<p>In the context of Deeplearning:</p>
<ul>
<li>FLOPS: Floating Point Ops per Second</li>
<li>FLOPs: Floating Point Ops</li>
</ul>
<p>FLOPS, refers to the number of floating point operations that can be performed by a computing entity in one second. It is used to quantify the performance of a hardware.</p>
<p>FLOPs, simply means the total number of floating point operations required for a single forward pass. The higher the FLOPs, the slower the model and hence low throughput.</p>
<p>This thread on stack overflow might help to get a deeper insight:
<a href="https://stackoverflow.com/questions/58498651/what-is-flops-in-field-of-deep-learning">https://stackoverflow.com/questions/58498651/what-is-flops-in-field-of-deep-learning</a></p>

CC BY-SA 4.0
2021-12-21T20:33:55.367Z
2021-12-21T20:33:55.367Z
Sooryakiran Pallikulathil
<p>I understand that FLOPS means floating-point operations per second, and throughput is the number of inputs (for example, images) per second. If a model has higher FLOPS, it means it performs faster.</p>
<p>However, in the article <a href="https://arxiv.org/pdf/2106.01401.pdf" rel="nofollow noreferrer">Container: Context Aggregation Network</a>, they show that:</p>
<p><a href="https://i.sstatic.net/yrX8N.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/yrX8N.png" alt="enter image description here" /></a></p>
<p>The container has higher FLOPS and less throughput, while the container-light has lower FLOPS and higher throughput.</p>
<p>What is the reason for that?</p>

<neural-networks><deep-learning><papers><image-net><flops>
Does higher FLOPS mean higher throughput?
BestR
# Does higher FLOPS mean higher throughput?

(Asked by: username_0 on 2021-12-21)

I understand that FLOPS means floating-point operations per second, and throughput is the number of inputs (for example, images) per second. If a model has higher FLOPS, it means it performs faster.

However, in the article [Container: Context Aggregation Network](https://arxiv.org/pdf/2106.01401.pdf), they show that:

[![enter image description here](https://i.sstatic.net/yrX8N.png)](https://i.sstatic.net/yrX8N.png)

The container has higher FLOPS and less throughput, while the container-light has lower FLOPS and higher throughput.

What is the reason for that?

**Tags:** neural-networks, deep-learning, papers, image-net, flops


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## 1 answers

### Answer by username_1 on 2021-12-21. Score: 5 | Accepted answer

In the context of Deeplearning:

* FLOPS: Floating Point Ops per Second
* FLOPs: Floating Point Ops

FLOPS, refers to the number of floating point operations that can be performed by a computing entity in one second. It is used to quantify the performance of a hardware.

FLOPs, simply means the total number of floating point operations required for a single forward pass. The higher the FLOPs, the slower the model and hence low throughput.

This thread on stack overflow might help to get a deeper insight:
<https://stackoverflow.com/questions/58498651/what-is-flops-in-field-of-deep-learning>


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