Comparison

NVIDIA L4 vs NVIDIA A10G

Inference tier — Ada Lovelace vs Ampere

The L4 offers similar FP16 performance to the A10G (121 vs 125 TFLOPS) at a fraction of the power (72W vs 300W). Both have 24GB of memory. The L4 is dramatically more power-efficient.

Pricing Comparison

Specifications

Specification	NVIDIA L4	NVIDIA A10G
Manufacturer	NVIDIA	NVIDIA
Architecture	Ada Lovelace	Ampere
Accelerator Type	GPU	GPU
Primary Use	inference	inference
Memory (VRAM)	24 GB	24 GB
FP16 Performance	121 TFLOPS	125 TFLOPS
TDP	72W	300W
Perf per Watt	1.68 TFLOPS/W	0.42 TFLOPS/W

Detailed Analysis

The L4 and A10G both have 24GB of memory and similar FP16 performance, but they achieve this through very different designs. The L4 uses Ada Lovelace architecture at a tiny 72W TDP, while the A10G uses Ampere at 300W.

This makes the L4 approximately 4x more power-efficient than the A10G for similar performance. In data centre deployments where power and cooling are significant costs, the L4's efficiency translates to meaningful savings.

The A10G has been available longer and has wider cloud availability, particularly in older regions. The L4 is becoming increasingly available and is the preferred choice for new deployments.

Both GPUs can handle models up to approximately 13B parameters with quantisation. For inference workloads within this memory range, either GPU works well, but the L4's efficiency and newer architecture make it the better forward-looking choice.