L40 vs T4

Compute performance defines the core difference: the L40's 90.5 TFLOPS in FP16 and FP32 provides over 11 times the throughput of the T4's 8.1 TFLOPS. This translates to faster model training, where epochs complete in minutes rather than hours, and quicker inference for real-time applications.

VRAM capacity impacts workload feasibility: 48 GB on the L40 supports large batch sizes for training massive models, avoiding out-of-memory errors common on the T4's 16 GB. Higher memory bandwidth of 864 GB/s on the L40 versus 320 GB/s minimizes data transfer bottlenecks, enabling efficient handling of high-resolution datasets or complex simulations.

Power efficiency varies by use: the T4's 70W TDP suits dense deployments, but the L40's 300W enables peak performance for demanding tasks without proportional cost increases in cloud environments.

The L40 excels in high-memory AI workloads. Its 48 GB GDDR6 VRAM accommodates large language models during training or inference, where the T4's 16 GB falls short. Users benefit from 90.5 TFLOPS FP16 performance for rapid iteration in fine-tuning or generative tasks.

Cloud operators prefer the L40 for its pricing: average $0.89 per hour across 14 offers supports scalable clusters better than the T4's $1.66 average.

The T4 fits low-power inference scenarios. Its 70W TDP enables dense server packing, reducing cooling costs compared to the L40's 300W. For lightweight models within 16 GB VRAM, 8.1 TFLOPS suffices without overprovisioning.

Budget-conscious users value the T4's starting price of $0.53 per hour for non-critical tasks like basic computer vision serving.