Run fast, private agents on-device with LFM2.5-24B-A2B

Liquid AI introduced LFM2.5-24B-A2B, an open-weight mixture-of-experts reasoning model designed for private, fast on-device agents, with 24 billion total parameters but only 2 billion active per token. Built on LFM2-24B-A2B, it expands context capacity to 131,000 tokens, doubles its vocabulary to improve non-Latin language support, and uses explicit reasoning, tool calls, and multi-step agent training across supervised fine-tuning, distillation, and reinforcement learning. In evaluations against similarly sized models, it performed particularly strongly in tool use, agentic workflows, and instruction following, including a 48.09 score on LiveClaw and a leading AutomationBench result, while larger models retained advantages in knowledge-intensive and some coding benchmarks. Liquid AI reports inference speeds of 184 tokens per second on an Apple M5 Max, 114 on a Ryzen AI Max+ 395, and more than 21,000 output tokens per second at high concurrency on an NVIDIA B300 GPU. The model supports major inference frameworks including llama.cpp, MLX, vLLM, SGLang, and ONNX, can be integrated with local agent harnesses such as Hermes Agent, OpenClaw, Pi, and OpenCode, and is available alongside its base version on Hugging Face.

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