Written by Moshe Tanach, CEO & Co-Founder, NeuReality

A solution for more affordable, faster, and scalable AI deployment.

In the world of artificial intelligence (AI), every software developer is working on the next big thing—designing, implementing, and testing new deep learning models and AI applications. They have trained AI models to write content creation tools, create chatbots, support data analytics, enhance risk modeling, improve financial forecasting, and tighten cybersecurity—all contributing to commercial productivity. Even before the ChatGPT love affair, the problems did not rest so much with AI training as with AI inference, meaning the costly and complex deployment of trained AI models into real-world applications.

The tech industry must turn its attention to AI inference, which is the critical second phase of AI. It’s not only about developing more accessible affordable AI-centric hardware infrastructure to prop-up AI Inference, but also the software development kits and well-architected application programming interfaces (APIs) that make it easier for software developers to install and use trained AI models. The role of software optimization in AI inferencing cannot be overstated.

Making AI accessible by making it affordable 

AI inference has long been a blind spot. The daily costs of AI inference associated with ChatGPT—with reports of one million dollars and higher daily—have pricked the ears of both the tech and non-tech market sectors.

NeuReality anticipated this problem years ago. Although CPUs have helped manage complex AI workflows, they were never designed to host the most advanced deep learning accelerators (DLAs) deployed in three main types of microchips: graphic processing units (GPUs), application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs). 

CPU-centric data centers and system architecture are unsuitable when attempting to process extensive amounts of information in the milliseconds required for real-time AI. Moreover, the high cost per AI query makes it impossible for many use cases to deploy and for entire market sectors to participate in the AI age. Our mission at NeuReality was to design an entirely different system architecture to enable a truly AI-centric data center that is accessible to all.

A CPU-centric architecture is akin to a car that can only run as fast as its engine will allow. If the engine in a smaller car is replaced with one from a sports car, the smaller car will fall apart from the speed and acceleration the stronger engine is exerting.

The same applies to a CPU-led AI inference system. A DLA motoring at breakneck speed completing thousands of inference tasks per second will not achieve its full capability with a limited CPU reducing its input and output. Even when an accelerator covers the heavy lifting of a deep learning model processing task, CPUs saturate and reach the peak of their capabilities when processing millions of identical instructions. Ultimately, this bottleneck makes the investment in high-end DLAs a waste.

A complete software stack 

AI creation usually comes with quite a bit of trial and error. Software developers must deal with various solution layers, investing their time in deploying the AI-model on high-end DLAs and optimizing the complete pipeline embedding this model. On top of that, developers must focus on data movement and preparation to feed that model’s inputs as well as integrate and wrap it with a service layer that allows remote clients to connect and consume the service. Lastly, in order to become citizens in modern data centers, those servers must integrate with existing orchestration and provisioning layers to support large-scale, dynamic AI deployment with high availability.

In designing AI inference of the future, NeuReality’s priority has always been to ensure it is accessible, available, and affordable to all, because this is what businesses large and small need to be commercially viable, especially outside the tech industry. This means architecting a complete solution inclusive of robust software tools and inclusive APIs to deploy any trained AI model in any development environment, to connect any AI workflow to any environment, and to offload the complete AI pipeline with tools covering orchestration, provisioning, and runtime.

With an AI service built using this holistic approach, the tech industry can now seamlessly integrate more powerful, affordable, and energy-efficient AI Inference solutions into scientific and commercial data centers—and fully support the growing desire and demand for large language models (LLMs), computer vision models, recommendation engines, financial risk modeling, conversational and generative AI, and beyond. Making AI easy.

Boosting AI inference performance 

At NeuReality, we recently launched the NR1 AI Inference Solution, which diverges significantly from conventional servers by harnessing embedded networking, heterogeneous computing, and hardware-based AI pipeline hypervision. Our inference solution stack—with three software layers focusing on model processing, pipeline processing, and services—transforms the overall AI experience while streamlining and enhancing the deployment process for developers, users, and DevOps and IT personnel.

In developing AI pipelines with our software development kit (SDK), developers are given significant flexibility. They can choose to use it as a part of the platform, similar to how NVIDIA MIG works to allocate a part of a GPU, for example. Alternatively, they can exploit pre-made setups, called compute graphs (CGs), in various AI applications to provide the system with more diversity in how that service can be deployed optimally across available accelerator resources. This agility allows developers to deploy the most advanced and complex pipelines more easily, based on the specific needs of their projects.

Through the introduction of our innovative AI Inference Solution, NeuReality has seen performance increase ten-fold and cost savings of up to 90% on AI operations per dollar. It’s a dramatic paradigm shift versus incremental improvements to the world’s data centers. And with the novel Network Addressable Processing Unit (NAPU), this complete hardware and software solution has dramatically lowered total cost of ownership. As a result, we are on the right path to democratizing AI for all businesses and government entities large and small, including the most recent generative AI and large language models that currently impede business profitability.

Join the Democratization of AI 

Today’s businesses are already struggling to run commonplace AI applications affordably—from voice recognition systems and recommendation engines to computer vision and risk management—with generative AI’s widespread penetration blocked by that financial struggle.

AI requires an entirely new AI-centric design ideal for inferencing: a solution that keeps up with the deluge of requests asked of it, performs optimally while not costing the earth, and—most importantly—becomes a reliable, collaborative tool for software developers rather than one more burden to navigate. By doing so, we can help unleash the next great human achievement together, whether it’s combating diseases, enhancing public safety, or creating exciting new software opportunities for the AI job market.