L4 vs RTX 5080

The L4 excels in half-precision tasks with 121 TFLOPS FP16 and 242 TFLOPS FP8, enabling faster inference for large language models where quantized models run efficiently. Its FP32 performance of 30.3 TFLOPS lags behind the RTX 5080's 56.3 TFLOPS, meaning the L4 suits inference more than full-precision training, which demands higher FP32 throughput on the RTX 5080. This delta implies the RTX 5080 handles training loops better, reducing epochs time in FP32-dominant workflows.

Memory bandwidth disparity is stark: the RTX 5080's 960 GB/s versus the L4's 300 GB/s allows larger batch sizes in memory-intensive operations like transformer training, minimizing data transfer bottlenecks. The L4's 24 GB VRAM supports bigger models without splitting, ideal for inference with batch size 1, while the RTX 5080's 16 GB GDDR7 limits it for ultra-large models but compensates with speed. Power efficiency favors the L4 at 72W, enabling dense deployments, whereas the RTX 5080's 360W demands robust cooling.

Overall, these specs translate to the L4 optimizing cost per inference token via low TDP and high FP8, while the RTX 5080 boosts throughput in bandwidth-hungry training by threefold.

Opt for the L4 in low-power cloud instances or edge deployments where 72W TDP minimizes energy costs. Its 24 GB VRAM handles memory-bound inference for models exceeding 16 GB, such as Llama 70B quantized, without multi-GPU setups. With FP8 at 242 TFLOPS, it accelerates serving high-volume requests efficiently.

The L4 fits budget-conscious inference pipelines, especially with 15 live pricing offers starting at $0.32/hr, outperforming in scenarios prioritizing VRAM over raw compute.

Select the RTX 5080 for training workloads requiring balanced FP16 and FP32 at 56.3 TFLOPS each, speeding up gradient computations. Its 960 GB/s bandwidth supports large batch sizes in diffusion models or fine-tuning, reducing per-iteration time.

At an average $0.38/hr across 4 offers, it offers value for high-throughput tasks where GDDR7 speed offsets the 16 GB VRAM limit via faster data movement.