Earlier this week, the AI startup Liquid, formed in 2023 by former MIT computer scientists, debuted LFM2.5-2.6B, a new open-weight language model designed specifically for agentic workloads. In release materials and a recent interview with VentureBeat, Liquid's researchers said LFM2.5-2.6B can run entirely on local hardware — from smartphones and laptops down to a Raspberry Pi — without relying on cloud inference or GPUs, unlocking edge AI applications and giving more options to enterprises working in regulated industries or with sensitive information they don't want to send up to the cloud. It's best suited for high-volume, well-defined agentic tasks that run locally — tool calling, document management, calendar and workflow automation, and always-on background routines — and for connectivity-limited environments like vehicles and robotics, though coding-heavy work is better left to larger models. Even for those businesses without such concerns, the appeal of running performant, task-specific agents at the cost of essentially electricity, may be enough to make the new model quite appealing. But the custom open weights license, as with Moonshot's larger frontier model Kimi K3 released last month, is worth a close look by enterprise legal teams. The basics LFM2.5-2.6B contains 2.6 billion parameters, supports a 128,000-token context window, and includes native tool calling. The somewhat tricky name is explained by the generation of model (2.5) combined with the parameter count (2.6B). Both the post-trained model and a base checkpoint (LFM2.5-2.6B-Base) for developers who want to fine-tune it are available now on Hugging Face, with day-one support for major inference stacks including llama.cpp, MLX, vLLM, SGLang, and ONNX — positioning it for deployment across consumer hardware, enterprise infrastructure, and embedded systems. Liquid also offers an open source fine-tuning framework, LEAP. Rather than positioning LFM2.5-2.6B as a competitor to the largest frontier models, the company is making a different argument: that a sufficiently capable small model can unlock categories of enterprise applications where latency, privacy, deployment flexibility, or inference costs matter more than absolute benchmark leadership. "I do also believe that the best models will be in the cloud, and there's no problem with that," Maxime Labonne, Liquid AI's head of post-training, told VentureBeat in an interview following the launch. "We want to make models for another type of user, and the best way of describing it is: you should use (edge AI) when you can't use a cloud model." Small enough for a Raspberry Pi Asked about the minimum viable hardware, Labonne said the model runs "very, very well" on CPUs — and that the LFM2 architecture underlying the model was explicitly designed around real-world CPU performance rather than GPU benchmarks. "I think the best example is a Raspberry Pi," he said. "We have a lot of demos that show that actually, it works pretty fast on the Raspberry Pi." Company-reported measurements indicate decoding throughput of approximately 220 tokens per second on an Apple M5 Max and 113 tokens per second on an AMD Ryzen AI Max+ 395, while using less than 2.5 GB of memory — and around 30 tokens per second on a smartphone. Users can try the models on their phones through Apollo, Liquid AI's mobile app. At the other end of the deployment spectrum, Liquid AI reports the model reaches nearly 15,000 output tokens per second on a single Nvidia H100 GPU under sustained concurrent load — roughly 1.3 billion tokens per day on one card. These figures are vendor benchmarks and have not been independently verified. For Labonne, memory footprint and speed are not conveniences but hard constraints that determine what can be deployed at all. "What we want to show is that it's a really good trade-off, because you get the level of quality that you get with much bigger models, but in a tiny, tiny form factor," he said. "You can deploy it in target devices where you are not able to deploy the other ones at all." Trained for agents instead of chatbots Liquid AI says LFM2.5-2.6B was developed around the assumption that language models are increasingly consumed through agent frameworks rather than traditional conversational interfaces. "Models are not consumed in chatbots anymore. They're really consumed through agentic harnesses, like OpenClaw, like Hermes Agent," Labonne said. "We wanted to make sure that this model is not just good at math or at code, but it's good at using tools." The model is pretrained on approximately 34 trillion tokens, with a vocabulary doubled to 128K to better support non-Latin scripts and a dedicated mid-training phase to extend the context window to 128K tokens for long-running agent workflows. Post-training follows a four-stage pipeline: supervised fine-tuning, teacher specialization (training separate expert models for domains like instruction following, math, code, and tool use), multi-domain on-policy distillation (MOPD) to merge those experts' capabilities back into a single student model, and finally agentic reinforcement learning. During that last stage, the model was trained directly inside production agent harnesses — including Hermes Agent and OpenClaw — on realistic productivity tasks involving research, coding, document management, tool invocation, and workflow automation, exposing it to those harnesses' actual tools, system prompts, and interaction patterns. Labonne described the pipeline overhaul as producing a "happy accident": gains that extended well beyond the agentic targets. "Through these new training techniques, we also got a lot better at everything. We got better at math, at instruction following. We've never been good at code, actually — and with this, we even got really good at code," he said. Building the model — and the harness Notably, Liquid AI also built its own agent harness rather than relying solely on existing frameworks, and demonstrated the model running inside it on a phone, planning and calling tools entirely on-device. "This is a harness running on a phone, and I don't know if there's any other harness running on a phone," Labonne said. The company had two reasons, he explained. The first was necessity — no phone-native harness existed. The second is a different interaction model: today's harnesses wait for a prompt, and Liquid AI wants

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