Modular efficiency layer for energy-optimized LLM inference

The RCF-LIM is a software module designed to improve the computational efficiency of any large language model during inference. It identifies repetitive internal patterns within the neural network and reduces unnecessary operations in a controlled, model-safe manner — without modifying the model architecture or requiring retraining.

The goal of the RCF-LIM is to deliver:

• – at least 30–40% energy and compute savings,
• – more stable and predictable performance,
• – reduced hardware load,
• – and broad compatibility with existing LLM architectures.

What does the RCF-LIM do? (in simple terms)

During inference, the module:

• – detects recurring internal activation patterns,
• – optimizes computations that do not contribute new information,
• – dynamically regulates attention-related load,
• – and can temporarily reduce the activity of certain layers.

It operates as an external optimization layer, requiring no architectural changes and no model retraining.

Why is it safe and credible?

• – completely external add-on (the model remains untouched)
• – no drastic interventions
• – does not introduce or collect new data
• – requires no fine-tuning or re-training
• – hardware-agnostic (GPU, CPU, NPU, edge devices)
• – compatible with all major LLM families

This makes the RCF-LIM a low-risk component suitable for enterprise, academic, and governmental environments.

Why this project matters

The cost and infrastructure demands of modern language models have become a major barrier to scaling.
The RCF-LIM provides a:

• – conservative,
• – engineering-sound,
• – easy-to-pilot,

solution that can be demonstrated through small-scale benchmarks and early partner tests.

Where it fits in the AVA ecosystem

The module is a foundational component of the broader AVA architecture:

• – part of the AVA Core,
• – runs on AVA-node hardware,
• – integrates with the Meta-Hopfield and Resonant Logic layers,
• – and is deployable across partner infrastructures with minimal adaptation.

Project status

• – Ready-to-start project
• – Mathematical and architectural foundations complete
• – Suitable for early pilot demonstrations
• – Open for international collaboration
• – Low technical risk
• – Short integration timeline