Can a 2B Model Really Do EVERYTHING?
LFM3-Omni-2B runs speech recognition, vision, and text-to-speech in one 2.2B model. See, hear, and answer on local hardware, beating bigger multimodal AI models on video with sound. Liquid AI released LFM3-Omni-2B in October as a 2.2-billion-parameter open-weight model that combines speech recognition, computer vision, language understanding, and text-to-speech in a single local LLM. On Liquid's own video-with-sound benchmarks, it scores around 70, ahead of Qwen's 10.7B omni model at about 61. The model processes video at two frames per second, timestamped with aligned audio, so it hears and sees the same moment. It decodes speech roughly 87 times faster than real time on an M4 Max CPU thread, and outputs text and voice woven together for natural responses. But the gains are specific: it leads on multimodal video-audio tasks, yet drops roughly 4 accuracy points when you ask questions aloud instead of typing, and scores about 52 on tool calling versus Gemma 4E4B's 72. Running it requires careful app choice, Llama.app on macOS/Windows gets full audio-video support, Ollama gets audio and images only, and LM Studio gets images only. The Omni model download is 2.4GB, and an eight-gigabyte machine is recommended. Licensing is free under ten million dollars annual revenue; beyond that requires a commercial licence. For anyone building a private local assistant that watches and listens together, or testing multimodal AI offline, this covers the real tradeoffs and deployment gotchas. Chapters: 0:00 Intro 0:50 How It Works 2:27 The Scores 4:48 Running It 6:39 The Licence 7:34 Verdict Tools & resources mentioned: - LFM3-Omni-2B: https://huggingface.co/LiquidAI/LFM3-Omni-2B - Llama.app - Ollama: https://ollama.com/library/lfm3-omni:2b - LM Studio - llama.cpp - Qwen Omni - Gemma - SGLang About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #local ai #multimodal models #speech to text
