Last updated on January 6th, 2023
Machine learning and AI provide businesses with a competitive edge. These technologies help gain insight into massive data collections, the facilitation of medical forecasts, and the enhancement of predictive maintenance in manufacturing, to name a few.
According to an analysis report by O’RILEY, nearly 48% of businesses are using machine learning, data analysis, or AI to keep up with their competitors. The challenge for businesses is to employ these technologies substantially without driving up infrastructure expenditures.
An infrastructure cost increase arises from employing components throughout the organization that support ML workloads efficiently. Multi-CPU setups, for example, can create excessive overhead costs when handling ML workloads.
For a better price-to-performance ratio, many organizations utilize specialized processing hardware like GPUs. Moreover, with the exponential increase in the number of cores and computational resources, GPUs have emerged as one of the major components of modern AI.
Let’s take a look at how cloud GPUs can help you take your AI projects to the next level.
How GPUs facilitate AI and ML workloads
GPUs are microprocessors that perform specialized tasks such as graphics processing and simultaneous computations by using their parallel processing capabilities and higher memory bandwidth.
They have become essential for applications that require intensive computing such as gaming, 3D imaging, video editing, crypto mining, AI, and Machine learning. Compared to CPUs, GPUs perform dense computations much faster and more efficiently.
CPUs are the best choice if your task requires the processing of sequential operations at very low latency, they have fewer extremely powerful cores. But they fail to deliver when the task requires parallel processing.
Deep learning was a fundamentally new software model which needed hardware architecture that could help process huge amounts of data simultaneously, this is where the GPU stepped in.
Initially, GPUs were used for gaming and other graphic-intensive projects, but as their parallel processing capabilities became clearer, they became an ideal match for deep learning applications. Today, 12 GPUs can deliver the deep-learning performance of 2000 CPUs.
Cloud GPUs: The Key to Growing Your AI and ML Projects Exponentially
We have established the importance of GPUs in AI and ML operations, but employing the appropriate hardware comes with its challenges, especially if you are a small business, start-up or are looking to use them for a personal project or research.
GPUs capable of running ML models are extremely powerful and equally expensive. Combining this with the cost of the infrastructure (memory, cooling solutions, space, etc.) needed to employ these effectively and you’ve reached a massive roadblock in your journey. This is where cloud GPU solutions come to your rescue.
With cloud GPUs, you can concentrate on your business operations since they are no longer required to handle on-premise GPUs themselves or worry about the cost associated with them.
This simplifies business operations and improves productivity, allowing everyone to work with AI and ML and privileges which were accessible only to massive organizations with abundant resources. Furthermore, cloud platforms provide other benefits as explained below:
- Data Migration: As your company migrates data to the cloud, it benefits from the advantages that it brings with it: fast provisioning, infinite scalability, pay per-use pricing, reduced infrastructure and IT costs, seamless upgrades, and rapid technological innovation.
- Accessibility: Cloud service providers offer extremely easy to use, intuitive and user-friendly interfaces, so that anyone with little or no technical knowledge can operate them and manage their plans according to their requirements.
- Integration: Cloud service providers offer seamless integration with popular OSs, Software, and Enterprise applications with the platforms.
- Storage Security: These platforms provide excellent security for your sensitive data and even provide guaranteed protection against DDOS attacks.
- Upgradability: You can add more memory and processing power to the server on demand and even upgrade to newer hardware whenever it rolls out.
- Scalability: Cloud GPUs are highly scalable allowing you to add more resources when there is a heavy workload and scale down when the requirement ceases.
Cloud GPU providers have been steadily gaining popularity as more businesses are using these services for various purposes. They are responsible for setting up trustworthy and reliable cloud GPUs, which can be used by millions of users worldwide including people from small organizations.
Converting the capital expenses associated with mounting and managing such computing resources into operational expenses, smaller businesses can grow much faster.
Also Read: The New Wave of Cloud GPUs
Employing Cloud GPUs: An Overview
In order to use cloud GPUs, you must first select a cloud service provider. Comparing providers based on their services will allow you to make an informed choice.
After choosing a provider, the next step would be to get familiar with its interface and infrastructure. Most cloud providers have extensive support documentation, tutorial videos, and blogs to help you get started. Many platforms also provide learning paths and certifications for their services to enhance learning experiences.
Choosing the right cloud GPU providers for personal and business computing can be a tiresome task and with plenty of cloud providers and plans available today, making a choice can be challenging.
Recommended GPUs for Large Scale AI and ML Workloads
Most modern AI systems are made up of trillions of data points collected over years, fed through sophisticated mathematical models that attempt to predict the future. Without a powerful GPU and a proper internet connection, these types of models would be strenuous to handle.
So, how do we determine which GPU should be used in modern AI?
NVIDIA has been the leading provider of both consumer and production-grade GPUs for a long time. Almost all players in the cloud GPU game provide NVIDIA GPUs in their offerings.
Some of the best GPUs for heavy AI/ML workloads are:
- NVIDIA Ampere A100
- NVIDIA AmpereA30
- NVIDIA Ampere A2
- NVIDIA Tesla T4
|NVIDIA T4||NVIDIA A2||NVIDIA A30||NVIDIA A100|
|Interface||PCIe 3.0 x16||PCIe 4.0 x8||PCIe 4.0 x16||PCIe 4.0 x16|
|Boost Clock Speed||1590 MHz||1770 MHz||1440 MHz||1410 MHz|
|Memory Size||16 GB||16 GB||24 GB||40 GB|
|Bandwidth||320 GB/s||200 GB/s||933 GB/s||1555 GB/s|
|Memory Bus Width||256 Bit||128 Bit||3072 Bit||5120 Bit|
|TDP||70 W||60 W||165 W||250 W|
Seven Factors to Consider Before Making an Informed Choice About Cloud GPU
Here are seven factors one must consider while choosing a cloud GPU for AI and ML operations:
Interconnection Potential of GPUs
The ability to interconnect GPUs is paramount in any deployment, but is often overlooked when benchmarking for a solution. The scalability of the application, support for multi-GPU, and distributed training methods are crucial factors when selecting a GPU.
For example, the NVLink feature available on the NVIDIA GPUs allows you to interconnect multiple GPUs to increase performance manyfold.
NVIDIA GPUs offer the best support for ML libraries and common frameworks, such as PyTorch and TensorFlow. In addition to GPU accelerated libraries, NVIDIA’s CUDA toolkit includes a C and C++ compiler and runtime, optimization, and debugging tools.
It’s free, easy to install and use as well as offering excellent performance in many modern applications.
Another factor to consider while planning is the licensing of CUDA software. NVIDIA is implementing restrictions that mandate organizations transition to production-grade GPUs.
Due to a licensing update in 2018 consumer grade GPUs can no longer be used in data centers. NVIDIA has a long history of releasing new products, each with the ability to usher in a new era in technology.
Consider the amount of data that needs to be processed by your algorithms. GPUs capable of multi-GPU training are recommended if datasets are large. To enable efficient distributed training, make sure that the servers can communicate swiftly with each other and with the storage components.
Do you plan to model large data sets? For instance, models analyzing lengthy videos or medical images have very large training sets. If so, you should invest in GPUs with relatively large memory.
Performance of the GPU
If your usage involves debugging and development, you won’t need the most powerful GPUs but strong GPUs are necessary for tuning models in the long run. They accelerate the training times and save you from the hassle of waiting hours or even days to run models.
Compatibility with Software and OS
Another factor to consider is whether a GPU is compatible with deep learning software. Most GPU-based programs can run on Windows, but some require a specific OS.
Why Choose Ace as Your Cloud GPU Provider?
Ace is a renowned public cloud service provider to small businesses, SMBs, accountants, CPAs, and IT enterprises.
We offer customizable cloud solutions based on open-source and commercial technologies such as OpenStack, CEPH, KVM, and more. We also provide the latest NVIDIA A series GPUs with resizable GPU instances, which are specially customized for AI & ML workloads.
Ace public cloud services are extremely secure with guaranteed protection against DDoS attacks and provide 24-hour customer service support to take care of all your cloud-related problems. By using OpenStack, Ace eliminates vendor lock-ins.
Ace Public Cloud is hosted in tier 4 and tier 5 data centers to ensure high availability, data security, and redundant storage. We offer simple subscription plans and different compute instances with multiple price options no matter how big or small your requirements are.
Contact us to leverage NVIDA GPUs for your next industry project.
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