L40S vs NVIDIA A100 40GB SXM

Detailed comparison of specifications, performance, and pricing between NVIDIA L40S and NVIDIA A100 40GB SXM

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Overall Winner
L40S
Wins 4 of 7 categories
Performance Leader
L40S
733.0 TFLOPS (+17%)
The L40S is 17% faster.

Difference Analysis

Metric
L40S
Difference
NVIDIA A100 40GB SXM
Tensor TFLOPS
733.0
+17%
624.0
VRAM
48GB
+20%
40GB
Memory Bandwidth
864 GB/s
-80%
1.6 TB/s
Hardware Price
$$9.0k
=
-
Cloud Price/hr
$0.860
-50%
$1.29

Full Specifications

Specification L40S NVIDIA A100 40GB SXM
Brand NVIDIA NVIDIA
Series Data Center Data Center
Architecture Ada Lovelace Ampere
VRAM 48GB 40GB
VRAM Type GDDR6 HBM2
Memory Bandwidth 864 GB/s 1.6 TB/s
FP16 TFLOPS 183.0 312.0
Tensor TFLOPS 733.0 624.0
TDP 350W 400W
Form Factor PCIe -
Hardware Price $$9.0k -
Cloud Price (min) $0.860/hr $1.29/hr

Which Should You Choose?

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For AI Training

Large model training needs maximum VRAM and memory bandwidth.

Recommended: L40S
48GB VRAM · 864 GB/s

For AI Inference

Inference prioritizes throughput and cost efficiency.

Recommended: L40S
Best performance per dollar
☁️

For Cloud Rental

Minimize hourly costs for cloud workloads.

Recommended: L40S
From $0.860/hr

L40S vs NVIDIA A100 40GB SXM FAQ

It depends on your use case. The L40S offers 17% better performance (733.0 vs 624.0 TFLOPS). For raw performance, choose L40S. For value, consider your budget and workload requirements.

The L40S has more VRAM with 48GB compared to 40GB (20% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.

For AI training, the L40S is generally better due to its larger VRAM (48GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 40GB, the cheaper option may be more cost-effective.

Price comparison requires both GPUs to have available pricing data. Check individual GPU pages for current market prices.

Upgrading to L40S would give you 17% more performance and 20% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.