excels in memory-intensive applications such as training or fine-tuning large language models exceeding 12 GB, leveraging its 24 GB GDDR6 VRAM. Its 150 W TDP suits power-constrained environments, and 31.2 TFLOPS FP16 performance edges out the RTX 3080 for sustained datacenter loads. Choose A10 when model size demands exceed RTX 3080 capacity, despite higher $1.06 per hour average pricing.

The RTX 3080 fits cost-sensitive projects with its $0.06 per hour starting price, averaging $0.15 per hour across more providers. Higher 760 GB/s bandwidth benefits bandwidth-bound tasks like Stable Diffusion generation, and 29.8 TFLOPS FP16 suffices for most inference. Select RTX 3080 for high-volume, smaller-model workloads where 10 to 12 GB VRAM meets needs and savings outweigh A10's memory premium.