L40S vs T4

The L40S outperforms the T4 dramatically in floating-point compute: 362 TFLOPS FP16 versus 8.1 TFLOPS means over 44 times faster half-precision operations, ideal for AI training and inference. FP32 performance of 91 TFLOPS on the L40S contrasts with 8.1 TFLOPS on the T4, providing 11 times the single-precision throughput for scientific simulations. FP8 at 724 TFLOPS on the L40S further accelerates quantized inference models unavailable on the T4.

Memory differences profoundly affect real-world usage: 48 GB VRAM on the L40S supports batch sizes up to three times larger than the T4's 16 GB, reducing out-of-memory errors in large language models. The 864 GB/s bandwidth versus 320 GB/s enables 2.7 times faster data movement, minimizing bottlenecks in training loops and allowing higher throughput for diffusion models.

Power efficiency reveals trade-offs: the T4's 70W TDP suits dense deployments, but the L40S's 350W delivers far superior performance per watt in high-utilization scenarios, with cloud pricing from $0.40 per hour underscoring its value for intensive workloads.

Select the L40S for large-scale AI training or inference where 48 GB VRAM handles models exceeding 16 GB, such as 70B parameter LLMs. Its 362 TFLOPS FP16 performance accelerates fine-tuning by orders of magnitude over the T4's 8.1 TFLOPS, while 864 GB/s bandwidth supports massive batch sizes.

The L40S excels in generative tasks like Stable Diffusion, leveraging FP8 at 724 TFLOPS for rapid image generation, and offers better economics at $1.10 average hourly cost.

Choose the T4 for lightweight inference on small models fitting within 16 GB VRAM, such as basic computer vision tasks, where its 70W TDP minimizes power costs in edge or dense server setups. The 320 GB/s bandwidth suffices for low-latency serving without the L40S's 350W draw.

It suits budget-conscious deployments for legacy applications, with pricing from $0.53 per hour providing adequate 8.1 TFLOPS FP16 for non-demanding workloads.