AI Tool

Needle 2 Review

Needle 2 is an open 45-million-parameter, 14MB agentic LLM designed for tool calling, device use, and structured extraction on tiny, resource-constrained devices.

shipped Aug 18, 2026freemium

Domain rating51Monthly visits269/mo

Why it matters

1Open 45-million-parameter model, 14MB binary size.

2Achieves 500 tokens/second on Raspberry Pi 5.

3MIT-licensed, with code on GitHub and weights on Hugging Face.

4Designed for sub-$200 devices, wearables, and microcontrollers.

About Needle 2

Business Model

Hybrid (Subscription + Usage)

Funding

Y Combinator

Target Audience

Developers and companies building AI applications on edge devices

API Docs GitHub

Specs

API Docs

View Documentation →

GitHub

View Repository →

API Available

Yes, public API

overview

What is Needle 2?

Needle 2 is an agentic Large Language Model (LLM) tool developed by Cactus Compute that enables developers and companies building AI applications on edge devices to implement tool calling, device use, and structured extraction. It is an ultra-compact, open-source model specifically engineered for resource-constrained edge devices, officially released on August 10, 2026.

Needle 2 is a 45-million parameter model, distributed as a 14MB binary that operates within approximately 28MB of RAM. Its primary functions include interpreting natural language for tool selection and execution, enabling direct control of hardware like smart home devices and robots, and extracting specific, structured data from text for tasks such as currency API document field extraction or sentiment classification. The model prioritizes privacy, low latency, and offline reliability, making it suitable for applications like voice-to-action on smart rings (e.g., Pebble Index Ring) and AI on sub-$200 phones, wearables, and microcontrollers.

features

Key Features of Needle 2

Needle 2 incorporates several distinct features designed for efficient, on-device AI deployment, leveraging its compact architecture and specialized capabilities.

Open 45-million-parameter model, MIT-licensed.
Extreme Compactness: 14MB binary, 28MB RAM usage for a full session.
High Efficiency and Speed: Achieves 500 tokens/second on Raspberry Pi 5, 400-1,500 tokens/second on VR devices (Meta Quest 3S, Apple Vision Pro), and 300-700 tokens/second on sub-$200 phones.
On-Device and Offline Operation: Ensures privacy, minimal latency, and reliable function without internet connectivity.
Custom Architecture (Simple Attention Network): Utilizes a novel architecture with a Hadamard MLP, GQA attention, engram key-value memory, and multi-lane hyper-connections.
Self-Contained Binary: Model weights are baked directly into the 14MB engine binary, eliminating separate model files.
Cloud Fallback Functionality: Supports hybrid deployments where cloud assistance can be requested.
State-of-the-art Quantization: Compressed to CQ2-bit using Cactus Quants for optimal performance on constrained hardware.

use cases

Who Should Use Needle 2?

Needle 2 is specifically engineered for developers and organizations focused on deploying AI capabilities directly onto resource-constrained hardware, prioritizing efficiency, privacy, and offline functionality.

Teams shipping firmware or apps on constrained hardware: For integrating AI into devices with limited processing power and memory.
Seed-stage wearable and IoT startups: To enable AI features like voice-to-action on smartwatches, rings, and other connected devices.
Mid-market consumer-electronics OEMs: For embedding AI into products such as smart home assistants and appliances for offline control.
Robotics teams: To sequence robot actions and enable on-device intelligence without relying on cloud connectivity.
Large device makers needing an offline fallback: To ensure AI functionality even when internet access is unavailable.
Engineers targeting MCUs (e.g., ESP32) and developers working on edge deployment: For bringing advanced AI capabilities to microcontrollers and other low-cost hardware.

how to use

How to Use Needle 2

Needle 2 is an open-source model available for integration into custom applications and firmware. Developers can access its code and weights for deployment on their target hardware.

1Access the MIT-licensed code on the Cactus Compute GitHub repository (https://github.com/cactus-compute/cactus).
2Download the model weights from Hugging Face.
3Integrate the 14MB binary into your device's firmware or application.
4Utilize the provided API documentation (https://docs.cactuscompute.com/) for tool calling, structured extraction, and device control implementations.
5Deploy the compiled application onto target edge devices such as Raspberry Pis, ESP32 microcontrollers, or sub-$200 smartphones.

pricing

Needle 2 Pricing & Plans

Needle 2, developed by Cactus Compute, is an open-source model released under an MIT license. Its code is publicly available on GitHub, and its weights can be downloaded from Hugging Face. This means there are no direct licensing fees or subscription costs from Cactus Compute for using the Needle 2 model itself. Any associated costs would pertain to the hardware required for deployment, development efforts, or potential specialized support services if offered by Cactus Compute. This open-source model should not be confused with other products also named 'Needle' or 'Needle 2.0' that offer workflow automation or marketing solutions with tiered pricing plans.

Open Source: MIT-licensed, no direct cost for model usage.

Pros

+Ultra-compact 14MB binary, 45-million-parameter model.
+Designed for efficient on-device and offline operation, ensuring privacy and low latency.
+High inference speeds on resource-constrained hardware (e.g., 500 tokens/second on Raspberry Pi 5).
+Open-source (MIT-licensed) with code on GitHub and weights on Hugging Face.
+Enables AI capabilities on inexpensive devices (under $200) lacking GPUs or NPUs.
+Specialized for tool calling, device control, and structured data extraction.

Cons

−May struggle with general intelligence or complex reasoning for arbitrary tool calling compared to larger models.
−Requires specific fine-tuning for optimal performance on highly specialized tasks.
−Initial web demo reception indicated it might not be impressive for general-purpose tasks.
−Deployment requires technical expertise in edge computing and firmware integration.