Liquid AI's LFM2.5-2.6B Beats 9B Models Running Entirely on Your Phone
Liquid AI's LFM2.5-2.6B runs full agentic workflows on-device, beating models nearly 4x its size on tool use and instruction following benchmarks.
- New release: LFM2.5-2.6B is a 2.6B parameter agentic model designed to run fully on-device with no cloud dependency.
- Benchmark performance: Beats Qwen3.5-9B on ToolSandbox (77.83 vs 76.44) and all instruction-following benchmarks despite being 4x smaller.
- Speed: 220 tok/s on Apple M5 Max, 113 tok/s on AMD Ryzen CPU, 30 tok/s on a phone, all under 2.5 GB memory.
- Architecture: Hybrid LFM2 design with 22 short convolution blocks + 8 attention layers, trained on 34T tokens with 128K context.
- Training: Four-stage post-training pipeline including agentic RL inside real harnesses (Hermes Agent, OpenClaw, Pi) using GRPO.
- Limitations: Not recommended for coding-heavy or knowledge-intensive agentic tasks; larger models still hold an edge there.
The premise of on-device AI agents has always been compelling but frustratingly out of reach. Models small enough to run locally tend to fall apart the moment you ask them to plan across multiple steps, call tools reliably, or follow complex instructions. Liquid AI is pushing directly against that wall with LFM2.5-2.6B, a 2.6B parameter model purpose-built for agentic workloads that runs entirely on-device, no cloud calls required.
Zero marginal cost agents
Agents that depend on cloud APIs carry per-token costs, latency, and privacy exposure. Running inference locally eliminates all three. More importantly, removing the per-token cost changes how developers can architect systems: agents can be massively parallelized on local hardware, burning through millions of tokens in the background at no marginal cost. When token spend stops being a constraint, you can run agents continuously, on the user's own hardware, across any number of parallel tasks.
LFM2.5-2.6B is small enough to run on a phone, fast enough to stay responsive on a CPU, and capable enough to handle planning, tool calls, and multi-step workflows.
A hybrid architecture built for constrained hardware
LFM2.5-2.6B is built on the LFM2 hybrid architecture, which is the primary reason it performs well on constrained hardware. LFM2 pairs efficient gated short convolution blocks with a small number of grouped query attention (GQA) blocks. Attention accounts for only about 37% of the model; the remaining 63% uses cheaper convolution blocks.
In a standard Transformer, every layer uses Softmax Attention, which scales quadratically with sequence length and produces large KV caches that consume VRAM. The LFM2 convolution blocks sidestep this entirely. Each convolution layer maintains a fixed-size state that updates on each new token. There is no attention over past tokens and no KV cache: state memory at token 10,000 is identical to state memory at token 10.
The specific block type is called an LIV (Linear Input-Varying) convolution, a linear-time operator whose behavior changes based on the input. Liquid AI ran hardware-in-the-loop architecture search to find the best layer mix across quality, latency, and memory. The search space included GQA, short convolutions, linear attention, S4, Mamba, and Mamba2. The winning configuration was the simplest: mostly short convolutions, with a handful of attention layers to handle long-range retrieval. LFM2.5-2.6B has 30 layers total: 22 double-gated short convolution blocks and 8 GQA blocks.