(US and Canada) Artificial intelligence (AI) can be a game-changer for businesses, unlocking new possibilities and outcomes. Large Language Models (LLMs) and Generative AI models are recent examples of extreme transformational change, enabling advanced capabilities in applications like chatbots, software code generation, and even the understanding of complex bio-pharmaceuticals.

However, these types of innovations have been out of reach for many only until a few years ago, and they are still mostly inaccessible due to various constraints. Building and running AI models efficiently in production can be challenging, particularly for organizations that lack in-house expertise, sizable budgets,  and other key technology resources. For most enterprises, even with skilled data scientists, large data sets and business knowledge, the move to an AI production environment can be cost prohibitive and difficult to support.  

This is where Neural Magic can help with their powerful optimization toolkit and runtime that allows companies to run their machine learning initiatives more efficiently, accurately and performant on commodity CPUs.

Their value proposition is two-fold, delivering both business and technical benefits. From a business perspective, Neural Magic enables organizations to leverage existing infrastructure, reduce operational costs, and drive better performance for machine learning projects. IT teams can manage the infrastructure they already know. And data scientists can focus on deriving business insights instead of dealing with the optimization headaches typically found in many machine learning projects due to their heavy processing requirements.

These heavy processing requirements often lead to machine learning models are often tethered to expensive physical infrastructure like GPUs. This approach leads to overhead and fragmentation when trying to build at-scale AI solutions, as performance optimization and hardware decisions must be built into each bespoke project/solution. So from a technical standpoint, Neural Magic’s optimization toolkit and runtime allows customers to run their AI projects at GPU speeds on commodity CPUs. This means data scientists can focus on building and training models without adding a burden on hardware and technology spend.  

Neural Magic’s use cases apply to a variety of industries, including retail and manufacturing.

For retail, there is a growing trend to leverage AI for transactional, security, and inventory tracking purposes. An example that has garnered recent attention due to its innovative use of AI is Amazon’s "grab and go" experiences at Amazon Go stores, which leverage a significant amount of sophisticated AI technology. But the amount of investment, in terms of time, resources and budget required to support an initiative like an “Amazon Go”-like experience is not something most retailers can readily take on. Simply said, it is feasible for an organization like Amazon to execute innovative solutions at scale even if it is just for R&D purposes, but not for everyone.

The tremendous scale of an AI project for physical retail stores can involve numerous high-resolution cameras and intense compute requirements, a daunting challenge for most cost-conscious retailers. To help take these types of initiatives from ideas into actual operations is Neural Magic, a software-based AI solution. Neural Magic has pioneered a groundbreaking solution that has set a new standard in the realm of computer vision inferencing on commodity hardware. By employing their state-of-the-art DeepSparse Runtime running on  regular  CPUs, Neural Magic has been able to show a remarkable reduction in latency of over 50%, along with an impressive 80% cost savings vs typical GPU-based solutions. A prominent large retailer even shared that they have been able to double the number of cameras employed in their test stores for a typical customer counting computer vision use case without any additional computing hardware investment. This achievement represents a significant step forward for retailers who want to implement technology at a lower cost and with greater efficiency, with the use of Neural Magic’s revolutionary DeepSparse Runtime.

Another industry example is manufacturing.  From scrutinizing defective products on the production line to enabling autonomous vehicles and robotics to process real-time information with impressive sub-millisecond speed, the impact of machine learning on Industry 4.0 and beyond is profound. Yet, these manufacturing use cases present even more significant challenges, as they require diverse solutions across a broader range of use cases that require scaled machine learning. This further complicates the linkage between machine learning models and supportive hardware.

This is where Neural Magic’s innovative container-based solution makes an impact, as it seamlessly incorporates auto-scaling and other cloud-native attributes, unifying model optimization and operational simplicity into a single platform. The Neural Magic team has been working closely with these customers to grapple with the overwhelming costs and complexity of managing numerous distinct machine learning models on costly GPU infrastructure. With Neural Magic’s container-based solution, these customers can significantly reduce expenses while simultaneously streamlining their infrastructure.

Neural Magic works closely with major chip manufacturers to ensure maximum performance of their inference runtime on CPU processors. The company is also an APN partner with AWS and a certified "Cloud Build" partner on Google Cloud, optimizing solutions for both data centers and public clouds. Edge use cases for machine learning are another area that was previously not possible due to performance issues, that customers can now take advantage of with Neural Magic. The company has shown up to 1,000x performance increases on certain types of natural language processing use cases. 

Tackling AI initiatives can feel daunting. Neural Magic can help you move projects from pilot to production, unlocking new possibilities with AI and creating improved customer experiences. To learn more about the startup, CDO Magazine recently moderated a discussion with leadership from Neural Magic to hear how the team got started and what they’ve been learning from customers as they continue to innovate. For additional information on Neural Magic’s technology and to understand the various use cases for their products, visit their NeuralFlix page.