
Liquid AI has released LFM3, a new generation of Liquid Foundation Models designed for specialized, on-device intelligence. The family includes text, vision, audio, omni, and decision models, all branching from a 1.7B parameter base. Key technical improvements include a tri-hybrid architecture and custom encoders that reduce compute requirements significantly; the vision model uses 3x less compute than previous standards while matching larger models' accuracy. The text model demonstrates superior agentic performance, outperforming Qwen3.5-9B on WebArena benchmarks despite having far fewer parameters.
Read originalEarlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
vLLM Releases · July 13, 2026 · Related
Together AI Blog · July 15, 2026 · Related
Fireship · July 20, 2026 · Background
MIT Technology Review AI · August 10, 2026 · Related
Together AI Blog · August 17, 2026 · Related
Sam Witteveen · August 30, 2026 · Related
Hugging Face Blog · October 7, 2026 · Same story
Hugging Face Blog · October 13, 2026 · Same story
llama.cpp Releases · October 28, 2026 · Related
Liquid AI releases LFM3 multimodal models
3 developments
© Hugging Face BlogAn NVIDIA engineer built a fully local voice agent for a Halloween attraction using the new Nemotron Voice Agent framework. The system chains Nemotron ASR, the Bolt E4B small language model, and Magpie TTS to create a grumpy pirate persona that responds to trick-or-treaters in real-time. Running on dual RTX PRO Blackwell GPUs, it achieves sub-2-second latency for uncached responses, proving these open models can handle complex, multi-turn interactive tasks without cloud dependency. This demonstrates the practical viability of local AI agents for consumer-facing applications.
© Hugging Face BlogIBM’s Granite Switch 4.1.1 proves modular architectures can move with the speed of standard software updates. By keeping the base model untouched and only updating specialized adapters like the multilingual ASR and decision logic, they sidestep the massive compute cost of full retraining. This approach lets developers compose new expertise on a single endpoint without migration headaches. It signals a shift toward composable AI where models evolve incrementally rather than in disruptive leaps.
Hugging Face and a coalition of major universities are launching Project ProtoStar to solve the 'last mile' problem of scientific data: making it machine-actionable. With $190 billion in annual U.S. federal R&D funding, the initiative pushes institutions like CMU and Purdue to release datasets in standardized formats rather than static files. The kickoff challenge tests AI's ability to predict urban climate conditions using historical weather records from 210 cities. This shifts the focus from mere publication to actual interoperability, creating a reusable infrastructure for AI-driven discovery.
This release quietly cements llama.cpp as the universal inference runtime by finally supporting NVIDIA's latest CUDA 13 stack alongside AMD's ROCm 10.0. The inclusion of native builds for Linux arm64 with Snapdragon chips, covering CPU, Adreno GPU, and Hexagon NPU, signals a serious push into mobile edge deployment. While Apple Silicon KleidiAI is disabled in this specific build, the broader hardware coverage means developers no longer need to compile from source for most modern accelerators. It’s less about new features and more about removing friction for anyone running local models on bleeding-edge or diverse hardware.
© The Verge AIApple is finally unlocking its AI suite for Chinese users with the iOS 27.3 beta, marking a significant regulatory milestone after months of approval delays. The rollout relies on Alibaba’s Qwen model rather than OpenAI, integrating features like notification summaries and visual intelligence directly into the OS. By sidestepping the new Siri in favor of established text and image tools, Apple is prioritizing immediate utility over the full generative experience. This move effectively brings Apple’s AI capabilities to a massive new market, albeit with localized constraints that distinguish it from the global release.
© Matt WolfeGoogle launched Gemini 4 Carbon, a new model variant aimed at providing high performance at a lower cost for inference tasks.