液态AI只是宣布液态RIPL，简称为实时推论管道。 RIPL是低纬度引擎，服务于液态基础模型(LFMs)在实时请求路径内。 它以50毫秒以下返回完整生成的回复。 另外，在交通连续高峰期到达时，耐用性也维持着声称。 对于开发者来说，消息很简单。 把渐变的树换成基因模型，并保留延迟预算。
TL；数据
- 发动机：从230M到2.6B参数的LFM2.5型号
- 运行：企业部署NVIDIA H100、H200、B200和RTX PRO 6000 GPU。 基准测试使用单机B200.
- 性能：5.6至28.4x在vLLM上比Qwen3.5低p95的耐久性，每个液体本身的试验。
- 最佳：LFM2.5-1.2B击出155.8 ms p95对Qwen3.5-0.8B的4 431 ms,28.4x差距(256 in,128 out).
- 最糟的是：LFM2.5-2.6B只获得5.6x(449.1毫秒对2 499毫秒)。 sub-50 ms在更长的请求中失败。
LIPL解决了什么问题？
大多数服务堆栈都针对大批量和长期代理工作。 每个请求可接受几秒钟。 搜索排名，欺诈检查和支付授权需要以毫秒计的答案。 Liquid AI认为团队在这些路径上默认为古典模型，因为LLM服务太慢。 建立RIPL是为了消除这种权衡。
RIPL是怎么工作的？
Liquid AI描述RIPL优化了整个路径，从请求到达到完成响应。 在几个工作量中，液态AI在vLLM和RIPL上绘制了相同的型号LFM2.5-350M. RIPL的p95线在每一个案件中都远远低于vLLM线。 这隔离了发动机的部分收益，而不仅仅是模型建筑。
LFM2.5型号本身是杂交种。 LFM2.5-1.2B-Instruct有16层：10个双加成的演化区块和6个GQA区块。 LFM2.5-2.6B有30层：22个演化区块和8个GQA区块。
RIPL 基准测试：它对vLLM有多快？.
液态测得的p95端到端耐久性在一次B200上的露天波森抵达下，LFM运行在RIPL上。 基线，包括MiniCPM5和Granite 4.2，运行在vLLM上。
资料来源：液态AI博客，图2 西尔夫报告。
液态AI还对照固定预算绘制了6实时的工作量图：
- Adtech查询重写：32 in,32 out,50ms预算，LFM2.5-350M.
- 搜索意图分类：128英寸，1英寸，20毫秒预算。
- 搜索重排：1公里，1出，30米的预算。
- 支付风险评分：512分，16分，50毫秒预算，LFM2.5-1.2B。
- 信用决定：8千分之六十四，150毫秒的预算。
- 图像替代文本：512x512 图像和64个信使，16个出，100ms预算，LFM2.5-VL-450M.
竞争对手比较
RIPL是否用于生产？
是的，根据液体AI。 Shopify正在部署LFM，涵盖对耐久性敏感的商业工作量。 在搜索增强中，LFMs运行时对生产流量有子20ms推论。 使用比替代品少50%的参数
关键外卖
- RIPL是Liquid AI的引擎
- 液态声称 5.6至28.4x在vLLM上比Qwen3.5低p95纬度。
- 同型测试(LFM2.5-350M)显示RIPL本身击败vLLM.
- Shopify在生产搜索中以子-20 ms的速度运行LFM.
- 仅限企业，关闭，所有数字都是自报的。
检查RIPL的公告，试用潜伏计算器，并查看LFM2.5型号卡片。 所有功劳都归功于这个项目的研究人员。 并加入我们的通讯。 等等你在电报里吗现在你也可以加入我们的电报。
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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.
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.
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.
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
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 Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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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.
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