A30 vs T4

The A30's 10.3 TFLOPS in FP16 and FP32 exceeds the T4's 8.1 TFLOPS by 27 percent, accelerating AI training and inference workloads. This compute advantage shines in deep learning where tensor core utilization matters: training large models benefits from the A30's edge in sustained throughput.

Memory differences define real-world limits: the A30's 24 GB HBM2 versus the T4's 16 GB GDDR6 allows larger models or batch sizes without swapping to host memory. Bandwidth disparity is stark at 933 GB/s for the A30 against 320 GB/s for the T4, enabling the A30 to handle bigger batches in inference without latency spikes from memory bottlenecks.

Power efficiency favors the T4 at 70W TDP compared to 165W, ideal for dense deployments. However, the A30's Ampere advancements yield better utilization in mixed-precision tasks, where FP16 dominance supports faster convergence in training pipelines.

Select the A30 for memory-bound workloads requiring 24 GB HBM2 VRAM, such as fine-tuning large language models exceeding 16 GB. Its 933 GB/s bandwidth sustains high batch sizes in inference servers handling enterprise-scale deployments.

The NVLink interconnect and 10.3 TFLOPS performance make the A30 superior for multi-GPU scientific simulations or Stable Diffusion generation with high-resolution outputs.

Choose the T4 for cost-effective inference on models fitting within 16 GB GDDR6, leveraging pricing from $0.53 per hour. Its 70W TDP suits edge or dense cloud instances minimizing power costs.

The T4 excels in lightweight fine-tuning or batch inference where 8.1 TFLOPS suffices and 320 GB/s bandwidth avoids overprovisioning.