Needle2

Needle2

Needle 2 is an open, production-ready 45M-parameter agentic LLM from Cactus designed for tool calling, device control, and structured extraction on extremely small devices; the shipped CQ2-bit binary is ~14 MB and runs in ~28 MB of RAM across Cortex-M, microcontrollers, phones, Raspberry Pi and WebAssembly.

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Quick Overview

Best for: Creative & Design

What it does

AI Agents software for decision-makers comparing workflow fit and alternatives.

Best fit

Creative & Design

Pricing snapshot

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Needle2

Needle 2 is a purpose-built, agentic language model optimized for on-device tool calling, device control, and structured extraction. The model is 45M parameters, delivered as a single 14 MB CQ2-bit binary that runs with a peak session RAM around 28 MB; it is designed to run on devices ranging from microcontrollers (ESP32-S3 family) to low-end phones, Raspberry Pi-class boards, and in WebAssembly in browsers. The project emphasizes shipping a complete, dependency-free C++ engine which includes the model, tokenizer and a grammar compiler to guarantee well-formed structured outputs.

The product is tuned for extremely small-device budgets and deterministic memory use: Needle trades broad chat/world-knowledge capability for reliable tool execution and structured outputs, uses a learned confidence score and escalation pattern for edge-cloud collaboration, and supports fine-tuning via the provided repo and python package so customers can teach the model their own tool schemas.

An open 45M-parameter model for tool calling, device use, and structured extraction. Needle 2 runs as a 14 MB binary in 28 MB of session RAM.

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Key Features

Tiny deployable binary

A single 14 MB CQ2-bit binary that contains model, tokenizer and grammar compiler and runs with ~28 MB session RAM.

Agentic tool calling & device use

Specialized for mapping natural language to typed function calls and arguments for smartwatches, home assistants, robots and other device APIs.

Structured extraction with grammar compilation

Extraction is implemented as tool calling with a compiled grammar from schemas to prevent malformed JSON and invalid structures, returning enums, arrays and objects as typed fields.

Lossless 2-bit quantization (Cactus Quants)

Model and inference are trained and deployed natively with CQ2-bit quantization so the deployed 2-bit model is the trained model, enabling the small footprint without post-hoc quality collapse.

Cross-architecture C++ engine + WebAssembly

One artifact probes the CPU at startup, selects optimized kernels (SDOT, NEON, AVX2, RISC-V vectors, wasm SIMD, or scalar) and runs from Cortex-M to x86 and in WebAssembly.

Bounded session memory and sliding KV window

Attention uses a 256-token sliding window so RAM is a deterministic ceiling (~28MB), making bounded memory feasible for MCU-class devices.

Edge-cloud collaboration and confidence

Responses include a learned confidence score; off-topic requests return empty calls and the system can escalate to cloud when below a threshold.

Fine-tuneable repo and python package

Repository and python package enable fine-tuning and testing on a developer's Mac/PC in minutes to hours so the model can learn a product's tool vocabulary.

Pricing

Current pricing details are not available from the vendor source.

Use Cases

Smart home device control

Map user utterances to typed function calls for smart home actions (turn on lights, set thermostat) locally on low-cost devices.

Wearable and always-on assistants

Run offline voice or intent recognition on wearables (example: Pebble Index Ring) to execute actions without network dependency.

Robotics and small robots

Control small robots and hardware with deterministic tool calls and low memory/latency footprints.

Structured extraction and document processing

Extract typed fields, lists and structured records from documents using a schema-compiled grammar to guarantee well-formed outputs.

Edge-first mobile applications

Deploy on budget phones and embedded devices to provide private, low-latency assistants in regions where devices cost <$200.

Integrations

WebAssembly

Needle runs in WebAssembly directly in the browser using the single shipped artifact.

C++ static library / engine

Shipped as a dependency-free C++ binary and static library that compiles for Cortex-M, M7, M55, x86 and similar targets.

Python package / repo

Repository and python package used for fine-tuning and testing on developer machines.

Hugging Face & GitHub (explore)

The page links to Hugging Face and GitHub locations for model exploration and source.

Benefits

Extremely small footprint (14 MB binary, ~28 MB RAM) enabling deployment on microcontrollers and low-end phones.
Low latency and high decode speeds on target hardware (benchmarks provided for Raspberry Pi, Meta Quest, Apple Vision Pro and sub-$200 phones).
Offline-first and privacy-preserving operation with local confidence scoring and optional cloud escalation.
Production-ready engineering: single dependency-free binary, cross-architecture support, and a repo + python package for customization.
Energy-efficient inference through architecture, 2-bit quantization and engine-level optimizations reducing bytes and FLOPs per token.

Limitations

Not trained for general-purpose chat or broad world knowledge; corpus focuses on consumer device actions and structured extraction.
Performance concentrates where training distribution exists; Java and JavaScript SDK-style API surfaces and multi-call Java categories lie outside the training distribution.
Scope is intentionally narrow: Needle optimizes tool calling and structured outputs at the cost of not providing a full general-purpose language-model capability.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Read the docs and product page on cactuscompute.com to understand capabilities and benchmarks.
  2. 2 Explore the Needle 2 repository and distribution (links on the page: Hugging Face, GitHub) and obtain the shipped binary or source.
  3. 3 Integrate the C++ engine or WebAssembly build for your target platform and include your tool schemas (grammar compiler enforces well-formed outputs).
  4. 4 Fine-tune the provided model locally using the repo and python package on your Mac/PC to teach product-specific tool vocabularies.
  5. 5 Deploy the single artifact to the target device, set your confidence threshold, and if needed configure escalation to cloud services.

Support

docs

Read the docs linked from the product page on cactuscompute.com for usage, benchmarks and integration guidance.

contact

Use the site's 'Talk to us' / Contact links on cactuscompute.com/needle to request custom tool schemas, hardware tuning, or post-training assistance.

repo / community

Explore the GitHub repository and Hugging Face pages (linked from the product page) for code, examples and model artefacts.

API

Available: Yes
Documentation:

Documentation and developer resources are linked from the product page ("Read the docs"); repo and python package provide developer integration points.

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