main blog image

Liberating Global Data Centers in the AI Revolution

Artificial Intelligence is everywhere – from detecting fraud and sharpening chatbot skills to driving cutting-edge cinematic experiences and medical diagnoses.

Beneath this vast AI landscape lies a pivotal force: AI Inference. It operates like the enigmatic wizard behind the curtain, tirelessly deploying and running trained AI models and business AI applications within the world’s data centers. Day in and day out.

What’s Wrong with Today’s Data Centers?

They are just too expensive to operate for all but the richest companies. Last year, OpenAI went on record to say it was bleeding up to $1 million per day since ChatGPT captured our hearts and minds in late 2022. The problem wasn’t the model – it was the expense of running it on inefficient CPU-centric data centers which had never been originally designed for AI Inference.

Fast forward to March 2024. Nvidia’s recent Blackwell announcement was more of the same – an obsession with raw performance and technology leadership without demonstrated concern for affordability. OpenAI and Microsoft’s $100 billion project is more proof of throwing money and hardware at the problem. Yet, more expensive chips and amazing AI applications will not take us very far – if only a small slice of businesses and governments can afford to deploy them.

While AI is flourishing, the costs, complexities, and energy consumption demands of managing AI workloads are skyrocketing. The operating expenses associated with AI inference are projected to become eight times greater than the initial cost of AI training due to the growing demand for power-hungry generative AI, large language models, and consumer desire for AI-enabled services. This calls for a change in the way data centers operate.

it’s time to Change – Businesses Can’t Wait!

The current state of AI inference is not sustainable for businesses – economically or environmentally. The cost barriers are so high that entire industries and businesses are shut out of the AI market. Even if you can afford  it, today’s AI data center deployments put a real strain on profit margins. That’s because AI Inference is tied to operational expenses, while AI Training is typically tied to capital expense or R&D.

If you are a lower margin business like retail, hospitality, oil and gas, or groceries, or you’re a city government working to improve public safety, how will you thrive in the AI revolution? There needs to be a change in the way today’s data centers operate, to reduce AI operational expenses and market barriers.

By implementing a new data center system architecture that is AI-centric, businesses can overcome many performance inefficiencies that plague current CPU-centric data centers. This alternative path – the end-to-end system approach – will enable businesses to slash cost by up to 90% and unlock new innovative AI customer experiences, ultimately boosting their profit margins.

It comes down to pairing the complete NR1™ AI Inference Solution with each AI or Deep Learning Accelerator (DLA) to take full advantage of their powerful capabilities. Quite simply, the NAPU is complementary to all AI chips – unlocking them to go from 30% to 100% utilization and eliminating the CPU performance bottleneck. Net-net, you won’t be standing in line to BUY more AI chips; you’ll be getting MORE from the chips you buy.

Inclusive Vision for ai

At the AIAI Summit in San Jose, April 16-17, NeuReality CEO Moshe Tanach, a seasoned semiconductor industry leader and systems engineer, will share with industry partners and enterprise customers a better way forward – defying the accepted norms of “chip scarcity” by focusing instead on their more efficient use.

He will delve into the insights that sparked the founding of NeuReality in 2019 and its revolutionary NR1 AI Inference Solution and magic components like the NAPU and AI-Hypervisor.

You’ll find our Business Team at Demo Table #19 where we are registering new customers for 2024 NR1 AI Inference pilots to fortify your AI compute infrastructure in advance of generative AI deployments. That way, you have side-by-side performance comparisons in your own data centers with and without NR1’s complete silicon-to-software solution.

Or ask your cloud service provider what they’re doing now at the systems level to reduce your costs and energy consumption for this and coming years. Don’t get left behind.

Please join us at the AIAI Summit for Moshe’s keynote on Day 2: Wednesday, April 17 at 10:00 am (Pacific). Please let us know if you need a pass!