A10 vs L4

The L4's FP16 performance of 121 TFLOPS vastly exceeds the A10's 31.2 TFLOPS, enabling faster training and inference for half-precision models common in modern LLMs: this translates to up to 3.9 times speedup in FP16-dominated workloads. Its FP32 rate of 30.3 TFLOPS nearly matches the A10's 31.2 TFLOPS, ensuring parity in single-precision tasks like scientific simulations. The FP8 support at 242 TFLOPS further accelerates quantized inference, reducing latency for deployment scenarios.

Higher memory bandwidth on the A10 at 600 GB/s versus 300 GB/s allows larger batch sizes in training, mitigating bottlenecks in data-heavy pipelines: for instance, it sustains higher throughput for models exceeding 24 GB VRAM utilization. The L4's lower TDP of 72W compared to 150W supports denser cloud configurations, cutting cooling and power costs by over 50 percent. Overall, the L4 excels in compute-bound inference, while the A10 shines in bandwidth-limited training.

Interconnect differences are minor, with both using PCIe, though the L4 specifies PCIe 4.0 for slightly faster host communication.

Select the A10 for workloads demanding high memory bandwidth, such as large-batch LLM training where 600 GB/s enables processing datasets without stalling, unlike the L4's 300 GB/s. Its balanced FP16 and FP32 at 31.2 TFLOPS each suits general-purpose computing or legacy Ampere-optimized code. Despite higher TDP of 150W and pricing from $0.60/hr, it fits scenarios prioritizing throughput over efficiency.

The L4 is ideal for FP16 and FP8 inference tasks, leveraging 121 TFLOPS FP16 and 242 TFLOPS FP8 for rapid LLM serving at lower latency than the A10's 31.2 TFLOPS FP16. Its 72W TDP and pricing from $0.32/hr make it preferable for scalable, cost-effective deployments across numerous instances. Choose it for power-constrained environments or modern Ada-optimized applications.