L4 vs T4

The L4's FP16 performance of 121 TFLOPS dwarfs the T4's 8.1 TFLOPS, delivering approximately 15 times the half-precision compute: this accelerates LLM training and inference significantly for mixed-precision workflows. FP32 rates further underscore the divide at 30.3 TFLOPS for the L4 versus 8.1 TFLOPS for the T4, a nearly fourfold boost ideal for scientific computing requiring single-precision accuracy.

Memory capacity proves decisive: the L4's 24 GB VRAM supports batch sizes for models up to 70 billion parameters in inference, while the T4's 16 GB limits scalability for larger batches. Bandwidth remains close at 300 GB/s for the L4 and 320 GB/s for the T4, but the L4's PCIe 4.0 interconnect outperforms the T4's older PCIe 3.0 in data transfer speeds.

The L4's FP8 capability at 242 TFLOPS enables ultra-efficient quantized inference, reducing latency for real-time applications where the T4 lacks equivalent support.

Opt for the L4 in high-throughput inference scenarios such as serving large language models, where 24 GB VRAM handles bigger batches than the T4's 16 GB. Its 121 TFLOPS FP16 performance suits modern Ada-optimized workloads at a lower cost of $0.32 per hour starting price.

Fine-tuning or Stable Diffusion tasks benefit from the L4's 30.3 TFLOPS FP32 and PCIe 4.0 speeds, outperforming the T4 across 15 cloud providers.

Choose the T4 for legacy Turing-optimized applications or environments with strict PCIe 3.0 compatibility, where its 320 GB/s bandwidth edges the L4's 300 GB/s for memory-bound tasks.

Budget constraints in spot markets may favor the T4 if priced below $0.53 per hour, suitable for lightweight inference on models fitting within 16 GB VRAM.