Liquid AI Launches RIPL Engine, Claims Up to 28x Lower Latency Than vLLM for Small Models
Liquid AI released RIPL, a real-time inference engine. Self-reported benchmarks show LFM2.5 models on NVIDIA B200 achieving 5.6x to 28.4x lower p95 latency than Qwen3.5 on vLLM.
Liquid AI announced the launch of RIPL (Real-time Inference Pipeline) on October 22, 2026. This low-latency engine is designed to serve Liquid Foundation Models (LFMs) within live request paths, targeting end-to-end response times under 50 milliseconds.
According to benchmarks published by Liquid AI, the LFM2.5-1.2B model running on RIPL demonstrated 14.4x to 28.4x lower p95 latency compared to the Qwen3.5-0.8B model running on vLLM, tested on a single NVIDIA B200 GPU. Specifically, in a workload with 256 input and 128 output tokens, LFM2.5-1.2B achieved a p95 latency of 155.8 ms versus 4,431 ms for Qwen3.5-0.8B. However, the advantage narrowed to 5.6x for the larger 2.6B model, which failed to meet the sub-50 ms target on longer requests.
Liquid AI describes RIPL as optimizing the entire path from request arrival to completed response, rather than relying solely on model architecture. The company presented comparisons using the same model (LFM2.5-350M) on both RIPL and vLLM, indicating that the engine itself contributes significantly to the speedup. RIPL is currently available only for enterprise deployments on NVIDIA H100, H200, B200, and RTX PRO 6000 GPUs.
The announcement also noted that Shopify is deploying LFMs in production for latency-sensitive tasks like search enhancement, with inference times below 20 ms. It is important to note that all performance figures are self-reported by Liquid AI and have not been independently reproduced.