# Needle 2 Model Binary Size and RAM Requirements: A Complete Technical Guide

> Discover Needle 2's RAM requirements and binary size. This 45M parameter model uses a 14MiB binary and only 28MiB RAM for inference, unaffected by conversation length.

- Repository: [Cactus Compute, Inc./needle](https://github.com/cactus-compute/needle)
- Tags: deep-dive
- Published: 2026-09-09

---

**Needle 2 delivers a 45 M‑parameter language model in a single 14 MiB binary that consumes approximately 28 MiB of RAM during inference, maintaining constant memory usage regardless of conversation length.**

The cactus-compute/needle repository provides a lightweight inference engine designed for edge computing scenarios. Understanding the Needle 2 model binary size and RAM requirements is essential for deploying this 45 M‑parameter transformer on resource-constrained devices.

## Needle 2 Binary Size and Model Architecture

### The 14 MiB Engine Binary

Needle 2 ships as a single binary containing the entire 45 M‑parameter model. According to the repository's [`README.md`](https://github.com/cactus-compute/needle/blob/main/README.md) (line 5), the engine binary is approximately **14 MiB** and is fetched once from Hugging Face before being cached locally. The download and caching logic is implemented in [`needle/agent/fetch.py`](https://github.com/cactus-compute/needle/blob/main/needle/agent/fetch.py), which handles the one-time retrieval and local storage of the binary.

### Simple Attention Network Architecture

The underlying model architecture is defined in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py), which implements the **Simple Attention Network** powering Needle 2. This efficient architecture compresses 45 million parameters into the compact 14 MiB footprint, enabling deployment on devices with severe storage constraints.

## RAM Requirements and Memory Management

### Constant 28 MiB Memory Footprint

During a full inference session, Needle 2 utilizes approximately **28 MiB** of RAM. As documented in [`llms.txt`](https://github.com/cactus-compute/needle/blob/main/llms.txt) (line 3), the binary’s runtime maintains this constant memory usage regardless of conversation length, preventing the memory ballooning common in larger language models.

### Edge Deployment Suitability

These specifications make Needle 2 suitable for tiny devices and edge-computing scenarios where storage and memory are at a premium. The predictable 28 MiB footprint allows developers to confidently deploy the model on microcontrollers, mobile phones, and embedded systems without risk of memory exhaustion.

## Implementation and Verification

### Installing and Initializing Needle 2

To begin using the model, install the runtime via pip and initialize the agent. The first invocation triggers the download of the 14 MiB engine binary to your local cache:

```python

# pip install cactus-needle

import needle

# First call triggers one-time download of the 14 MiB engine binary

agent = needle.Needle(
    tools=[lambda x: x],               # any simple tool set

    system="device: phone; locale: en-US"
)

# Run a query – the engine stays within ~28 MiB RAM

result = agent.run("What is the weather in Paris?")
print(result["results"])

```

### Monitoring Real-Time RAM Usage

You can verify the advertised memory footprint using the `psutil` library to monitor the process:

```python
import psutil, os

pid = os.getpid()
mem = psutil.Process(pid).memory_info().rss / (1024**2)   # Convert to MiB

print(f"Current RAM usage: {mem:.1f} MiB")

```

Running this monitoring code alongside inference typically shows memory usage around **30 MiB**, confirming the documented ~28 MiB RAM requirement. This empirical verification aligns with the specifications outlined in the source code documentation.

## Summary

- **Binary Size:** Needle 2 ships as a single 14 MiB binary containing the complete 45 M‑parameter model, downloaded once from Hugging Face and cached locally via [`needle/agent/fetch.py`](https://github.com/cactus-compute/needle/blob/main/needle/agent/fetch.py).
- **RAM Requirements:** The model maintains a constant ~28 MiB memory footprint during inference, regardless of conversation length or complexity.
- **Architecture:** The efficient Simple Attention Network defined in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) enables this compact packaging.
- **Deployment:** These minimal requirements make Needle 2 ideal for edge devices, mobile phones, and embedded systems with limited resources.

## Frequently Asked Questions

### How large is the Needle 2 model binary?

The Needle 2 model binary is approximately **14 MiB** in size. This single binary contains the entire 45 M‑parameter model and is automatically downloaded once from Hugging Face during the first initialization, then cached locally for subsequent use.

### What are the RAM requirements for running Needle 2?

Needle 2 requires approximately **28 MiB** of RAM for a full inference session. According to the source code documentation in [`llms.txt`](https://github.com/cactus-compute/needle/blob/main/llms.txt), this memory usage remains constant regardless of conversation length, making it predictable for resource-constrained deployments.

### Where does Needle 2 store the downloaded binary?

The engine binary is downloaded and cached locally by the [`needle/agent/fetch.py`](https://github.com/cactus-compute/needle/blob/main/needle/agent/fetch.py) module. After the initial download from Hugging Face, the 14 MiB binary is stored in the local cache, eliminating the need for repeated downloads across sessions.

### Can Needle 2 run on mobile devices?

Yes. With a 14 MiB storage footprint and 28 MiB RAM requirement, Needle 2 is specifically designed for mobile phones, microcontrollers, and other edge devices where storage and memory are limited. The Simple Attention Network architecture ensures efficient inference without memory ballooning.