Cactus – Ollama for Smartphones
Hey HN, Henry and Roman here - we've been building a cross-platform framework for deploying LLMs, VLMs, Embedding Models and TTS models locally on smartphones. Ollama enables deploying LLMs models locally on laptops and edge severs, Cactus enables deploying on phones. Deploying directly on phones facilitates building AI apps and agents capable of phone use without breaking privacy, supports real-time inference with no latency, we have seen personalised RAG pipelines for users and more. Apple and Google actively went into local AI models recently with the launch of Apple Foundation Frameworks…
In plain words
Cactus is a cross-platform framework that lets developers run large language models, vision models, embedding models, and text-to-speech directly on smartphones. It works with Flutter, React Native, and Kotlin, supporting any GGUF model. Running AI locally on phones enables privacy-preserving applications with no latency and faster inference. It fills a gap left by platform-specific solutions like Apple Foundation Frameworks and Google AI Edge, offering developers flexibility to build personalized AI features without relying on cloud services.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN, Henry and Roman here - we've been building a cross-platform framework for deploying LLMs, VLMs, Embedding Models and TTS models locally on smartphones. Ollama enables deploying LLMs models locally on laptops and edge severs, Cactus enables deploying on phones. Deploying directly on phones facilitates building AI apps and agents capable of phone use without breaking privacy, supports real-time inference with no latency, we have seen personalised RAG pipelines for users and more. Apple and Google actively went into local AI models recently with the launch of Apple Foundation Frameworks and Google AI Edge respectively. However, both are platform-specific and only support specific models from the company. To this end, Cactus: - Is available in Flutter, React-Native & Kotlin Multi-platform for cross-platform developers, since most apps are built with these today. - Supports any GGUF model you can find on Huggingface; Qwen, Gemma, Llama, DeepSeek, Phi, Mistral, SmolLM, SmolVLM, InternVLM, Jan Nano etc. - Accommodates from FP32 to as low as 2-bit quantized models, for better efficiency and less device strain. - Have MCP tool-calls to make them performant, truly helpful (set reminder, gallery search, reply messages) and more. - Fallback to big cloud models for complex, constrained or large-context tasks, ensuring robustness and high availability. It's completely open source. Would love to have more people try it out and tell us how to make it great! Repo: https://github.com/cactus-compute/cactus
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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