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cactus-compute
cactus
Python2025–2026
cactus-compute / cactus

cactus

Sub-50mb agentic, voice and vision intelligence for consumer devices.

AI & Machine Learning
Explore on GitHubRead Hacker News discussions
8Distinct people
12Mentions
1Explicit recommenders
14.7KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Sub-50mb agentic, voice and vision intelligence for consumer devices.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

Amazing project, I love it. How about using these Cactus models? Would it make sense for you to collab with those guys (1) for you to design a cheap but improved,…

gbxk used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:49:33 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month12 dated mentions

Observed mentions: 2025-07-06 17:12:17 UTC to 2026-08-16 22:16:21 UTC.

0123452025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 5 mentions; 1 submissions, 4 commentsJul 20252025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJan 20262026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-01 00:00:00 UTC to 2026-08-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-08-01 00:00:00 UTC to 2026-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsAug 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC514
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC312
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC000
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC101
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC000
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC000
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC110
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC000
Jul 20262026-07-01 00:00:00 UTC2026-08-01 00:00:00 UTC110
Aug 20262026-08-01 00:00:00 UTC2026-09-01 00:00:00 UTC101

What Hacker News says

3 sources
observationist·Sep 18, 2025
Open source for the PR, then switching to non-open licensing is a cowardly, bullshit move. https://github.com/cactus-compute/cactus/commit/b1b5650d1132... Use open source and stick with it, or don't touch it at all, and tell any VC shitheels saying otherwise to pound sand. If your business is so fragile or unoriginal that it can't survive being open source, then it will fail anyway. If you make it open source, embrace the ethos and build community, then your product or service will be stronger for it. If the big players clone your work, you get instant underdog credibility and notoriety.
In Launch HN: Cactus (YC S25) – AI inference on smartphones
HenryNdubuaku·Sep 18, 2025Submission

Launch HN: Cactus (YC S25) – AI inference on smartphones

Hey HN, Henry & Roman here, we are building Cactus (https://cactuscompute.com/), an AI inference engine specifically designed for phones. We're seeing a major push towards on-device AI, and for good reason: on-device AI decreases latency from >1sec to <100ms, guarantees privacy by default, works offline, and doesn't rack up a massive API bill at scale. Also, tools and agentic designs make small models really good beyond benchmarks. This has been corroborated by other papers like https://arxiv.org/abs/2506.02153, and we see model companies like DeepMind aggressively going into smaller models with Gemma3 270m and 308m. We found Qwen3 600m to be great at tool calls for instance. Some frameworks already try to solve this but in my previous job, they struggled in production compared to research and playgrounds: - They optimise for modern devic
Read the rest
es but 70% of phones today are low-mid budget. - Bloated app bundle sizes and battery drain are serious concerns for users. - Phone GPU battery drain is unacceptable, NPUs are preferred, but few phones have those for now. - Some are platform-specific, requiring different models and workflows for different operating systems. At Cactus, we’ve written kernels and inference engine for running AI locally on any phone, from the ground-up. Cactus is designed for mobile devices and their constraints. Every design choice like energy efficiency, accelerator support, quantization levels, supported models, weight format, and context management were determined by this. We also provide minimalist SDKs for app developers to build agentic workflows in 2-5 lines of code. We made a Show HN post when we started the project to get the community's thoughts (https://news.ycombinator.com/item?id=44524544). Based on your feedback, we built Cactus bottom-up to solve those problems, and are launching the Cactus Kernels, Cactus Graph and Cactus Engine, all designed for phones and tiny devices. CPU benchmarks for Qwen3-600m-INT8 : - 16-20 toks/sec on Pixel 6a / Galaxy S21 / iPhone 11 Pro - 50-70 toks/sec on Pixel 9 / Galaxy S25 / iPhone 16. - Time-to-first-token is as low as 50ms depending on prompt size. On NPUs, we see Qwen3-4B-INT4 run at 21 toks/sec. We are open-source (https://github.com/cactus-compute/cactus). Cactus is free for hobbyists and personal projects, with a paid license required for commercial use. We have a demo app on the App Store at https://apps.apple.com/gb/app/cactus-chat/id6744444212 and on Google Play at https://play.google.com/store/apps/details?id=com.rshemetsub.... In addition, there are numerous apps using Cactus in production, including AnythingLLM (https://anythingllm.com/mobile) and KinAI (https://mykin.ai/). Collectively they run over 500k weekly inference tasks in production. While Cactus can be used for all Apple devices including Macbooks due to their design, for computers/AMD/Intel/Nvidia generally, please use HuggingFace, Llama.cpp, Ollama, vLLM, MLX. They're built for those, support x86, and are all great! Thanks again, please share your thoughts, we’re keen to understand your views.
123 points · Open discussion

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