Best GPUs For Deep Learning: Considerations To Support Large AI Projects

In this information-driven era, every organization is leveraging data emanating from multiple sources. Some extract deep insights through analytics, while others teach machines to work unescorted using supplied data. Have you ever wondered how translator apps like Google Translate can render an entire text from one language to another in a fraction of a second?

Have you wondered how these systems are trained and what computational power is required to develop these advanced systems and their underlying algorithmic models? Preparing such algorithms to work efficiently requires humongous amounts of data and processing power.

Dedicated GPUs can be deployed to undertake complex Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/ DL) training operations in parallel and use these for prediction/ inference ventures. GPUs significantly reduce time and increase efficiency in ML/ DL model development and training.

This article will give a comprehensive guide to why GPUs are beneficial for large-scale AI and DL projects. The comprehension will also stretch to the best GPUs enterprises can opt for DL operations.

What is Deep Learning?

Deep Learning (DL) is a booming new subset within the larger world of AI/ ML. It uses complex algorithms inspired by biological brain structure known as Artificial Neural Networks (ANNs). These marvelous algorithms leverage massive datasets to learn and derive insights which can then be employed to make market predictions, offer personalized recommendations, translate languages, recognize patterns/ images/ biometric features, undertake IoT-associated diagnostics, and so on.

In short, barring few exceptions, DL systems can perform tasks that humans can perform but with substantially more efficiency and accuracy!

Cherry on top? DL vastly improves analytical ability and automation in AI systems.

But training such complex systems often requires enormous amounts of processing power. Given the complexity of underlying data and multiplicity of said data sources, AI and ML/ DL model training with traditional processors (CPUs) is not only extremely time-consuming but expensive as well. Thus, powerful Graphics Processing Units (GPUs) that concurrently run thousands of CUDA cores and Tensor cores are preferred both by commercial enterprises (like self-driving car developer Tesla) and DL research project developers (Deepmind, Google’s AI/ ANN subsidiary).

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Why GPUs for Deep Learning?

The most time-consuming and resource-intensive phase a DL project incurs is during the training phase. DL model training often relies on Terabytes of data, requiring weeks and even months of sorting, sieving, rationalizing and processing data. Hence, enabling AI to derive even the most basic insights on its own can be very time-consuming during the training phase. An increase in the number of parameters and the underlying dataset size is directly proportional to increase in processing power requirement.

Thus, enterprises are shifting the processing and computational paradigm towards GPU-based systems. GPUs come with more processing cores compared to CPUs, leading to far superior performance. Nvidia’s A100 GPU can outperform the most advanced CPU by 237 and 30 times respectively in data center inference and image recognition tests.

No wonder, GPU-enabled systems have cornered 57% segment of the global DL market which was valued at USD 34.8 billion in 2021 and is expected to surge forward with CAGR over 30% over 2022-30.

The exemplar performance and market segmentation are evidence of the universal suitability and acceptability of GPUs for DL. By utilizing GPUs, enterprises can manifolds reduce the overall training time and expenditure, even when incorporating a fairly large number of underlying parameters, relationships and datasets. The multi-core architecture of GPUs facilitates parallel computation and seamlessly expedites training tasks by distributing the workload over multiple processor clusters. Such high-level parallelism reduces the training time by handling multiple computation resource-intensive operations simultaneously.

Helpful Read: Why GPUs for Deep Learning? A Complete Explanation

Critical Considerations when choosing GPUs for Deep Learning Operations

Selecting GPUs for DL projects often becomes an irredeemably hectic task. Constraints like performance, budget, scalability, ease of use, ease of programming, etc., come into the picture. Enterprises should choose GPUs or a Cloud GPU service that is capable of supporting versatile large-scale AI and DL projects in the long run. Let us understand the various factors to be considered when choosing GPUs for critical AI/DL projects –

  • High performance and accuracy – Large-scale AI and Deep Learning projects require GPUs that enable dynamic adapting calculations and mixed-precision computing. GPUs with multiple cores accelerate ML/DL model training and help in fine-tuning the model performance, especially when new parameters/ relationships are incorporated.
  • Price factor – Whether developing a small-scale DL model or a large-scale self-sustaining, self-learning AI system, companies must pay heed to price constraints when commissioning exorbitant on-prem GPU networks.
  • Interconnectivity – Optimizing the colossal datasets underlying DL training is an important factor in Unsupervised Learning and Reinforcement Learning. DL projects are heavily resource-intensive and when the datasets contain semi-structured and unstructured data, enterprises must invest in multiple GPUs, interconnecting switches and associated network resources. This interconnectivity is directly related to scaling the processing power and enabling efficient multi-GPU utilization with distributed training strategies.
  • Memory usage – A model training that utilizes massive and complex datasets, such as medical images, videos, etc., naturally requires relatively large memory in GPUs. Unlike tabular data (used in NLP), which is lightweight and can be efficiently processed with less computational and memory resources, semi-structured and unstructured data necessitate huge resource availability. Thus, memory usage depends on the input data type, algorithmic parameters, and programming efficiency.
  • Supporting software – Enterprises should also take note of the various software development libraries a particular GPU can support. Not all GPUs can support all ML/ DL libraries that your large-scale AI projects will use.

Advanced Nvidia GPUs with cutting-edge Tensor cores and CUDA cores best support ML/ DL libraries, frameworks, and integrations like TensorFlow, Scikit-Learn, Keras, and PyTorch. Nvidia CUDA toolkit offers GPU-accelerated libraries with optimization and debugging tools. All such software support becomes a perfect match for large-scale AI and DL model training and development.

  • Scalability through Cloud GPUs – Like all resources, on-prem GPUs also present numerous drawbacks as far as dynamic scalability is concerned. It is exorbitant, dependent on highly trained human resources for server setup, maintenance and operations, and requires intensive electricity and cooling resources, besides being very time-consuming. And then, it cannot be scaled down when utilization decreases.

To overcome these numerous challenges, enterprises must look towards Cloud Service Providers specializing in GPU-as-a-Service (GaaS). Cloud resources are universally well-regarded for their dynamic scalability. They also authorize enterprises to leverage optimum parallel processing across diverse workloads. Developers and engineers can effortlessly run DL projects on Cloud Virtual machines without bank-breaking upfront hardware investments.

  • Proper licensing – Enterprises must also consider the licensing obligations before investing in any on-prem GPU. According to Nvidia’s guidelines specified in their GPU End-User Licensing Agreement (EULA), data centers are not allowed to use particular GPU models in enterprise-grade projects. Furthermore, enterprises are also restricted from enabling CUDA software in consumer-grade GPUs deployed in datacenters.

Naturally, enterprises must be very careful when shortlisting production-supported GPUs eligible to run CUDA software. Opting for Cloud GPU services can remediate this obstacle.

Helpful Read: How To Find The Best GPU For Deep Learning?

Best GPUs for Deep Learning

An exploration of cutting-edge GPUs suitable for both individual-level and enterprise-grade AI/ DL project development –

Nvidia A30 GPU

Nvidia A30 GPU is a powerful GPU based on Ampere architecture, it comes equipped with Tensor cores. With Multi-Instance GPU processing (MIG) enabled, this GPU can deliver Exascale computing, accurate image recognition, fully interactive Ray Tracing (RT), and situational analysis. It can provide High-Performance Computing (HPC) across diverse workloads. It also features fast memory bandwidth to create an ideal system for scientific application development, Machine Learning training, and Big Data Analytics.

Nvidia A100 GPU

Also featuring advanced Tensor cores and MIG capabilities allowing dynamic partitioning up to 7 GPU instances, the A100 was designed by Nvidia for resource-intensive 10x higher Machine Learning, large-scale AI, real-time data analytics and complex HPC. Offered by Ace Cloud Hosting, this GPU can also be optimized for portability across different architectural setups. The A100-80 GB GPU can deliver up to 312 TFlops FP16 Tensor core performance, sport 80 GB memory and 1935 GB/s GPU memory bandwidth, besides ultrafast 600 GB/s interconnectivity with other GPUs via NVLink Bridge.

Nvidia Tesla V100

Another Tensor core-boosted GPU that is an excellent choice for dedicated AI/ ML, DL and HPC. It leverages the Volta technology and uses Tensor cores to accelerate extensive computations and dataset training in ML/DL operations. It is a powerful, energy-efficient, and dedicated GPU to speed up complex computing operations. The V100 delivers 112 Teraflops of Tensor performance, boasts of 32 GB inbuilt memory, and can support CUDA, DirectCompute, OpenCL and OpenACC Libraries.

Google’s Tensor Processing Unit (TPU)

The odd one on the list, this is not a GPU exactly but an Application-Specific Integrated Circuit (ASIC) developed by Google using its own TensorFlow library to accelerate massive AI workloads and data-rich ML/ DL tasks. Though less customizable for diverse workloads vis-a-vis GPUs, this Cloud-based high-powered array can help build TensorFlow compute clusters that leverage CPUs, GPUs, and TPUs. A TPU v4 system can deliver up to 275 Teraflops Int8 performance and 32 GB High Bandwidth Memory (HBM).


Today, almost every industry is leveraging the power of Artificial Intelligence, Machine Learning, and Deep Learning. Though Deep Learning is still in its infancy, research remains ongoing to make it more efficient, robust and cost-effective.

Enterprises and software development firms working on DL projects require extensive computation to handle the vast number of data points and the corresponding complex algorithms. Hence, their overdependence on GPUs for undertaking parallel computing, which in turn reduces model training time and fosters accuracy.

The numerous technical, financial and networking-related challenges involved when using GPUs can be elegantly sidestepped by effecting a shift from on-prem deployment to Cloud-based GPU services.

Ace Cloud Hosting offers top-of-the-line Cloud GPU services at affordable costs. Chat with our Consultant here and find out how we can sort your GPU requirement concerns!

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About Nolan Foster

With 20+ years of expertise in building cloud-native services and security solutions, Nolan Foster spearheads Public Cloud and Managed Security Services at Ace Cloud Hosting. He is well versed in the dynamic trends of cloud computing and cybersecurity.
Foster offers expert consultations for empowering cloud infrastructure with customized solutions and comprehensive managed security.

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