Context-Aware Inference Optimization

Most enterprises rely on infrastructure providers and serving frameworks — NVIDIA NIM, vLLM, and similar platforms — to handle inference. While these solutions excel at managing hardware utilization, batching requests, and maximizing GPU throughput, they’re built for your infrastructure, not your workload. They have no visibility into your domain, your data, or the nature of your queries.

Context-Aware Inference Optimization (CAIO) changes that.

In this whitepaper, you’ll learn how CAIO delivers faster responses, lower compute costs, and higher accuracy—without changing your models or infrastructure.

Inside, we explore:

• The four CAIO techniques: ReFRAG, Semantic KV Cache Management, Context-Aware Self Speculative Decoding, and Intelligent Model Routing
• How ReFRAG addresses the largest single source of waste in the enterprise RAG pipeline—and how both techniques compare in benchmark testing
• How Uniphore’s ReFRAG implementation delivers production inference intelligence specific to your enterprise