The difference between GPUs and CPUs
GPUs and CPUs serve as the backbone for many of our daily tasks, from gaming and video editing to machine learning and data analysis. Here, we break down the core differences between GPUs and CPUs, and help you determine which is best suited for your specific needs.
Whether you’re a tech enthusiast or a casual user, understanding these differences will empower you to make more informed decisions in your computing journey.
What is a CPU?
A Central Processing Unit (CPU) is the primary component of a computer responsible for interpreting and executing most of the commands from the computer's hardware and software.
It is often referred to as the brain 🧠 of the computer because it performs the basic arithmetic, logic, control, and input/output operations specified by the instructions.
What is a GPU?
A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to accelerate the processing of images and calculations needed for rendering graphics.
Unlike CPUs, which are designed for general-purpose computing, GPUs are optimized for parallel processing, making them highly efficient at handling complex mathematical calculations required for rendering images, video processing, and other graphics-intensive tasks.
What is the difference between a GPU and a CPU?
An analogy to explain the difference is to think of CPUs as a Ferrari and GPUs as a bus. If you wanted to get people from a city to an airport, the Ferrari would get a small number of them there quickly, and then go back and forth (sequentially). The bus however could get them all there in one trip (in parallel), but at a slower pace.
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The primary difference between a CPU and a GPU lies in their architecture and the type of tasks they are optimized to perform.
CPUs are designed for general-purpose computing, with a focus on executing a wide range of tasks sequentially. It has a few cores optimized for complex, sequential processing.
GPUs are designed for parallel processing, with a focus on executing many tasks simultaneously. It has thousands of smaller cores optimized for handling multiple operations at the same time, making it ideal for tasks that can be parallelized, such as graphics rendering and large-scale computations.
Mythbusters teamed up with Nvidia to provide this great visual analogy too:
Is a GPU better than a CPU?
Whether a GPU is better than a CPU depends on the type of task being performed.
GPUs are better for tasks that require parallel processing, such as graphics rendering, video processing, and large-scale computations in machine learning and scientific simulations.
CPUs are better for general-purpose tasks that require complex sequential processing, such as running an operating system, performing calculations, and handling everyday computing tasks.
How are GPUs used in AI?
GPUs are extensively used in AI for accelerating the training and inference processes of deep learning models.
Their parallel processing capabilities allow them to handle the massive amounts of data and complex computations involved in training neural networks much more efficiently than CPUs. Key applications include:
Training deep learning models: GPUs can process many data points simultaneously, significantly speeding up the training process.
Inference: Once a model is trained, GPUs can also speed up the inference process, enabling real-time predictions and analyses.
Data parallelism: Distributing data across multiple GPU cores allows for handling large datasets effectively, improving model accuracy and training speed.
How are GPUs used in ASR and speech-to-text?
In Automatic Speech Recognition (ASR) and speech-to-text systems, GPUs are used for:
Accelerated model training: Training ASR models involves processing large volumes of audio data. GPUs' parallel processing capabilities allow for faster and more efficient training of complex models.
Real-time inference: GPUs enable real-time transcription by quickly processing audio inputs and converting them to text with low latency, making them ideal for applications requiring immediate results.
Enhancing accuracy: The ability to handle larger and more complex models with more parameters can lead to higher accuracy in recognizing diverse accents, dialects, and noisy environments.
What are the advantages of using GPUs for speech-to-text?
The advantages of using GPUs for speech-to-text systems include speed, efficiency, accuracy and scalability.
Speechmatics now use GPUs across all 50 of our languages. When we added this addition to our architecture, we saw the following improvements:
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What are the costs associated with using GPUs?
GPUs are generally more expensive than CPUs, especially high-end models designed for deep learning and AI applications.
There's also extra energy consumption to consider. GPUs consume more power than CPUs, leading to higher operational costs. As well as this, high-performance GPUs generate more heat and may require additional cooling infrastructure, increasing overall costs.
Developing and maintaining GPU-optimized applications can require specialized skills and tools, which can add to the costs.
However it is important to note that despite an initial outlay on hardware costs, the speed and processing cost often mean than in the medium and long term, overall costs fall.
How do Speechmatics use GPUs?
We use GPUs in two key areas of our offering.
The most visible to our customers is how we run our service. Leveraging GPU inference provides the highest level of accuracy, the lowest latency, and reduces our overall running costs in our SaaS infrastructure.
The second area is in the training of our models. GPUs allow us to train faster so we can either provide updates at a higher cadence or handle a lot more information over the same period.
For us, this meant re-architecting our product to split out the parts of our processing that are best suited to GPUs. Specifically, we started with a single “worker” container on CPU that did everything and moved to a worker which uses CPU and talks to a central “inference container” which does the GPU bit for many workers. This means we can have more workers on similar CPU infrastructure, with the only addition being the GPU resulting in a higher density of workers that can process information faster.
Can you use GPUs in on-prem deployments of Speechmatics?
Yes! GPUs can be used in on-prem deployments of Speechmatics.
Utilizing GPUs in such deployments can enhance the performance and efficiency of ASR systems by speeding up both training and inference processes, leading to faster and more accurate speech-to-text conversions.
Even when factoring in upfront hardware investment, the long term usage of GPUs is cost efficient in most on-prem deployments.
If you are interested in using GPUs for an on-prem deployment of Speechmatics, please speak to a Sales Engineer today. [Link]