Liquid AI 发布 RIPL 实时推理引擎，LFM 端到端延迟低于 50 ms

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Home Editors Pick Agentic AILiquid AI Launches RIPL, a Real-Time Inference Engine for Sub-50 ms LFMs

Liquid AI Launches RIPL, a Real-Time Inference Engine for Sub-50 ms LFMs

Asif Razzaq

October 22, 2026

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Liquid AI just announced Liquid RIPL, short for Real-time Inference Pipeline. RIPL is a low-latency engine that serves Liquid Foundation Models (LFMs) inside a live request path. It returns complete generated responses end-to-end in under 50 ms. It also claims that latency holds while traffic arrives continuously at peak load. For developers, the message is simple. Swap gradient-boosted trees for a generative model, and keep the latency budget.

TL;DR

Engine: Serves LFM2.5 models from 230M to 2.6B parameters
Runs on: Enterprise deployments on NVIDIA H100, H200, B200 and RTX PRO 6000 GPUs. Benchmarks used a single B200.
Performance: 5.6 to 28.4x lower p95 latency than Qwen3.5 on vLLM, per Liquid’s own tests.
Best: LFM2.5-1.2B hit 155.8 ms p95 vs 4,431 ms for Qwen3.5-0.8B, a 28.4x gap (256 in, 128 out).
Worst: LFM2.5-2.6B gained only 5.6x (449.1 ms vs 2,499 ms). Sub-50 ms fails on longer requests.

What problem does Liquid RIPL solve?

Most serving stacks target large batches and long agentic jobs. A few seconds per request is acceptable there. Search ranking, fraud checks and payment authorization need answers in milliseconds. Liquid AI argues teams default to classical models for these paths because LLM serving is too slow. RIPL is built to remove that trade-off.

Open SourceOmniParseBench · by Datalab · Apache 2.0 Benchmark any document parser on tables, math, forms and handwriting.Yes/no tests per page across text, table and layout families. Score vendor parsers on the Hugging Face dataset or your own PDFs with none score and none predict primitives.★ Star on GitHub Multi-column, rotated text, degraded scans · tagged by script and language github.com/datalab-to/omni_parse_bench Fork it · Add your parser

How does RIPL work?

Liquid AI describes RIPL as optimizing the full path, from request arrival to completed response. In several workloads, Liquid AI plots the same model, LFM2.5-350M, on vLLM and on RIPL. RIPL’s p95 line sits well below the vLLM line in each case. That isolates part of the gain to the engine, not just the model architecture.

The LFM2.5 models themselves are hybrids. LFM2.5-1.2B-Instruct has 16 layers: 10 double-gated convolution blocks and 6 GQA blocks. LFM2.5-2.6B has 30 layers: 22 convolution blocks and 8 GQA blocks.

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RIPL benchmarks: how fast is it vs vLLM?

Liquid measured p95 end-to-end latency under open-loop Poisson arrivals on one B200. LFMs ran on RIPL. Baselines, including MiniCPM5 and Granite 4.2, ran on vLLM.

Workload	Load	LFM2.5 on RIPL (p95)	Qwen3.5 on vLLM (p95)	Gap
1B class, 32 in → 32 out	1,000 req/s	34.6 ms (1.2B)	498 ms (0.8B)	14.4x
1B class, 256 in → 128 out	400 req/s	155.8 ms (1.2B)	4,431 ms (0.8B)	28.4x
2B class, 32 in → 32 out	1,000 req/s	76.4 ms (2.6B)	586.8 ms (2B)	7.7x
2B class, 256 in → 128 out	300 req/s	449.1 ms (2.6B)	2,499 ms (2B)	5.6x

Source:Liquid AI blog, Figure 2. Sealf-reported.

Liquid AI also mapped 6 real-time workloads against fixed budgets:

Adtech query rewriting: 32 in, 32 out, 50 ms budget, LFM2.5-350M.
Search intent classification: 128 in, 1 out, 20 ms budget.
Search reranking: 1k in, 1 out, 30 ms budget.
Payments risk scoring: 512 in, 16 out, 50 ms budget, LFM2.5-1.2B.
Credit decisioning: 8k in, 64 out, 150 ms budget.
Image alt text: 512×512 image and 64 tokens in, 16 out, 100 ms budget, LFM2.5-VL-450M.

Competitor Comparison

Feature	LFM2.5-1.2B on RIPL	Qwen3.5-0.8B on vLLM	LFM2.5-2.6B on RIPL	Qwen3.5-2B on vLLM
Parameters	1.17B	0.8B	2.69B	2B
Context	32,768	262,144	131,072	262,144
License (weights)	LFM Open License v1.0	Apache 2.0	LFM Open License v1.0	Apache 2.0
Modality	Text	Text, image, video	Text	Text, image, video
Default mode	Instruct	Non-thinking	Always reasons	Non-thinking
Serving engine	RIPL (enterprise)	vLLM (open source)	RIPL (enterprise)	vLLM (open source)
p95, 32→32	34.6 ms	498 ms	76.4 ms	586.8 ms
p95, 256→128	155.8 ms	4,431 ms	449.1 ms	2,499 ms

Is RIPL used in production?

Yes, according to Liquid AI. Shopify is deploying LFMs across latency-sensitive commerce workloads. In search enhancement, LFMs run with sub-20 ms inference on production traffic. According to Liquid AI’s blog, they use about 50% fewer parameters than alternatives and deliver 2 to 10x faster inference.

Key Takeaways

RIPL is Liquid AI’s engine for LLMs inside live request paths.
Liquid claims 5.6 to 28.4x lower p95 latency than Qwen3.5 on vLLM.
Same-model tests (LFM2.5-350M) show RIPL itself beats vLLM.
Shopify runs LFMs at sub-20 ms in production search.
Enterprise-only, closed, and all figures are self-reported.

Check out the RIPL announcement, try the latency calculator, and see the LFM2.5 model cards.. All credit goes to the researcher of this project. Also,feel free to follow us on**Twitterand don’t forget to join our150k+ML SubRedditand Subscribe toour Newsletter. Wait! are you on telegram?now you can join us on telegram as well.**

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Asif Razzaq

Website|+ posts Bio ⮌

Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.