Inserloft

Hi, I`m Cleo

Introducing NaNo 3.1

A new generation of the NaNo model: optimized for programming, edge AI, and mobile devices

At Inserloft, we continue pushing forward our vision of building modern, efficient, and highly optimized AI models. Today, we are officially introducing NaNo 3.1, the biggest and most ambitious update to the model so far.

NaNo was created with a clear objective: to build a lightweight, fast, and highly efficient AI model specialized for programming tasks and deployment on edge and mobile environments. With version 3.1, we are taking that vision much further.

This new generation introduces major improvements in architecture, context understanding, conversational stability, and technical reasoning, alongside a significant increase in scale: NaNo has grown from 22 million parameters to 52 million parameters, more than doubling the size and capability of previous generations.

NaNo 3.1 represents a major step forward for our AI ecosystem and establishes a much stronger foundation for future versions of the model.

Built for Programming and Edge AI

Unlike general-purpose models focused primarily on educational or broad conversational tasks, NaNo is specifically designed for:

  • Programming assistance
  • Code generation
  • Technical automation
  • Edge AI
  • Local inference
  • Mobile devices
  • Lightweight AI systems
  • Embedded AI applications

Our goal with NaNo is to create a model capable of running efficiently on limited hardware while still delivering modern technical reasoning capabilities.

This makes NaNo ideal for:

  • Mobile applications
  • Developer tools
  • Offline AI systems
  • Local AI assistants
  • Embedded software
  • Edge computing platforms
  • Lightweight coding copilots

From 22M to 52M Parameters

The jump from 22M → 52M parameters marks a major evolution in NaNo’s architecture.

During the development of version 3.1, we redesigned several internal components to improve:

  • Code understanding
  • Context retention
  • Conversational consistency
  • Technical accuracy
  • Generation quality

The increased capacity allows the model to process more complex instructions and generate significantly more coherent and useful outputs for real-world development workflows.

Despite the larger size, NaNo still maintains its core philosophy of efficiency and optimization — a critical factor for edge and mobile deployments.

Improved Context and Technical Reasoning

One of the biggest focuses of NaNo 3.1 was improving the model’s ability to handle long technical conversations and programming workflows.

The new version significantly improves:

  • Technical instruction following
  • Long-context code understanding
  • Response consistency
  • Multi-step task handling
  • Structured code generation

This makes NaNo 3.1 much more capable for:

  • Autocompletion
  • Refactoring
  • Function generation
  • Technical explanations
  • Basic debugging
  • Workflow automation
  • Local AI integrations

Optimized for Real-World Performance

Performance has always been one of the core pillars behind NaNo.

Many modern AI models require extremely powerful hardware to run effectively. With NaNo 3.1, we are pursuing a different approach: building an efficient model that is actually usable in modern edge computing environments.

NaNo 3.1 has been optimized for:

  • Low latency
  • Efficient inference
  • Reduced memory usage
  • Better performance-per-parameter
  • Compatibility with limited hardware

We believe the future of AI is not only about building massive models, but also about creating smarter and more optimized systems capable of running almost anywhere.

A Specialized AI Ecosystem

At Inserloft, we are building multiple specialized AI models for different purposes.

While NaNo is primarily focused on programming, edge AI, and lightweight performance, our most advanced model currently is Kyro, designed for much broader and more advanced intelligence capabilities.

Kyro represents our large-scale, high-intelligence model line, while NaNo focuses on speed, efficiency, and optimized deployment.

This separation allows us to build more specialized systems optimized for real-world use cases instead of forcing a single model to handle everything.

Training and Development

NaNo 3.1 was developed using new internal methodologies focused on:

  • Architecture optimization
  • Training quality
  • Conversational stability
  • Better code understanding
  • Efficient inference

We are also continuously working on future improvements to further increase the model’s performance and capabilities.

What’s Next for NaNo

NaNo 3.1 is only the beginning of a new stage for the project.

We are already working on:

  • Better technical reasoning
  • Longer context windows
  • Greater efficiency
  • Improved multilingual support
  • New mobile AI optimizations
  • Faster inference
  • Better code generation
  • New internal architectures

Our goal is to make NaNo one of the best lightweight AI models for programming and edge computing.

The Future of Inserloft AI

The vision of Inserloft is to build a new generation of specialized, efficient, and modern AI systems.

NaNo 3.1 represents a huge step in that direction.

We will continue developing AI technology focused on real-world performance, optimization, and practical capabilities for developers and modern products.

And this is only the beginning.