L4 vs RTX 4080

L4's FP16 performance of 121 TFLOPS doubles RTX 4080's 48.7 TFLOPS: this advantage accelerates AI training and inference using half-precision formats, common in modern neural networks. The L4's 242 TFLOPS FP8 further enhances quantized inference speeds, unavailable on RTX 4080 specs. Such tensor core efficiencies make L4 preferable for high-throughput serving.

RTX 4080's memory bandwidth of 717 GB/s vastly outpaces L4's 300 GB/s: higher bandwidth enables larger batch sizes in training, minimizing data transfer bottlenecks and improving utilization. Its matched FP16 and FP32 at 48.7 TFLOPS each supports versatile workloads blending graphics and compute, unlike L4's FP32 deficit at 30.3 TFLOPS.

Power and memory profiles differentiate further. L4's 72W TDP allows dense server packing, ideal for scale-out inference, while its 24 GB VRAM handles models exceeding RTX 4080's 16 GB capacity. RTX 4080's 320W draw demands robust cooling but delivers raw throughput for bandwidth-bound tasks.

The L4 stands out for inference-heavy workloads: 24 GB VRAM accommodates large language models, and 121 TFLOPS FP16 with 242 TFLOPS FP8 ensures rapid low-precision serving. Its 72W TDP enables deployments in power-limited datacenters, fitting 4x more GPUs per rack than RTX 4080's 320W.

Choose L4 for cost-effective density where PCIe 4.0 interconnect suffices, especially at scale across 15 cloud offers averaging $0.68 per hour.

RTX 4080 excels in training and graphics tasks: 717 GB/s bandwidth supports massive batches, and balanced 48.7 TFLOPS FP16/FP32 handles diverse compute. Lower pricing from $0.11 per hour averaging $0.28 across 8 offers maximizes value for bursty workloads.

Select RTX 4080 for high-throughput scenarios where 16 GB VRAM suffices and 320W TDP aligns with available power.