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…
Cactus – Ollama for Smartphones launched on July 10, 2025 with 231 votes, #143 of 9,224 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 1 year on.
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
Quantization, kernels, runtime and inference engine for mobiles, wearables, smart home and robots. - GitHub - cactus-compute/cactus: Quantization, kernels, runtime and inference engine for mobiles, wearables, smart home and robots.
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…
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1CodeJan 2026 · github.com · ▲569Just to clarify the background a bit. This project wasn’t planned as a big standalone release at first. On January 16, Ollama added support for an Anthropic-compatible API, and I was curious how far this could be pushed in practice. I decided to try plugging local Ollama models directly into a Claude Code-style workflow and see if it would actually work end to end. Here is the release note from Ollama that made this possible: https://ollama.com/blog/claude Technically, what I do is pretty straightforward: - Detect which local models are available in Ollama. - When…


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Cactus Hybrid: We taught Gemma 4 to know when it's wrongJul 2026 · github.com · ▲191Hey HN, Henry & Roman here from Cactus. A small, on-device model is fast and private, but sometimes wrong, but frontier models are getting expensive pretty fast. So, we post-trained Gemma 4 E2B post-trained to know when it's wrong. Every response comes with a confidence score between 0 and 1. Developers can accept the on-device when it's high, hand off to a bigger cloud model when it's low. By routing only 15-35% of queries to Gemini 3.1 Flash-Lite, Gemma-4-E2B matches Gemini 3.1 Flash-Lite on most benchmarks. - ChartQA: 15-20% - LibriSpeech: 25-30% - MMBench, GigaSpeech, MMAU: 30-35% -…
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