A10 vs GTX 1080

The A10 outperforms the GTX 1080 dramatically in raw compute: 31.2 TFLOPS versus 8.9 TFLOPS in both FP16 and FP32, equating to a 3.5-fold increase. This delta translates to faster training times for deep learning models, where FP32 handles precise gradient computations and FP16 accelerates mixed-precision workflows common in frameworks like PyTorch. Inference benefits similarly, with the A10 processing larger models or higher throughputs without proportional slowdowns.

Memory bandwidth reveals another edge for the A10: 600 GB/s compared to 320 GB/s, nearly doubling data throughput. This supports larger batch sizes in training, reducing overhead and enabling models that exceed the GTX 1080's 8-11 GB VRAM limit, such as those requiring over 12 GB for effective operation. The A10's 24 GB capacity prevents out-of-memory errors in VRAM-intensive tasks, while the GTX 1080 struggles with contemporary large language models.

Power efficiency favors the A10 at 150W TDP versus 180W, yielding better performance per watt: approximately 0.208 TFLOPS/W for FP32 on A10 against 0.049 TFLOPS/W on GTX 1080. In cloud rentals, this efficiency lowers sustained costs for prolonged jobs despite higher hourly rates.

The A10 excels in memory-constrained workloads like training or fine-tuning large language models exceeding 8-11 GB VRAM, leveraging its 24 GB GDDR6 to handle models such as Llama 7B without quantization. High bandwidth of 600 GB/s supports batch sizes up to 2-3 times larger than the GTX 1080's 320 GB/s limit, accelerating convergence in datasets over 100 GB. Datacenter reliability suits production inference at scale.

Cloud users prioritizing 31.2 TFLOPS performance over cost select the A10 for Stable Diffusion or scientific simulations demanding 3.5x faster FP16 tensor operations.

The GTX 1080 fits budget-conscious setups for lightweight inference on models under 8 GB, such as MobileNet, where its $0.30 per hour starting price halves the A10's $0.60. Legacy Pascal codebases or gaming-related ML tasks run efficiently without needing Ampere-specific optimizations.

Short prototyping runs or educational projects benefit from the GTX 1080's 8.9 TFLOPS at lower average $0.45 per hour, avoiding overprovisioning for sporadic 180W loads.