Without retraining models. Without new hardware. Without changing a single model weight.
Today's AI inference engines execute the full neural network for every token — whether generating a simple word like 'the' or solving complex reasoning. This wastes enormous compute. Most tokens require far less computation than current systems assume.
Vectris identified a structural relationship between sequence entropy and required compute. We call this the entropy–compute scaling law. Our control plane continuously measures the information state of the sequence and dynamically adjusts the neural compute path — in real time, during inference.
The control plane sits between the AI model and the GPU hardware. It requires no model retraining, no weight modification, and is designed to integrate as a drop-in layer into existing stacks including HuggingFace, vLLM, Triton, and enterprise inference pipelines.
Complete validation of core mathematical principles underlying the entropy-compute relationship. Every invariant test passed with zero exceptions.
Multi-GPU simulations on LLaMA-70B across a 50-GPU MI300X cluster completed with zero runtime errors. All GPUs converged to stable operating points.
Global AI inference spending has already crossed $100B+ annually, with projections exceeding $300B within the decade. Inference is the dominant and fastest-growing workload as AI moves into production at scale. A 1.8× efficiency gain means a 10,000-GPU cluster performs like 18,000 GPUs — no new hardware required.
Drop-in inference efficiency control plane compatible with HuggingFace, vLLM, and Triton. Seamless integration into existing inference pipelines.
Cluster-level compute orchestration across large GPU fleets. Intelligent workload distribution and resource optimization at scale.
Hallucination risk and reasoning instability detection derived from information dynamics. Real-time quality assurance for AI outputs.
From your existing infrastructure
This document contains proprietary information. Simulation results reflect modeled GPU cluster performance. Production hardware validation in progress.