In the last few years, the AI landscape has seen a surge in the development of large language models (LLMs), sparking an explosion of applications across sectors. Graphics processing units (GPUs) have traditionally been the go-to hardware for anyone looking to run large-scale AI models, despite the fact that they rely on brute force, are extremely inefficient, and complex to maintain. Fueled by the widespread adoption of AI technologies, they’re also increasingly unavailable. It’s now common for businesses to wait up to a year to secure the latest GPUs. It’s clear that an alternative solution is needed.
Advantages of Deploying Large AI Models on CPUs with NuPIC
Traditionally, central processing units (CPUs) have been overlooked for high-performance machine learning tasks as they are designed to handle tasks sequentially rather than in parallel. However, the Numenta Platform for Intelligent Computing (NuPIC) opens a new chapter in this narrative. Based on decades of proprietary neuroscience research, we’ve developed a platform that enables models to run highly efficiently on CPUs. By leveraging the inherent flexibility of CPUs and recent architectural advantages, NuPIC has rendered CPUs a surprisingly superior solution for deploying large-scale AI models. Here are a few reasons:
1. No batching required
CPUs are general-purpose processors that can handle individual data inputs quickly and efficiently, which minimizes latency, making them ideal for time-sensitive applications like online gaming, recommendation systems or customer support services. Fast bulk processing on GPUs requires grouping data in batches, which introduces complexity and delays. By running NuPIC on CPUs, you can avoid batching altogether, which makes things much simpler and reduces latency.
2. Run multiple models on the same server
Many business applications leverage multiple LLMs for different sub-tasks, which have different sets of weights and vary in their computation requirements. Because CPUs have more access to memory than GPUs typically do, they can effectively handle multiple models concurrently, keeping pace with the demands of multiple users or applications. Since NuPIC-optimized models run a lot faster than traditional models, you can deploy many more large-scale AI models and handle a larger number of clients on the same server. These capabilities are prohibitively difficult, if not impossible, on GPUs.
3. Scale seamlessly
As your data and computational needs grow, it is much simpler and less restrictive to add more CPUs to your system vs. GPUs. This is especially true in cloud-based settings where increasing computational capacity is often just a click away. Driver and software compatibility relating to GPUs can also be highly specific, and therefore fragile. CPUs are generally easier to set up and maintain, an advantage for businesses with limited resources or deep learning expertise. They can seamlessly integrate NuPIC with existing CPU-based infrastructures, which reduces cost and complexity.
4. Reduce energy and cost
CPUs often come with a lower price tag compared to GPUs, rendering them a more feasible choice for a wide range of businesses. When paired with software that enhances model speed and efficiency, such as NuPIC, CPUs consume a lot less energy than GPUs, resulting in considerable cost savings in the long term. Their lower energy usage not only lowers costs, but also reduces the environmental footprint, positioning CPUs as a more sustainable option over time.
5. Maintain 100% privacy
NuPIC is fully containerized and runs on your own infrastructure, whether on-premise or in your private cloud. This means that you are in full control of your models and data and can update them whenever and wherever. No data or model information is ever sent to our servers, ensuring complete data privacy and security, crucial aspects in today’s digital age.
Conclusion
In the face of the ongoing GPU shortage, it’s time to explore the untapped potential of CPUs as another alternative. NuPIC makes this possible. Deploying NuPIC on CPUs not only makes your LLM deployment more efficient, but also future proofs your operations in an increasingly uncertain hardware market.
If you’re ready to get your AI initiatives off the ground, you need a trusted AI platform that is efficient, scalable, and secure. With NuPIC, running LLMs on CPUs becomes more than a possibility – it becomes a strategic advantage.
If you’d like to learn more about Numenta’s work in accelerating LLMs on CPUs, request a demo or check out the resources below:
Charmaine Lai • Marketing Manager