Numenta

Run large AI models on CPUs with peak performance, multi-tenancy, and seamless scaling, while maintaining full control over models and data.
Artificial Intelligence Large Language Models CPU-Only AI Deployment

Numenta is geared to run large AI models on CPUs, with performance that's better price-performance and with full control over models and data. By using its NuPIC (Numenta Platform for Intelligent Computing) system, customers can run Generative AI workloads without GPUs.

Among other benefits, the platform offers:

  • Peak Performance: Fast response times for real-time applications.
  • Multi-Tenancy: Hundreds of models can run on a single server for maximum resource utilization.
  • Seamless Scaling: CPU-only systems can be easily upgraded to meet demand.
  • Simplified MLOps: Infrastructure is easy to manage, with lower overhead costs.

Numenta lets companies fine-tune generative and non-generative Large Language Models (LLMs) on CPUs, keeping data private and in control. It's geared for a variety of industries, including gaming, customer service and document retrieval.

The company has had success with customers including Gallium Studios, Intel and F5, which have seen big performance boosts and cost savings. For example, Numenta helped Gallium Studios run LLMs at "incredible performance" on CPUs, with full control over models and data.

Numenta's technology is based on two decades of neuroscience research that maps how brains work to modern CPU designs. That foundation helps to unlock new AI abilities, and it's a big reason why companies interested in AI want to use it.

Pricing isn't disclosed. Prospects can sign up for a free 30-minute use case consultation on the Numenta website to see how the company thinks the platform can help their specific needs.

For anyone working on AI projects, Numenta has an interesting option, especially if you need to keep data private and models in house. With high performance and scaling on CPU-only hardware, Numenta lets companies tap into the power of large language models without the GPU complexity and expense.

Published on July 18, 2024

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