Unified intelligent workload distribution across GPU and CPU architectures

The HGO (Hybrid GPU–CPU Optimizer) is a software layer designed to dynamically distribute computational workloads between GPUs and CPUs in real time. Its purpose is to increase system efficiency and throughput by ensuring that each task runs on the most suitable processing architecture — resulting in at least 30–40% improvement in performance and energy efficiency.

The HGO unlocks additional capacity in existing hardware without requiring infrastructure upgrades.

What does the HGO do?

The system continuously evaluates:

• – AI/LLM/HPC task requirements,
• – GPU and CPU utilization levels,
• – memory and bandwidth patterns,
• – latency and transfer constraints between devices.

Based on this analysis, the HGO:

• – moves tasks to the optimal compute unit (GPU or CPU),
• – reduces GPU overload during peak demand,
• – eliminates slow or redundant CPU-bound operations,
• – ensures more stable and predictable performance,
• – minimizes switch-over losses between compute units.

It augments existing GPU/CPU schedulers — it does not replace them.

Key advantages

• – at least 30–40% efficiency improvement
• – faster, more stable execution under heavy load
• – reduced overheating and throttling
• – lower energy consumption
• – significantly better utilization of high-value GPU resources
• – compatible with CUDA, ROCm, OpenCL, PyTorch, TensorFlow, JAX, and major frameworks
• – no hardware or architectural modifications required

Why is it safe and reliable?

• – fully software-based, no hardware intervention
• – transparent, auditable internal logic
• – no changes to model or application architecture
• – fallback mechanisms ensure stability
• – low integration risk

Where can it be deployed?

• – enterprise and research compute clusters
• – AI/LLM training and inference environments
• – HPC systems
• – GPU farm data centers
• – edge or hybrid GPU–CPU infrastructures
• – cloud providers with GPU bottlenecks

How it fits into the AVA ecosystem

The HGO is a core infrastructure component of the Resonant Intelligence architecture:

• – works together with RCF-LIM to improve LLM efficiency,
• – cooperates with ADC-Optim for energy-level optimization,
• – integrates into the adaptive scheduling logic of AVA Core,
• – and forms a key part of AVA-node high-load operation.

Project status

• – ready-to-start project, suitable for pilot deployment
• – low integration risk
• – rapid implementation cycle
• – high international applicability