# Liquid AI launches sub-20ms latency stack > Source: > Published: 2026-10-22 22:11:16+00:00 [News](https://www.liquid.ai/news) # RIPL: Real-time Intelligence When Every Millisecond Counts Today we're announcing **Liquid RIPL (Real-time Inference Pipeline)**, a **low-latency inference engine** built to put Liquid Foundation Models (LFMs) directly in the path of a live request. It returns complete generated model responses end-to-end in **under 50 ms**, and it keeps doing so while traffic arrives continuously at peak load. Most of today's serving infrastructure is built for a different job: general purpose models, large batches, and long running agentic workflows where a few seconds per request is acceptable. Enterprise real-time workloads have far smaller latency budgets. Search rankings, product recommendations, fraud decisions, payment authorizations, and other real-time workloads need an answer in milliseconds. Even small delays add friction to the user experience, like a slower checkout that hurts conversion. That's why these systems have traditionally used fast, but less accurate, methods like gradient-boosted trees or small embedding models. **Liquid’s RIPL** changes that trade off. It’s built for real-time constraints, delivering faster, more responsive generative AI experiences that improve how customers search, discover, transact, and interact.  ## Results across real-time workloads In Figure 1, we highlight a few different use cases for small LLMs in real time enterprise workflows, enabled by low-latency LLM inference. Across the two workloads in Figure 2, LFM2.5-1.2B on **Liquid RIPL** has **14.4–28.4× lower p95 latency** than Qwen3.5-0.8B on vLLM. LFM2.5-2.6B has **5.6–7.7× lower p95 latency** than Qwen3.5-2B.