Cactus

The PowerSync AI Hackathon starts today. Bring your favorite AI ideas to life and compete for over $8k+ in prizes, including bonus prizes from our partners @supabase @neondatabase @mastra @tan_stack @cactuscompute. Let the hacking being!

We now also have @cactuscompute on board as a partner in the hackathon! Cactus' on-device AI with cloud fallback pairs well with PowerSync. They are sponsoring a prize of a month of Cactus Hybrid inference for the best submission using Cactus 🌵🎉 x.com/powersync_/sta…

@sobedominik @sunglassesface Cactus Chat is a great choice :)

@sunglassesface @cactuscompute oh that’s sick! i’m actually looking for a consumer version that I can use myself

Anyone successfully using a local LLM on their iPhone? I have tested a few a year ago on my iP14 Pro but they all made my battery extremely hot and the UI sucked. Now with the iP17 Pro I’d love to give it another try. What app/model should I use?

From generalist to expert! See how @cactuscompute used #Tunix for Supervised Fine-Tuning on the Gemma 3 1B model, boosting its tool-calling capabilities from 28% to 35%. All on the free tier of Google Colab. #AI #SFT #Gemma goo.gle/gemma3-tunix-t…

@cactuscompute @nothing @huggingface Excited to share that our team placed 2nd at the Cactus (YC S25) x Nothing x Hugging Face Mobile AI Hackathon. We were up against teams from MIT, Stanford, and builders from around the world. Grateful to have had the chance to build and compete alongside so many talented people.

Spent 24 hours at Nothing’s new London HQ as part of the @cactuscompute × @huggingface × @nothing hackathon. My team and I built Pulse; a first-aid assistant to guide bystanders through basic life support whilst reporting to first responders providing situational context. Against ~700 international participants, we were awarded the honorary prize!!!

1.6B INT8 VLM by @liquidai on Cactus (YC S25) never exceeds 231MB of peak memory usage at any context size. 1. Cactus is aggressively optimised to run on budget devices with minimal resources, enabling efficiency, negligible pressure on your phone and passes your OS safety mechanisms. 2. Notice how 1.6B INT8 CPU reaches 95 toks/sec on Apple M4 Pro, faster than your eyes could process. Our INT4 will almost 2x the speed when merged. Expect up to 180 toks/sec decode speed. 3. The prefill speed reaches 513 toks/sec. Our NPU kernels will 5-11x that once merged. Expect up to 2500 - 5500 toks/sec. The time to first token of your large context prompt will take less than 1sec. 4. LFM2-1.2B-INT8 in the Cactus compressed format takes only 722mb. This means that with INT4 will shrink to 350mb. Almost half as much as GGUF, ONNX, Executorch, LiteRT etc. 5. Once done, we will start recommending 1B models to our users, cause your Grandma’s phones will run them. Stay tuned! github.com/cactus-compute…

We just shipped the Cactus React Native SDK🌵- the fastest and most efficient on-device AI inference engine for React Native.⚡️Lightweight, insanely fast, and built for mobile devices from the ground up.🚀

@SelimBenayat @tigran3rd @nothing @huggingface @tigran3rd not in SF. Our community at Stanford is away for Thanksgiving. SF will be online-only

@tigran3rd @nothing @cactuscompute @huggingface @cactuscompute did we lock in on a location on campus?

Hackathon alert! London, SF, Boston. This Friday! 👀 @nothing is teaming up with @cactuscompute and @huggingface to hack on redefining on-device AI experiences! Come build something memorable, meet the teams, and ship in 24 hours! Signups are wild so far 🔥

@_iamEtornam thanks for building with us, Etornam! 🫶🏼🌵

Let’s talk about on-device model… #DevfestPretoria

Cactus React Native v1 is live! Deploy AI on-device with text inference, tool calling, embeddings and more – powered by the fastest edge inference engine 🌵 Our React Native bindings run on @margelo_com's Nitro Modules, yielding the fastest mobile inference we've seen so far.