Posted by HenryNdubuaku 17 hours ago
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 300-700 on sub-$200 phones such as the Samsung A-Series.
On the tool call and mobile device use benchmarks, Needle 2 trades wins with closest small models like LFM2.5 230M and Apple Foundation Model, at 5x to 70x smaller, both at f16 vs Needle 2 at 2bit. Needle is based on Simple Attention Networks from our paper (https://arxiv.org/abs/2607.18363).
Edge AI has lately meant Macs and PCs, but that is just 1.5 billion of over 21 billion connected IoT devices in the world today, and in emerging markets most phones ship under $200, no NPU, cheap GPUs. These include budget phones, Raspberry Pis, microcontrollers, wearables, small robots like Reachy Mini, and connected home devices.
A conventional transformer of Needle's width and depth spends 164 MFLOPs per token, and even one squeezed down to Needle's parameter count spends 87, Needle spends 70. Even on a high-end phone, an always-on assistant lives inside a power budget; every MFLOP is milliwatt-hours, and Needle spends 7x to 85x fewer of them per token than the smallest performant LLMs. More about the architecture in the link.
When we structure intelligence for consumer devices as functions with typed parameters, the only hard part is mapping a messy sentence onto them; which function, with which values. Our research found that when framed that way, the problem needs no world knowledge and no open-ended prose, which is why 45M parameters suffice.
Needle 2 expands to structured extraction where the schema can be passed in-place of tools and the model returns structured output. You can use Needle as a text-classification model with an enum field, as a summarization model by providing a schema that extracts key fields, everything but free-range decode.
Every product has its own tool vocabulary and fine-tuning needle helps it achieve frontier-level performance on custom tasks, so using the python package (https://github.com/cactus-compute/needle), Needle can be fine-tuned Needle on a Mac/PC in minutes to a few hours, with automated data-generation pipeline, just pass a couple samples.
Nonetheless, every response carries a learned confidence score based our Cactus Hybrid technique. If above your threshold, act, below it, escalate to the cloud or bigger model. Combining Needle 2 with a private DeepSeek-v4-Flash deployment works particularly well for enterprise-level tasks at barely any cost, we can help with this setup.
We have put a lot of thoughts into Needle 2 but might still be missing quite a lot, please use the playground in the provided link to test Needle and share your thoughts, always appreciated!
import needle
@needle.tool
def add(a: int, b: int):
"Add two numbers."
return a + b
agent = needle.Needle(tools=[add])
print(agent.run("calculate 1 + 1?")["reasoning"])
conclusion: completly useless
import needle
@needle.tool
def add(a: int, b: int):
"Calculate the sum of two numbers. Use for any arithmetic or math question."
return a + b
agent = needle.Needle(tools=[add])
print(agent.run("what is 5 + 7?")["reasoning"])
While most of the industry focuses on the frontier of “intelligence” (function), a release like this represents the frontier of the other end of the spectrum (form).
Both are important if we ever want to see “Opus-level” capability running locally on commodity machines in the future.
Re: robotics: I'm unsure how this could be helpful.
It fails a pretty simple navigation prompt.
X0: (0.0, 0.0). Object bounding box: [1.0, 1.0, 2.0, 2.0]. navigate to (3.0,3.0)
I changed it to "call path planner to navigate: a_star(x0, xf, obs)"
Another fail.
My intuition tells me micro llms will/are important for robotics. I just can't grok it. Can someone without control theory experience give me a good example?
Probably at the planning level of the navigation stack. That's where I see reasoning being helpful. Lower than that...idk
Give me an example of a robotics prompt that seems useful and I'll give you an example why we don't need LLMs to be a tracking controller, etc.
But yeah, in terms of “physical” AI, robotics definitely comes to mind for me as well, where tool calls/structured “device” use in a “realtime”/edge application are highly beneficial (if you wanted to go with LLMs), but beefy hardware can’t be easily used.
yes you're right, there's only so much a 14MB model can do.
Needle excels at in-conext inference, with tightly defined environments. In our experience:
accurate descriptions + narrow tool scope = success
Edit: I have a pile of d1 minis, but not much time.
Please, though, take a pass at humanizing the text on the page. It's Clauded up all over and makes it hard to read.
{ "function_calls": [ { "name": "lock_door", "arguments": { "door": "tv" } } ], "confidence": 0.0158 }
Very interesting, seems confidence is 0 when tool calls are right?