INTEL XEON PROCESSORS ACCELERATE GENAI WORKLOADS WITH AIBLE

Intel and Aible, an end-to-end serverless generative AI (GenAI) and augmented analytics enterprise solution, now offer solutions to shared customers to run advanced GenAI and retrieval-augmented generation (RAG) use cases on multiple generations of Intel^® Xeon^® CPUs.

The collaboration, which includes engineering optimizations and a benchmarking program, enhances Aible’s ability to deliver GenAI results at a low cost for enterprise customers and helps developers embed AI intelligence into applications. Together, the companies offer scalable and efficient AI solutions that draw on high-performing hardware to help customers solve challenges with AI and Intel.

Aible’s solutions demonstrate how CPUs can significantly enhance performance across a range of the latest AI workloads, from running language models to RAG. Optimized for Intel processors, Aible’s technology utilizes an efficient serverless end-to-end approach for AI, consuming resources only when there are active user requests. For example, the vector database activates for just a few seconds to retrieve information relevant to a user query, and the language model similarly powers up briefly to process and respond to the request. This on-demand operation helps reduce the total cost of ownership (TCO).

While RAG is often implemented using GPUs (graphics processing units) and accelerators to leverage their parallel processing capabilities, Aible’s serverless technique, combined with Intel^® Xeon^® Scalable processors, allows RAG use cases to be powered entirely by CPUs. The performance data shows that multiple generations of Intel Xeon processors can run RAG workloads efficiently.

Aible enables customers to lower the operational costs of GenAI projects by exclusively utilizing CPUs in serverless form to share the same underlying compute resources more securely across multiple customers. As a comparison, the lowered operational costs can be compared to buying electricity when it’s used rather than renting an electricity generator. Moreover, as demand for generative AI grows, the need to optimize both performance and energy consumption becomes more crucial. Aible's CPU-based services offer customers a cost-effective and energy-efficient solution.

According to Aible’s benchmark analysis, customers can realize up to a 55x cost saving when running RAG models on their CPU-based serverless solutions1. This cost reduction is a testament to the effectiveness of Aible's CPU-exclusive approach, which sidesteps the need for more expensive GPU-based infrastructures with shared services or dedicated servers.

Intel – including Intel Labs – has worked with Aible to optimize AI workloads on Xeon processors. Notably, by optimizing Aible’s code for AVX-512, Aible saw significant performance gains and improved its throughput on Xeon processors, highlighting the impact of strategic software optimizations on overall efficiency.

The combination of RAG models with Intel Xeon processors, facilitated by platforms like Aible, can enable applications such as:

Intel’s collaboration with Aible began with the launch of 4th Gen Xeon processors. The two companies have since optimized AI workloads, code and libraries for Xeon processors to increase performance for Aible’s product offerings.