At Q2BSTUDIO we combine expertise in software development, custom applications, artificial intelligence, cybersecurity, AWS and Azure cloud services, business intelligence services, and Power BI to offer innovative solutions tailored to each project
The inference process of large language models is divided into a prefill phase, where the model incorporates initial embeddings and generates internal representations, and a decode phase, where through autoregressive attention it produces each new token leveraging stored keys and values
The transformer architecture is based on self-attention blocks, multi-head attention mechanisms, feed forward networks, and normalization layers that capture complex relationships in text, ensuring efficiency and scalability
The KV cache stores, for each layer and each past token, the key and value matrices, optimizing performance during the decode phase, avoiding unnecessary recalculations, and accelerating real-time response generation
Our AI solutions for businesses include custom AI agents that rely on this inference process and KV cache to deliver fluid and contextual interactions, improving productivity and user experience
