A100 SXM4 80GB vs Tesla T4

The A100 SXM4 80GB outperforms the T4 dramatically in compute capabilities: its FP16 performance reaches 312 TFLOPS versus the T4's 8.1 TFLOPS, enabling faster mixed-precision training for deep learning models. FP32 performance also favors the A100 at 19.5 TFLOPS against the T4's 8.1 TFLOPS, benefiting general-purpose simulations and single-precision tasks. This FP16 to FP32 delta on the A100 accelerates training pipelines where half-precision dominates, reducing epochs by factors tied to the 38x FP16 advantage. In inference, the A100 handles larger models without quantization due to superior throughput. Memory bandwidth defines batch size limits: the A100's 2039 GB/s supports massive batches for stable training gradients, while the T4's 320 GB/s restricts it to smaller batches, increasing latency in memory-bound scenarios. Power consumption reflects this: the A100 draws 400W for peak output, but the T4's 70W suits dense deployments. Real-world impacts include the A100 training ResNet-50 in minutes versus hours on the T4.

The A100 SXM4 80GB excels in scenarios demanding high VRAM and compute, such as training large language models requiring over 40 GB memory. Its 312 TFLOPS FP16 and 2039 GB/s bandwidth enable efficient handling of billion-parameter models with large batch sizes. Cloud users benefit from 24 live offers starting at $0.67 per hour for scalable HPC clusters.

The T4 suits lightweight inference and edge computing where 70W TDP minimizes costs in multi-GPU servers. Its 16 GB GDDR6 handles standard vision models at 8.1 TFLOPS FP16 with low pricing from $0.53 per hour. Deployments prioritizing density over peak performance favor the T4's PCIe form factor.