L4 vs L40S

Compute throughput defines the core disparity: the L40S delivers 362 TFLOPS FP16 versus the L4's 121 TFLOPS, enabling roughly three times faster tensor operations in mixed-precision training. FP32 performance at 91 TFLOPS on L40S outpaces the L4's 30.3 TFLOPS, benefiting general-purpose computing and simulations requiring single-precision arithmetic.

Memory subsystems amplify real-world impacts. The L40S's 864 GB/s bandwidth, nearly three times the L4's 300 GB/s, supports larger batch sizes in inference and training, reducing bottlenecks for large language models. Coupled with 48 GB VRAM against 24 GB, the L40S handles models exceeding 24 GB without excessive paging, accelerating convergence in fine-tuning workflows.

Power dynamics contrast sharply: the L4's 72W TDP yields dense deployments, but the L40S's 350W sustains peak performance under sustained loads. FP8 at 724 TFLOPS on L40S versus 242 TFLOPS on L4 optimizes quantized inference, where low-precision formats dominate production serving.

The L4 suits budget-conscious inference and light workloads. Its 72W TDP enables high-density server configurations, and cloud pricing from $0.32 per hour across 11 offers minimizes costs for always-on serving. Deploy it for edge AI or small-batch LLM inference where 24 GB VRAM and 121 TFLOPS FP16 suffice without overprovisioning.

Power-limited environments favor the L4. Cooling requirements stay low, and PCIe 4.0 compatibility fits legacy racks, ideal for prototyping or non-critical tasks.

The L40S dominates demanding training and large-model inference. With 48 GB VRAM and 362 TFLOPS FP16, it processes expansive datasets and models infeasible on the L4's 24 GB. Bandwidth at 864 GB/s supports massive batches, slashing training times.

High-performance computing selects the L40S. FP32 at 91 TFLOPS accelerates simulations, and $1.65 per hour pricing justifies premiums for throughput gains in production pipelines.