Needle2 is a very small LLM — just 14MB — built specifically to run on edge devices. Its selling point is running agentically on phones, wearables, smart home devices, and even robots — without depending on the cloud. It suits developers who want lightweight, power-efficient on-device AI, rather than something meant to compete on raw performance with large models.
This post comes from Show HN, so it’s a freshly launched project — there’s no benchmark data or detailed spec yet to compare it clearly. We’ll need to wait for real-world results from the community to see how it stacks up against other edge models.
If you’re working on IoT, wearables, or robotics projects and want a language model that doesn’t eat up storage/RAM, this is one to keep an eye on — though it’s not a fully decided choice just yet.
What it looks like in practice
Picture this model packed into a smartwatch or a smart speaker that doesn’t even have a cooling fan. That’s what sets it apart from typical LLMs, which usually rely on powerful GPUs or constant round-trips to the cloud.
At 14MB, it can fit into devices with extremely limited storage and RAM — wearables, smart home hubs, even small robotics boards — unlike the edge models we’re used to, which are typically measured in gigabytes.
It remains to be seen how latency and accuracy hold up when actually running on these devices, since a size this small inevitably comes with some trade-off.
When waiting for AI to think takes too long on small devices
Picture a smartwatch that has to send your voice up to the cloud before it can respond — one network hiccup, and a simple command like “turn on the bedroom light” hangs for several seconds. This is a problem IoT developers run into often. For smart home hubs or small robots that need to respond instantly, constant reliance on the cloud is a real weakness — on latency, on requiring an unbroken internet connection, and on privacy, since voice data or in-home behavior has to leave the device.
This is the gap a 14MB model like Needle2 is trying to fill. If it can run directly on the device, without waiting on a round-trip to a server, responses get faster and the data never has to leave the house at all.
Where Needle2 sits on the on-device AI map
Looking at the current SLM (small language model) landscape, most players are competing on “how smart can it chat within a small footprint.” Needle2 plays a different game — it’s designed for agentic/tool-use, meaning it takes a command and then calls a function or controls a device, rather than chatting casually or answering general questions.
Releasing via Show HN says a lot — this is an open-source/research project surfacing for the community to poke at, not a product with official backing and a launch announcement from a major company.
What’s interesting is how narrow the scope is: phones, wearables, smart home, and robots are all extremely resource-constrained targets. Unlike most small models, which mainly target phones, tackling this range of edge devices puts Needle2 in a fairly specialized corner of the on-device AI world, rather than competing head-on with the larger general-purpose SLMs.
From the original Needle to Needle2 — what changed
The Needle team hasn’t disclosed detailed specs for the previous version. What we can confirm is Needle2’s own number — 14MB, which is tiny compared to general-purpose language models that are usually measured in the hundreds of MB up to GB range. The clearest change is scope: from a mobile-first focus to now covering wearables, smart home, and robots all in one model.
| Factor | Previous Needle | Needle2 |
|---|---|---|
| File size | Not clearly disclosed | 14MB |
| Agentic/Tool-calling | Limited to specific tasks | Full tool-calling support |
| Supported hardware | Mostly phone-focused | phone, wearable, smart home, robot |
| Speed/latency | No comparative figures disclosed | No comparative figures disclosed |
There are no latency numbers or direct benchmarks disclosed by the team yet — real data is needed before making a call on performance.
What real use looks like across different devices
Let’s map out some real-world use cases: Smart home — commands like “turn off the bedroom light” work even if the internet drops, because the model runs on-device with no outbound request needed.
Wearables like watches or glasses, where storage is extremely limited, benefit from the 14MB size letting an agentic model fit without eating up more space than necessary.
Small robots that need to make their own agentic decisions (multi-step planning, not just answering questions) without constantly relying on the cloud — suited for tasks where the network is unstable or latency needs to stay low.
Older phones with limited RAM/storage also have a shot at running it, since the model is so small — unlike typical LLMs that demand heavy resources.
All of this is “possible according to spec” — there are no real benchmark numbers yet to confirm performance on each device.
Compared to other options in the SLM market
| Factor | Needle2 | Gemini Nano | Phi-3-mini |
|---|---|---|---|
| File size | 14MB | Much larger (no clear figure disclosed) | Much larger (GB range) |
| Agentic capability | Purpose-built for agentic use | More general-purpose focused | More general-purpose focused |
| Deploy on edge devices | Easy, thanks to the tiny file size | Mostly tied to the Android ecosystem | Requires more resources |
| License/availability | Open for testing via Show HN | Closed, tied to Google | Open (Microsoft) |
The clearest difference is file size — 14MB is genuinely several times smaller than its competitors in the SLM market. But that small size comes with open questions about capability, since there’s no direct benchmark comparison yet. We’ll need real data from people who’ve actually tested it before concluding whether “small” is still “enough.”
Pros and cons that stand out from trying it / reading the spec
Pros
- +14MB file size is tiny, fitting comfortably onto phones/wearables/IoT devices without eating up storage
- +Runs directly on-device, no constant internet connection needed — good fit for smart homes or robots with unstable signal
- +Data stays on-device instead of being sent to the cloud, a clear privacy advantage
- +Works across multiple platforms, from phones to robots, without writing separate implementations for each device
Cons
- −A small model means trading off intelligence/context length — it can't be compared directly to large models
- −Tool-calling accuracy at this model size has no confirmed benchmark yet
- −The project just launched (Show HN), so documentation and community support are still thin
The point is: a size this small inevitably comes with capability trade-offs — there just aren’t real performance numbers yet to show exactly how much. We’ll need people actually using it in the field before making a final call.
The real cost of actually putting this to work
14MB is the model file size — not the total cost of putting it into production.
Dev teams will need to write their own tools/skills for the model to call — there’s no ready-made ecosystem for plug-and-play the way large models have, with communities that have already built out a lot of integrations.
If you want to use it to control a robot or smart home for real, it needs to go through safety and accuracy testing first, because errors from a model this size could mean a device actually executes the wrong command in the physical world — not just giving a wrong answer in a chat.
Fine-tuning to fit each hardware brand is also labor that needs to be factored in. Since the project just launched and documentation is still thin, teams should expect a fair amount of trial and error early on.
Who it’s for, who it’s not for
Made for
- IoT/robotics developers who want to run an LLM on real devices without depending on the cloud
- Teams building offline-first agentic workflows that need low latency
- Smart home/wearable projects with limited resources who want to experiment with a small model
Think twice
- Teams with some time to fine-tune for specific hardware brands should budget enough time for it
Skip this one
- Work that needs GPT-4-level cloud intelligence — use a large model instead
- Production projects not yet ready to take on the risk of a new open source project with thin documentation
What’s next for tiny AI on the devices around us
Needle2 is a signal that AI is increasingly shifting from “thinking in the cloud” to “thinking in our hands.” Watches, speakers, even robots at home may no longer need an internet connection to understand simple commands.
What’s worth watching is that once a 14MB model actually works on everyday devices, developers will start designing products that are “smart without being online” as the default — not just a fallback for when the connection drops.
This direction still has a lot to prove — documentation needs to get more solid, and benchmarks need to be tested against more real-world use cases. But for anyone curious how far edge AI has come, cloning the repo and testing it on your own device will paint a much clearer picture than reading the spec alone.