Sam Altman changed the world with his innovation, ChatGPT – a “tool” often touted as a “destroyer of humanity”. Interestingly, today, ChatGPT’s algorithms – Large Langauge Models (LLMs), power hundreds of thousands of software applications and are used by Adobe, Notion, Figma, and Zoom.
In this article, we aim to discuss something even more vital than artificial intelligence (AI) itself. We are speaking of Graphical Processing Units (GPUs) that make LLMs possible. The Walmarts and Morgan Stanleys of the world have already kicked off a race to leverage the computing power of GPUs and here’s everything you need to know about GPUs or cloud GPUs in detail.
Large Language Models (LLMs) in Action
To ensure LLMs work fine, they are fed with large training datasets. This act is often termed “model training”. LLMs and Foundational Models (FM) achieve “reasoning” through model training and in most cases are trained on publicly available datasets like Common Crawl. However, this does not bar the application from training on private datasets. LLMs trained on private datasets need the information to be “parameterized” to ensure it is in a format that the model can use. Each of these parameters is then given a weight to encapsulate how much of it should impact the results from the role.
But, Why Graphical Processing Units (GPUs)?
The entire process explained above requires intense computing resources. GPUs, initially designed for graphics on computers, ensure the resources are allocated in a parallelized fashion and hence find a spot in AI developments.
To realize how important GPUs are, here are a few numbers,
- OpenAI is believed to have used 25,000 NVIDIA A100 GPUs to train their 1.76T parameter GPT-4 model for over 100 days straight.
- It took Meta approximately 1.7 million GPU hours to train its 70B-parameter Llama 2 model. This equates to roughly 10,000 GPUs running over 7 weeks.
- Additionally, Meta announced it will utilize an equivalent of 600,000 NVIDIA H100s to train their upcoming Llama 3 model.
The Big 4 along with other tech behemoths seem to have accelerated innovation with the use of AI models. The days of anyone having free access to any training model are already numbered. Most models are already open-source, which brings us to Cloud GPUs. Experts believe, cloud GPUs have the key to commoditization of AI. Imagine a generation where kids generate AI models in a snap!
Here is more on how Cloud GPUs play a pivotal role in the entire development.
Leveraging cloud GPUs to boost the performance of AI/ML Workload
Cloud GPUs represent specialized computing instances within the cloud infrastructure. The entire setup features the use of hardware tailored to handle demanding computational tasks. This setup differs completely from traditional CPU-based systems as GPUs are well-optimized for parallel processing. This also makes them exceptionally good at tackling workloads requiring vast data crunching and mathematical computations. This inherent parallelism of GPUs makes them ideal for applications like analytics, deep learning, computer-aided design (CAD), gaming, and image recognition.
Apart from faster computation rates than CPUs, one of the principal benefits of having GPUs in the cloud is eliminating the need for physical deployment on local devices. In arrangements such as these, users can access powerful computing resources remotely via cloud service providers. This not only streamlines infrastructure management but also enables seamless scalability, allowing organizations to leverage computing resources based on varying demands.
The role of GPUs in model inference
Post the training stage, large foundation models require continuous computation capabilities to run. This is called inference. Training of LLMs often requires significantly large clusters of interconnected GPUs running over an extended period. Model inference, however, requires much less computing capacities depending on when a model is prompted and hence cannot be ignored at all costs.
Cloud GPUs vs. Traditional GPUs: The Business Outlook
Cloud GPUs, such as the popular NVIDIA GPUs, differ from traditional GPUs in several aspects including usability and cost. Here are some key aspects to help us differentiate one from another.
| Parameters | Traditional GPUs | Cloud GPUs |
| Cost | Expensive because of the constant upgradation and maintenance of devices. | Cost-effective. Commercially more viable. |
| Scalability | Hard to scale. Requires significant financials and human resources. | Easier to scale and retract based on usage. |
| Access | Lacks arrangements. | Service providers use high-performance devices and deep learning algorithms to speed up the entire process. |
| Resource Utilization | Local resources are not efficiently used, affecting performance. | Don’t consume local resources and frees up devices for uninterrupted operations |
| Ease of Use | Lacks versatility. | Offers large varieties of platforms, making it easier to run models using popular machine learning algorithms across platforms |
| Total Cost of Ownership (TCO) | High TCO because of licensing models | Follows a pay-as-you-go model |
Elevate your computing power with ZNetLive Cloud GPU
Experience unmatched performance with ZNetLive’s Cloud GPU plans, designed to handle the most demanding tasks with ease. Whether you’re into gaming, content creation, or other high-performance computing needs, our GPUs offer the power you need at an affordable price.
Why ZNetLive?
- Our NVIDIA GPU cloud ensures seamless scalability and is supported by top-tier infrastructure from NVIDIA.
- Enjoy the best price-to-performance ratio with flexible pricing options—no long-term commitments or hidden fees.
- Achieve guaranteed performance for your high-performance computing tasks without straining your local resources.
- Benefit from 24/7 dedicated support.
Explore ZNetLive’s Cloud GPU plans today and take your AI/ML computing to the next level.
Read next: How can you save on egress costs while using cloud storage?
