A10 vs A30

The A10's FP16 and FP32 performance of 31.2 TFLOPS each outperforms the A30's 10.3 TFLOPS by a factor of three: this advantage accelerates deep learning training phases reliant on dense matrix operations, such as gradient computations in LLM fine-tuning. Inference workloads similarly benefit, as higher throughput reduces latency for batched predictions on models up to 24 GB.

The A30's superior memory bandwidth of 933 GB/s versus 600 GB/s enables larger batch sizes in memory-bound applications, minimizing data transfer bottlenecks during forward passes. HBM2's lower latency complements NVLink for multi-GPU setups, ideal for distributed training where interconnect speed matters. However, the A30's lower TDP of 165 W compared to the A10's 150 W offers marginal efficiency gains in bandwidth-heavy tasks.

In practice, compute-intensive workflows favor the A10 for faster wall-clock times, while bandwidth-limited scenarios leverage the A30 to sustain higher throughput per step.

Select the A10 for compute-dominant workloads like model training and real-time inference on 24 GB models. Its 31.2 TFLOPS FP32 performance processes iterations three times faster than the A30's 10.3 TFLOPS, ideal for single-GPU setups via PCIe.

Availability drives choice too: live cloud offers start at $0.60 per hour, averaging $1.06 per hour, versus no listings for the A30.

Opt for the A30 in memory-intensive applications demanding high bandwidth, such as large-batch training or simulations fitting within 24 GB HBM2. The 933 GB/s bandwidth supports 55 percent more data movement than the A10's 600 GB/s, enabling bigger batches without OOM errors.

NVLink interconnect suits multi-GPU scaling for distributed workloads, where PCIe on the A10 limits peer communication.