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 assistants that act on their own.
«We want proactive agents. We want agents that run in the background, check what you’re doing, check your calendar, and based on this context, do tasks,» he said. «That doesn’t exist today, really.»
Co-designing the harness and model also lets the software compensate for the model’s weak spots. «Everything that the model is bad at, the harness should help the model with — provide as much assistance as possible to make it more reliable,» Labonne said. «End users don’t care if it’s the model or the harness. What they want is that the task is achieved at the end of the day.»
The model nevertheless works out of the box with established harnesses including Hermes Agent, OpenClaw, and Pi, served behind any OpenAI-compatible endpoint.
Swap the harness, not the model
For enterprise deployment, Labonne argued the release marks a shift in what small models can be used for. Until now, he said, local models made economic sense mainly as narrowly fine-tuned specialists — trained to do one thing at cloud-model quality, much faster and cheaper. Agentic capability changes that calculus, because the same model can be repurposed by changing the tools around it rather than the model itself.
«You can have a calendar assistant, and you can reuse the same model and make a meeting assistant that will record what everybody said and summarize it — a bit like Granola, for example,» he said. «You don’t change the model; you just change the harness. You just change the tools around it. This gives much more generalizability, and it’s a lot easier to do and a lot cheaper as well.»
He still recommends fine-tuning for production deployments whenever feasible: «If you don’t fine-tune it, you leave some quality on the table. If you fine-tune it well, it’s going to match the performance of GPT and Claude — really, if your task is not the most complex task in the world,» he said, adding that the barrier to entry has collapsed: «The bar to be able to do fine-tuning now is super low. It’s very accessible to everyone.»
How it stacks up against DeepSeek-V4-Flash, Google’s Gemma and Alibaba’s Qwen
Liquid AI released its own benchmark comparison charts pitting LFM2.5-2.6B against the models enterprises are most likely to shortlist for the same edge deployments: Google’s Gemma 4 E2B (5.1B parameters) and E4B (8B), and Alibaba’s Qwen3.5-4B (4.7B) and Qwen3.5-9B (9.7B).
A separate test by local AI client platform Atomic Chat found that LFM2.5-2.6B completed 35 tool calls to complete three tasks (checking weather and local time in six cities, converting one budget into six currencies, checking four hotels and booking for a date) 3.7 times faster than DeepSeek-V4-Flash (a whopping 284B parameters), the model has skyrocketed to the top of OpenRouter since its release last week.
Gemma 4’s small models are multimodal generalists, accepting image and audio input alongside text, and use a Per-Layer Embeddings design that keeps only a fraction of their weights active per token — which is why Google markets them by «effective» size (2.3B and 4.5B) despite total footprints of 5.1B and 8B. Alibaba’s Qwen3.5 small series, released in March, is natively multimodal from 4B up and leans on scaled reinforcement learning to chase frontier-style reasoning — Alibaba touts the 9B model as matching or beating OpenAI’s far larger gpt-oss-120B on reasoning benchmarks.
LFM2.5-2.6B takes a narrower path: it is text-only, dense, and specialized for agentic work, with Liquid AI shipping separate vision and audio variants of the LFM family rather than folding everything into one checkpoint.
Where Qwen’s post-training reinforcement learning targets reasoning, Liquid’s targets tool use inside real agent harnesses.
The result, per the company’s published numbers, is that the smallest model in the comparison leads every instruction-following benchmark (IFBench, Multi-IF, IFStruct) and nearly every tool-use benchmark — 77.83 on ToolSandbox versus 76.44 for Qwen3.5-9B, a model nearly four times its size — trailing only that 9B model on BFCLv4.
On agentic evaluations it beats both Gemma models across the board and essentially ties the Qwens: 26.89 on BrowseComp+ versus 27.23 for Qwen3.5-9B. It also posts the best score on AA Omniscience, a knowledge benchmark that penalizes hallucination.
The Qwen models keep the edge where their training focus lies: math (Qwen3.5-9B leads AIME25) and coding, where larger models retain an advantage on LiveCodeBench — though Labonne noted the gap is smaller than the parameter counts would suggest.
«With LiveCodeBench v6, we might not be the best among these models, but we’re also by far the smallest. Showing that we’re competitive with them is already quite a big win for me,» he said.
One differentiator cuts the other way: licensing. Gemma 4 and Qwen3.5 ship under the permissive Apache 2.0 license — a change Google made specifically to court enterprises. DeepSeek-V4-Flash ships under a similarly permissive MIT License.
Meanwhile, Liquid AI’s revenue-gated license (detailed below) asks larger companies to strike a commercial deal. Enterprises above the threshold are effectively trading license friction for footprint and tool-use performance.
Licensing reflects a commercial middle ground
LFM2.5-2.6B is distributed under the LFM Open License v1.0, which permits use, modification, and redistribution — including commercial use — for organizations with less than $10 million in annual revenue. Commercial use by larger companies is not covered by the license, requiring a separate arrangement with Liquid AI; qualified nonprofits are exempt from the threshold for non-commercial and research purposes.
Labonne framed the structure as a way to sustain model development — «the models are really the moats, so we need to be sensible in the way that we license them; otherwise, we cannot make money, so we can’t make more models» — while characterizing the threshold as a light-touch mechanism in practice.
Asked how the company would even know if a large enterprise quietly deployed the open weights, he was candid: «I think this is a question for our legal team, but personally, I don’t know. And even if you’re above $10 million, the only thing that we ask you is to contact us.»
The company pairs its licensed model releases with freely published research, he added, including new structured-output evaluations and a training technique that mitigates the repetition loops common in small models — a failure mode he noted Qwen models are «kind of guilty of.»
Small model, big enterprise implications
The launch coincided with an announcement from MacPaw, the Ukrainian software company behind CleanMyMac and Setapp, of a long-term strategic partnership with Liquid AI to build an on-device AI stack for the Mac.
Liquid AI will design and fine-tune foundation models for Eney, MacPaw’s macOS assistant, running locally on Apple silicon through MacPaw’s Elix inference engine and Mnemos memory layer, with results expected later this year.
Labonne pointed to the deal as a concrete validation of the size argument: «One of the reasons why they chose us is also because the model is quite small, and they don’t have all the memory budget to run the other models.»
The release arrives as hardware vendors, operating system developers, and enterprise software companies increasingly invest in local AI execution — and as agent harnesses proliferate across the industry. Liquid AI’s bet is that deployment economics, not raw scale, will define an important segment of that market: agents running continuously, everywhere, at zero marginal token cost.
Whether small, highly optimized agent models become a significant segment of enterprise AI will ultimately depend less on benchmark scores than on operational reliability. But Liquid AI’s latest release suggests the next competitive frontier is no longer simply building larger models — it’s building models small enough, and capable enough, to run wherever enterprise workflows already live.
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