How to choose a GPU for machine learning?
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How to choose a GPU for machine learning?
GPUs for machine learning are a popular choice for gamers and developers looking for higher computational power. But how do you know which GPU is the best? Are there any factors to consider? We attempt to answer all the above through this article.
?According to research by AMR (Allied Market Research), the global GPU (Graphics Processing Unit) market size is projected to reach $200.85 billion by 2027, growing at a CAGR of 33.6% from 2020 to 2027.
With increasing dependence on data-intensive decision-making, the demand for high-performance computing solutions like GPUs is growing. GPUs are known for their ability to perform complex calculations at high speeds. This makes them an indispensable tool for businesses or individuals looking to run applications based on artificial intelligence, machine learning (ML), scientific simulations, and even cryptocurrency mining.
As a result, many companies and gamers are now investing or looking to invest in GPUs to run high computational processing like machine learning and deep learning (DL). But with so many overwhelming options available, it can be difficult to determine the best GPU for machine learning or deep learning workloads.
In this blog post, we’ll explore everything from the basics of GPUs to how they support machine learning—so that you can make an informed decision when selecting the right GPU for your setup.
So, let’s get started!
Why go for GPUs for machine learning?
A GPU is a specialized computer chip that can handle massive amounts of mathematical calculations required for graphics rendering and visual effects. In recent years, the demand for GPUs for deep learning and machine learning systems is growing – due to their computational power.
Deep learning is a subset of machine learning. (We will be using both terms interchangeably during the article).
Machine learning algorithms mostly involve training models on large datasets – thus requiring intensive computations. You might be thinking – then why not use CPUs (Central Processing Units) or?how are GPUs better than CPUs ?when it comes to running machine learning algorithms?
GPUs are faster than traditional CPUs due to their ability to run computations fast – and thus train ML models faster. GPUs support parallel processing which makes them particularly well-suited for deep learning.
GPUs can thus accelerate machine learning workloads like image recognition. They can also share the work of CPUs and train deep learning models for AI-based applications. If you want to know more about the difference between CPUs and GPUs, check out this video by Mythbusters – Adam Savage and Jamie Hyneman.
How do GPUs for machine learning or deep learning work?
GPU computing is based on the principles of parallel processing. It means multiple calculations are performed simultaneously to achieve a faster result and the task is handled by multiple processors. GPUs use a SIMD (single instruction, multiple data) architecture.
GPUs mostly come in two types – integrated and discrete. While the former comes embedded alongside the GPUs, discrete GPUs can be mounted on a separate circuit board.
For heavy computations, especially when working with ML/DL models, or 3D visualizations, companies should prefer GPUs fixated in the cloud. Cloud GPUs offer everything that a GPU does – with the added benefits of cloud computing. You can free up local resources, save time, and cost, and achieve scalability. An example would be?NVIDIA GPUs on the cloud .
Suggested Reading:?Cloud GPU: The Benefits of Using GPUs in the Cloud
How to Choose the Best GPU for Machine Learning?
Till now we learned what is a GPU and what are its benefits for running ML/DL applications. Let’s now look at some of the factors that you must consider when choosing a GPU for machine learning projects.
The TDP or thermal design power value of a GPU indicates the maximum amount of power the cooling system needs to dissipate. Thus, it gives you an estimate of the power consumption of your GPU. The important thing to note is that it does not tell you the amount of electricity consumed by your processor – but just a power consumption limit or ceiling that should not be exceeded to avoid overheating.
A higher TDP value often means higher heat output, which means that you’ll need a better cooling system to prevent overheating. GPUs with higher TDP tend to offer better performance but may also consume more power and generate more heat. Your PSU (power supply unit) should be able to handle this.
VRAM or video random access memory is another important factor to consider when choosing a GPU for machine learning systems. More memory allows you to run applications at higher resolutions with better image quality. It is also important for improving the overall performance and running multiple applications at a time.
In the case of machine learning, if you are dealing with algorithms that require long videos to train data sets, you should buy a GPU with large memory. Simple data sets with basic predictions require less memory.
Computational cores are one of the key factors to consider when selecting a GPU. The number of cores in a GPU can have a significant impact on its performance, as more cores generally mean that the GPU can handle more tasks simultaneously and perform them faster.
GPU vendors might sometimes use separate terms for their cores. For example, AMD calls their cores stream processors, while in NVIDIA they are known as CUDA (Compute Unified Device Architecture) cores. NVIDIA also has Tensor cores and Ray-tracing cores (exclusive to NVIDIA).
Each core has a separate and specific function. For ML models, tensor cores are better as they are faster and more efficient. These are specifically designed keeping in mind the heavy calculations required to run the systems.
GPU chip architecture refers to the design and structure of the microprocessor, or the “chip,” that powers a GPU. The architecture determines how the chip processes and manages data and is a critical factor in determining a GPU’s performance, efficiency, and capabilities.
There are several different GPU chip architectures used by different manufacturers, including NVIDIA, AMD, and Intel. The popular architecture used by NVIDIA includes Ampere, Turing, Tesla, Volta, Hopper, and more.
A GPU with a well-designed and optimized chip architecture can provide better performance and efficiency, making it a critical factor in your overall computing experience.
The interconnecting GPUs enable you to scale up the performance of your ML applications and train DL models more quickly. By connecting multiple GPUs, you can divide the workload among them and take advantage of the parallel processing capabilities of GPUs to speed up the training process.
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Some GPUs may support only a single GPU connection, while others may support multiple GPU connections via technologies such as NVIDIA’s NVLink or AMD’s Infinity Fabric.
Additionally, the software and frameworks that you plan to use for machine learning should be compatible with the interconnect technology that your GPU supports. For example, if you plan to use TensorFlow, it’s important to ensure that the version you are using supports the interconnect technology of your GPU.
The compatibility between a GPU and the machine learning libraries you want to use is a crucial factor to consider when choosing a GPU. Some libraries are specifically designed to work with certain GPUs and hardware architectures, so it’s important to check the compatibility before making a purchase.
Additionally, newer GPU models may also support more recent and advanced libraries, so it’s good to stay updated on the latest developments.
NVIDIA GPUs tend to be more supportive in terms of ML libraries and integration with common frameworks like TensorFlow or PyTorch.
The license requirement for GPUs can vary depending on the manufacturer and the specific GPU model. Some GPUs, such as those made by NVIDIA, require a proprietary license to use certain features or access certain software. For example, NVIDIA’s CUDA platform, which is widely used for GPU-accelerated computing, may require a license for commercial use.
It’s important to consider the license requirement and cost when choosing a GPU, as it can have a significant impact on the overall cost and availability of the GPU for your use case.
When it comes to choosing GPUs for machine learning applications, you might want to consider the algorithm requirements too.
The computational requirements of an algorithm can affect the choice of GPU. Some algorithms are computationally intensive and may require a high-end GPU with many cores and fast memory. Algorithms that can be parallelized across multiple cores will benefit from a GPU with many cores. Large datasets may require a GPU with a large memory capacity to store the data and prevent data transfer bottlenecks.
Best GPUs for machine learning
If you’re unsure of which GPU is good for machine learning, here are some GPUs you can consider.
NVIDIA Titan RTX
The NVIDIA Titan RTX is a high-performance graphics card designed for demanding computing tasks, such as deep learning and AI development. With its powerful 24GB of GDDR6 memory and 4,608 CUDA cores, the Titan RTX provides incredible speed and accuracy for complex computations. Other specs:
NVIDIA GeForce RTX 3090 Ti
The NVIDIA GeForce RTX 3090 Ti is a cutting-edge graphics card designed for high-performance gaming and content creation.
It supports NVIDIA’s latest technologies, such as AI-accelerated features and advanced cooling solutions, making it ideal for demanding gaming and content creation workloads.
Other specs:
NVIDIA Tesla V100
The NVIDIA Tesla V100 is a high-performance GPU designed for demanding data centers and scientific computing workloads. The Tesla V100 is designed to accelerate various demanding applications such as machine learning, data analytics, high-performance computing, and more.
It is also the world’s first GPU to break the 100 teraFLOPS barrier of DL performance. The next generation of NVIDIA NVLink can connect multiple V100 GPUs at up to 300 GB/s.
Other specs:
NVIDIA Quadro RTX 8000
The NVIDIA Quadro RTX 8000 is the world’s first ray-tracing GPU. It is based on the Turing architecture and features 4,608 CUDA cores, 48 GB of GDDR6 memory, and a peak performance of up to 16 TFLOPs (FP32).
Other specs:
NVIDIA Tesla A100
The NVIDIA Tesla A100 is built on the Ampere architecture and features 6,912 CUDA cores. The Tesla A100 also supports multi-GPU scaling and is designed to deliver performance and versatility for a wide range of applications, including AI, HPC, data analytics, and more.
Other specs:
Important note: The GPUs are just a recommendation based on available user reviews and resources on the Internet. The technical specifications can change; hence readers are advised to refer to the official vendors’ site for the exact and detailed technical specifications.
Wrapping up – how ZNetLive can help you select the right GPUs?
At ZNetLive, we offer high-performance?cloud GPUs powered by NVIDIA . Our cloud GPUs come with the perfect price-performance ratio and zero-vendor lock-ins. We also ensure 99.9% service uptime with 24/7 technical support and SLA-backed guarantees so your applications and workloads can be up and running without any issues.
If you need guidance in selecting the right GPU for your deep learning or machine learning needs, you can get in touch with our experts at?[email protected] .