Comparison

NVIDIA L40S vs NVIDIA A100 80GB

Inference-optimised vs training-class

The L40S offers slightly higher FP16 performance (366 vs 312 TFLOPS) at a lower price, but with less memory (48GB GDDR6X vs 80GB HBM2e). Different strengths for different workloads.

Pricing Comparison

Specifications

Specification	NVIDIA L40S	NVIDIA A100 80GB
Manufacturer	NVIDIA	NVIDIA
Architecture	Ada Lovelace	Ampere
Accelerator Type	GPU	GPU
Primary Use	inference	training
Memory (VRAM)	48 GB	80 GB
FP16 Performance	366 TFLOPS	312 TFLOPS
TDP	350W	400W
Perf per Watt	1.05 TFLOPS/W	0.78 TFLOPS/W

Detailed Analysis

The L40S and A100 80GB represent different design philosophies. The A100 is a training-first GPU with HBM2e memory optimised for bandwidth-intensive workloads. The L40S is an inference-optimised Ada Lovelace GPU with GDDR6X memory.

While the L40S has slightly higher raw TFLOPS (366 vs 312), the A100's HBM memory provides significantly higher bandwidth (2.0 TB/s vs ~864 GB/s), making it faster on memory-bound operations like large model training.

The L40S's advantage is cost and versatility. It's typically 30-50% cheaper per hour than the A100 and includes hardware ray tracing for mixed AI/graphics workloads. Its 48GB of memory is sufficient for serving models up to ~25B parameters.

The A100's 80GB HBM memory and higher bandwidth make it the better choice for training workloads. For pure inference, the L40S often delivers better price/performance.

Verdict

Best for Training

A100 80GB — HBM memory bandwidth matters for training.

Best for Inference

L40S — cheaper with sufficient performance for most inference workloads.

Best Value

L40S for inference. A100 for training. Match the GPU to your workload type.

Frequently Asked Questions

Can the L40S replace the A100 for training?

For fine-tuning and small-scale training, yes. For large-scale pre-training, the A100's HBM memory bandwidth gives it a meaningful advantage despite lower TFLOPS.