In the first phase of the project, Fsas Technologies creates optimized blueprints to evaluate how various infrastructures perform with different Large Language Models (LLMs). This approach utilizes data synthesis (which enables custom AI without large datasets), refinement, and techniques such as knowledge distillation (to build smaller, more efficient models), and thorough benchmarking of key performance indicators (KPIs) such as reasoning ability and Retrieval Augmented Generation (RAG).
These benchmarks are also applied to the core solution of Fsas Technologies: Private GPT, a secure, on-prem Generative AI platform – to identify and improve bottlenecks. Through strategic integration with advanced storage technologies from partners, the company ensures that the scalability and efficiency of Private GPT are maintained as features are added.
In addition to benchmarking, Fsas Technologies also develops AI Validated Designs and real use case scenarios with PRIMERGY hardware. These comprehensive blueprints assist customers in customizing and configuring AI infrastructure to meet their specific needs – whether they are starting from scratch or looking to scale existing systems. With the support of European expert consultants, organizations gain access to advice on infrastructure, best practices, workshops, and integration strategies.
Additionally, Fsas Technologies is preparing version 1.4 of Private GPT. This includes better understanding of documents, increased security and stability, smarter interactions, and support for audio and video integration. It illustrates how the company continues to commit to AI innovation.
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