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
'Customers are looking for efficient, enterprise-grade solutions to harness the power of AI. Our collaboration with Aible shows how we're closely working with the industry to deliver innovation in AI and lowering the barrier to entry for many customers to run the latest GenAI workloads using
Mishali Naik,
About Xeon's GenAI Performance: 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
While RAG is often implemented using GPUs (graphics processing units) and accelerators to leverage their parallel processing capabilities, Aible's serverless technique, combined with
Why It Matters: 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.
How Aible Solutions Help Customers Lower Costs: 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.
How
The combination of RAG models with
Natural language processing (NLP)
Recommendation systems
Decision support systems
Content generation
What's Next:
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