Run fast, private agents on-device with LFM2.5-24B-A2B
Back to Articles Run fast, private agents on-device with LFM2.5-24B-A2B Team Article Published October 13, 2026 Upvote 37 +31 Raahul Vignesh Raahul26 Follow LiquidAI Song Duong sduong Follow LiquidAI Akhilesh Deepak Gotmare akhileshdeepakgotmare Follow LiquidAI Fernando Fernandes Neto…
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Back to Articles Run fast, private agents on-device with LFM2.5-24B-A2B Team Article Published October 13, 2026 Upvote 37 +31 Raahul Vignesh Raahul26 Follow LiquidAI Song Duong sduong Follow LiquidAI Akhilesh Deepak Gotmare akhileshdeepakgotmare Follow LiquidAI Fernando Fernandes Neto fernandofernandes Follow LiquidAI Jiahui Wang WWWjiahui Follow LiquidAI Mayank Agarwal mayankagarwal Follow LiquidAI nathan ranchin nathanrchn Follow LiquidAI Sergei Tilga tilgasergey Follow LiquidAI Sinoué GAD GAD-cell Follow LiquidAI Tim Seyde tseyde Follow LiquidAI Zetian Li zetianli Follow LiquidAI changyi yang changyiyang Follow LiquidAI Simon Lee yungisimon Follow LiquidAI Leonie Monigatti iamleonie Follow LiquidAI Maxime Labonne mlabonne Follow LiquidAI Karthik Macherla karthikrmacherla Follow LiquidAI Alfred Eng alfredeng Follow LiquidAI Khoi Nguyen KTLK Follow LiquidAI Paul Pak paulpak58 Follow LiquidAI xx tugot17 Follow LiquidAI Tarek Dakhran tdakhran Follow LiquidAI Anthony Maltsev amaltsev Follow LiquidAI Aldo Gael Carranza agcarranza Follow LiquidAI Tyler Murray undfined Follow LiquidAI Aditya Tadimeti adityatadimeti Follow LiquidAI Jimmy T.H. Smith jimmysmith1919 Follow LiquidAI Yuri Khrustalev ykhrustalev Follow LiquidAI LFM2.5-24B-A2B is a 24B-parameter reasoning model with just 2B active parameters, built for fast, private agents on-device. It thinks before it answers, calls tools, and works through multi-step tasks inside popular agent harnesses. Because only 2B parameters are active per token, each reasoning token is cheap, so you can run agents continuously on your own hardware. Best-in-class agent: Competitive with Qwen3.6-35B-A3B, a model with ~50% more total and active parameters, on agentic and tool-use tasks, scoring 48.09 on LiveClaw versus 38.19. Reasoning at MoE speed: Always thinks before it answers. With only 2B active parameters per token, each reasoning token is cheap. Efficient inference: 184 tok/s on an Apple M5 Max and up to 21K output tok/s on a single NVIDIA B300 GPU. How we built a reasoning agent with 2B active parameters LFM2.5-24B-A2B builds on LFM2-24B-A2B and keeps its architecture: 40 layers, 64 experts per MoE block with top-4 routing, and the same hybrid of gated short convolutions and GQA. The context window grows from 32K to 131K tokens, and an in-place tokenizer expansion doubles the vocabulary from 65K to 128K tokens for better non-Latin tokenization. Unlike its predecessor, it is a reasoning-only model that produces an explicit chain of thought before its final answer. Pre-training was scaled up from ~17T to ~33T tokens. Mid-training then adds 2T tokens at 32K context, weighted toward reasoning, math, code, instruction following, and multilingual text, and 300B tokens at 131K context, weighted toward long-context data. Tool calls and multi-turn trajectories appear throughout both phases. Post-training turns the base model into an agent in five stages: Supervised fine-tuning (SFT) Teacher specialization Multi-domain on-policy distillation (MOPD), non-agentic: distill the specialist teachers on non-agentic tasks. MOPD, agentic: a second distillation stage focused on agentic tasks. Agentic reinforcement learning (Agentic RL): train the model inside agent harnesses, such as OpenClaw, Hermes Agent, Pi, and OpenCode. Benchmark results We evaluated LFM2.5-24B-A2B against Qwen3.6-35B-A3B, Gemma-4-26B-A4B-it, and Nemotron-3.5-Lightning on 16 benchmarks covering tool use and agentic workflows, knowledge, instruction following, math, and coding. It is the smallest model in the group, yet it is competitive with MoEs with ~50% more total and active parameters. Benchmark LFM2.5-24B-A2B Qwen3.6-35B-A3B Gemma-4-26B-A4B-it Nemotron-3.5-Lightning (30B-A3B) AutomationBench 27.69 8.72 5.46 2.61 BFCLv4 65.01 65.00 57.09 53.78 ToolSandbox 80.71 75.02 72.09 75.13 LiveClaw 48.09 38.19 41.25 37.72 PinchBench 83.18 89.93 77.12 84.20 τ³-Bench Banking 8.04 7.63 4.54 7.01 WebArena Verified 26.75 37.62 25.62 28.69 GPQA Diamond 74.42 83.69 74.90 72.42 AA-Omniscience -24.90 -24.35 -59.39 -3.58 HLE 7.40 7.69 8.57 5.65 IFEval 96.75 93.06 92.00 94.19 IFBench 74.69 64.21 47.44 71.67 Multi-IF 84.38 81.53 82.19 82.34 MATH-500 96.04 96.73 95.66 96.40 SWE-Bench Verified (OpenCode) 51.00 66.20 25.80 53.40 SciCode 32.71 37.35 35.42 27.78 *All the models were evaluated with vLLM, using each benchmark's recommended generation parameters. For your app, the strengths are tool use and instruction following. LFM2.5-24B-A2B leads or matches the competition on five of the eight tool-use and agentic benchmarks (AutomationBench, BFCLv4, ToolSandbox, LiveClaw, and τ³-Bench Banking), and on AutomationBench it scores more than three times the next-best model. It also leads on all three instruction-following benchmarks. Larger models keep an edge on knowledge-heavy benchmarks like GPQA Diamond, HLE, and AA-Omniscience, and on coding benchmarks like SWE-Bench Verified (OpenCode) and SciCode, so pair LFM2.5-24B-A2B with retrieval or web search for knowledge-heavy workloads. These results make LFM2.5-24B-A2B a strong foundation for agents that need to reason, call tools, and run reliably on your device. Inference speed on CPU and GPU LFM2.5-24B-A2B ships with day-one support across the inference ecosystem, including llama.cpp, MLX, vLLM, SGLang, and ONNX. Edge inference. LFM2.5-24B-A2B is the fastest of the compared MoEs, decoding 184 tokens/s on an M5 Max and 114 tokens/s on a Ryzen AI Max+ 395. That is up to 1.7x faster than the next-best model, while using the least memory. GPU inference. LFM2.5-24B-A2B is the fastest model in its size class, reaching over 21K output tokens per second at high concurrency on a single B300. How to use LFM2.5-24B-A2B Reach for LFM2.5-24B-A2B when you need agents that reason, call tools, and run reliably on your device. Install the latest version of transformers (compatible with transformers>=5.0.0): pip install -U transformers Then load and run the model: from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_id = "LiquidAI/LFM2.5-24B-A2B" model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", dtype="bfloat16", # attn_implementation="flash_attention_2"