Needle 2 Model Memory Requirement: 28MB RAM for Full Inference

Needle 2 requires approximately 28MB of RAM to run a complete inference session and ships as a single 14MB binary, making it suitable for edge devices and microcontrollers.

The cactus-compute/needle repository provides a compact language model designed for extreme efficiency. Needle 2 delivers full tool-calling capabilities while maintaining a minimal footprint through specialized quantization and a self-contained architecture. Understanding the exact memory requirement for the Needle 2 model helps developers deploy AI on resource-constrained hardware.

Memory Specifications and Binary Footprint

Needle 2 is a 45M-parameter model compressed into a single binary. The distribution method and runtime behavior reflect aggressive optimization for edge deployment.

Binary Size vs. Runtime Memory

The model ships as one self-contained file. When deployed, the memory overhead remains minimal.

• 14MB binary size: The complete model weights are embedded in a single file using CQ2-bit quantization
• 28MB RAM usage: A full inference session loads the binary and executes end-to-end within this memory budget
• Zero external dependencies: All weights are embedded inside the binary, eliminating dynamic loading overhead

This footprint enables deployment on tiny edge devices and microcontrollers where every kilobyte counts.

Architecture Choices Driving Efficiency

The modest memory requirement stems from specific implementation decisions in the source code. The needle/model/architecture.py file implements the Simple Attention Network, a streamlined transformer variant that reduces activation memory. Additionally, Cactus Quants compression reduces precision to 2-bit weights (CQ2-bit), cutting storage requirements by 75% compared to 8-bit quantization.

Loading and Running Needle 2 Within Memory Constraints

The needle/model/run.py file provides memory-efficient loading functions. Below are practical examples demonstrating how to stay within the 28MB budget.

CLI Execution

Use the command-line interface for immediate execution without manual memory management.

# The CLI automatically loads the 14MB embedded binary

needle run "Summarize the latest news about AI."

The needle run command invokes the logic in needle/cli.py, which handles the load_checkpoint call internally. Runtime memory stays within the 28MB limit throughout execution.

Direct Python API Integration

For programmatic access, import the loading and inference functions explicitly.

from needle.model.run import load_checkpoint, inference

# Load the 14MB binary containing all 45M parameters

params, config = load_checkpoint("needle-2")

# Execute inference while maintaining ~28MB RAM usage

prompt = "List three ways to reduce electricity consumption at home."
output = inference(params, config, prompt)

print(output)

The load_checkpoint function pulls the pre-packed weights into memory, while inference manages the forward pass without allocating excessive activation buffers.

Streaming Generation for Minimal Footprint

When processing long outputs, use streaming to avoid holding the full response in memory.

from needle.model.run import load_checkpoint, stream_inference

params, config = load_checkpoint("needle-2")

# Stream tokens to further reduce peak memory usage

for token in stream_inference(params, config, "Explain quantum computing in simple terms."):
    print(token, end="", flush=True)

The stream_inference generator yields tokens incrementally, ensuring the process never exceeds the baseline 28MB allocation even during extended generation tasks.

Key Source Files Managing Memory Efficiency

Several modules in the cactus-compute/needle repository collaborate to maintain the strict memory budget.

• needle/model/run.py: Implements load_checkpoint, inference, and stream_inference with careful buffer management
• needle/model/architecture.py: Defines the Simple Attention Network structure that minimizes intermediate activation storage
• needle/cli.py: Provides the needle run entry point that orchestrates loading without user-side memory configuration
• README.md: Documents the official 14MB binary size and 28MB RAM specification

These components work together to ensure Needle 2 operates reliably on hardware with severe memory constraints.

Summary

Needle 2 achieves extreme efficiency through architectural compression and embedded weight packaging.

• 14MB binary contains the complete 45M-parameter model using CQ2-bit quantization
• 28MB RAM supports a full end-to-end inference session
• Simple Attention Network architecture reduces activation memory overhead
• Self-contained engine eliminates external dependency loading

This profile makes Needle 2 viable for microcontrollers and edge devices that cannot accommodate larger transformer models.

Frequently Asked Questions

How much RAM does Needle 2 need to run a full inference session?

Needle 2 requires approximately 28MB of RAM to execute a complete inference session. This includes the memory needed to load the binary, process activations through the Simple Attention Network layers, and generate output tokens. As documented in the repository's README, this footprint remains consistent across both the CLI and Python API implementations.

What is the file size of the Needle 2 model binary?

The model ships as a single 14MB binary file. According to the cactus-compute/needle source, this file contains all 45 million parameters compressed using CQ2-bit quantization. The self-contained design embeds all weights directly in the binary, requiring no additional downloads or external weight files.

Can Needle 2 run on microcontrollers and tiny edge devices?

Yes. The combination of a 14MB binary footprint and 28MB runtime memory requirement enables deployment on microcontrollers and severely constrained edge hardware. The load_checkpoint function in needle/model/run.py is optimized for environments with minimal available RAM, and the CQ2-bit quantization reduces storage demands to levels feasible for flash memory-constrained systems.

How does streaming inference affect memory usage in Needle 2?

Streaming inference using stream_inference maintains the baseline 28MB memory budget while preventing accumulation of large output buffers. Rather than storing the complete generated sequence in memory before returning, the function yields tokens incrementally. This approach keeps peak memory usage flat even when generating long responses, making it the preferred method for sustained operation on memory-limited devices.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}