L4 vs V100

FP16 performance remains close between the GPUs: the L4 achieves 121 TFLOPS while the V100 reaches 125 TFLOPS, enabling similar throughput for mixed-precision training and inference workloads. However, the L4 pulls ahead in FP32 at 30.3 TFLOPS against 15.7 TFLOPS, benefiting scientific simulations and graphics rendering that rely on single-precision compute. The L4's FP8 capability of 242 TFLOPS further accelerates modern quantized inference tasks absent in the V100.

Memory bandwidth presents the starkest divide: the V100's 900 GB/s HBM2 supports larger batch sizes in training compared to the L4's 300 GB/s GDDR6, reducing data starvation in memory-bound models. Yet, the L4's consistent 24 GB VRAM contrasts with the V100's variable 16-32 GB, aiding predictable deployments. Lower 72W TDP on the L4 enables dense cloud scaling without thermal constraints, unlike the 300W V100.

In real-world terms, the L4 excels in power-sensitive inference with higher FP32 and FP8 speeds, while the V100 suits bandwidth-intensive training where 900 GB/s sustains massive datasets.

Opt for the L4 in inference-heavy workloads like serving LLMs, where its 242 TFLOPS FP8 and 121 TFLOPS FP16 deliver efficient throughput at 72W TDP. Cloud users benefit from lower average pricing of $0.78 per hour across 11 offers and PCIe 4.0 simplicity for single-node setups. Modern Ada Lovelace features outperform legacy Volta in quantized models fitting within 24 GB GDDR6.

Choose the V100 for memory-intensive training tasks requiring 900 GB/s bandwidth to handle large batches in models up to 32 GB HBM2. NVLink interconnect enables multi-GPU scaling for HPC, and spot pricing from $0.05 per hour suits budget-conscious large-scale jobs despite 300W TDP. Legacy availability across 6 offers supports established Volta-optimized codebases.