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Liquid AI LFM2.5: Launch and Edge Testing

Liquid AI launched the open-source edge model LFM2.5. Tests reveal it delivers high-speed local execution, matching DeepSeek's tool-calling performance at a much faster rate.

2026-08-04 ~ 2026-08-11 · 3 episodes · 25 posts

Episode 1 · Liquid AI Releases 2.6B On-Device Agent Model LFM2.5 (2026-08-04, 18 posts)

Liquid AI released LFM2.5-2.6B, a new on-device agent model with only 2.69B parameters, capable of fully local multi-step planning, tool calling, and reasoning on phones, laptops, and robots, ensuring data never leaves the device and near-zero marginal cost per run. Official data shows 30 tok/s on mobile, and benchmarks surpass several larger models, quickly topping Hugging Face trending. Developer @nicodotdev built a fully autonomous research agent in-browser, validating the feasibility of complex agent tasks on-device with small models. This offers a cost-effective solution for mobile and offline AI applications.

Confirmed

  • Specs: 2.69B parameters, 34T training tokens, 128K context. Compatible with llama.cpp and GGUF at launch; MLX version optimized for Apple Silicon, supporting text generation and multilingual (Chinese, English, French, German, Arabic).
  • Core strengths: Dense mixture architecture, fully local multi-step planning and tool calling, zero marginal cost, suitable for simple high-throughput tasks. Official data via @BTALabs: 30 tok/s on mobile.
  • Benchmarks: Surpasses several larger models; topped Hugging Face trending after release.

Why it matters

  • On-device agent potential: Developer @nicodotdev built a fully in-browser research agent that plans, calls tools, checks results, and loops until completion, proving small models can handle complex agent tasks on-device.
  • Lower deployment barrier: Brings powerful tool-calling to edge devices, offering a cost-effective solution for mobile and offline AI.

Episode 2 · Liquid AI 2.6B Model Matches DeepSeek in Tool Calling, 3.7x Faster (2026-08-06, 3 posts)

Liquid AI's 2.6B-parameter LFM2.5 model achieves 90 tokens/s on Apple M5 Pro and matches DeepSeek-V4 in tool calling while being 3.7x faster on RTX 5090s, showcasing strong edge potential.

Episode 3 · Liquid AI Releases Open-Source Edge Model LFM 2.5 (2026-08-09, 4 posts)

Liquid AI has released LFM 2.5, a 2.6-billion parameter open-source edge Agent model. Designed with an elegant architecture, it targets local deployment, allowing small devices to run independently without relying on the cloud.