A30 vs A40

A40 demonstrates superior raw compute power: its 37.4 TFLOPS FP16 and FP32 ratings exceed A30's 10.3 TFLOPS by over 3.6 times, accelerating deep learning training and inference workloads that rely on tensor core throughput. In training, this delta shortens epochs for models using mixed precision, while inference benefits from higher tokens per second in batch processing.

Memory configurations impact real-world scalability. A30's 933 GB/s bandwidth from 24 GB HBM2 supports larger batch sizes in bandwidth-limited tasks like dense neural networks, reducing latency in memory-bound inference. A40's 48 GB GDDR6 capacity at 696 GB/s enables handling of expansive models, such as those exceeding 24 GB, though lower bandwidth may constrain peak throughput in high-data-movement scenarios.

Power efficiency further differentiates them: A30's 165W TDP allows denser server packing than A40's 300W, influencing total cost of ownership in large-scale clusters.

A30 proves ideal for power-constrained data centers: its 165W TDP supports higher GPU density per rack compared to A40's 300W draw. The 933 GB/s memory bandwidth excels in applications dominated by data transfer, such as certain scientific computing kernels or inference with moderate model sizes fitting within 24 GB HBM2.

A40 stands out for memory-intensive AI tasks: 48 GB GDDR6 VRAM accommodates large language models or high-resolution generative workloads infeasible on A30's 24 GB. The 37.4 TFLOPS FP16 performance drives faster training throughput, making it preferable for production-scale deep learning pipelines available from $0.24 per hour.