L4 vs RTX 3080 Ti

FP16 performance defines a key disparity: the L4's 121 TFLOPS doubles the RTX 3080 Ti's 29.8 TFLOPS, accelerating half-precision inference and training for deep learning models. The L4's FP32 at 30.3 TFLOPS slightly exceeds the 3080 Ti's 29.8 TFLOPS, but its FP8 capability at 242 TFLOPS supports emerging quantized inference tasks unavailable on Ampere. Memory bandwidth favors the RTX 3080 Ti at 760 GB/s over the L4's 300 GB/s, allowing larger batch sizes in bandwidth-bound workloads like high-resolution image processing. However, the L4's 24 GB VRAM versus 12 GB enables bigger models or sequences without swapping, crucial for LLMs during inference. In real-world terms, the L4 excels in VRAM-limited scenarios, while the 3080 Ti suits bandwidth-intensive gaming or rendering ports to compute.

Opt for the L4 in inference-heavy workloads requiring substantial VRAM, such as deploying LLMs with 24 GB models that exceed the RTX 3080 Ti's 12 GB limit. Its 121 TFLOPS FP16 and 242 TFLOPS FP8 deliver faster quantized serving, ideal for cloud services prioritizing low-latency AI endpoints. The 72W TDP ensures efficiency in dense multi-GPU racks, reducing cooling costs despite higher hourly rates averaging $0.69.

Select the RTX 3080 Ti for budget-sensitive training or rendering where 760 GB/s bandwidth supports large batches, outpacing the L4's 300 GB/s. At $0.14 per hour average, it provides value for Stable Diffusion or scientific simulations not constrained by 12 GB VRAM. The 320W TDP fits high-performance single-GPU setups tolerant of power draw.