ARXH-DC+
Adaptive compute infrastructure.
A proposed orchestration layer for AI infrastructure that filters requests and routes workloads across caching, CPUs, smaller models, GPUs and cloud capacity. Its objective is to balance response quality, latency, resource consumption and energy use.
Workload admission and filtering
A defined component within the proposed architecture, intended to support more reliable decisions and clearer system behaviour.
Compute-tier routing
Structured processing and explicit criteria guide selection, interpretation or optimisation.
Resource and energy optimisation
An area for progressive engineering, evaluation and refinement as the technology is developed.
The challenge.
AI workloads vary greatly in difficulty, yet indiscriminate high-cost compute allocation can be inefficient.
How it works.
A proposed orchestration layer inspects requests and chooses among cache, CPU, smaller-model, accelerator or elastic cloud tiers, then validates results.
Conceptual workflow, not a production system diagram.
Development position.
Architecture proposal / research; energy and cost benefits presented in collateral are targets rather than independently verified measurements.
Where it may apply.
Data-centre scheduling, model serving, workload admission, resource allocation and AI infrastructure.
Questions still open.
Overhead, energy telemetry, real-world hardware diversity, failure modes and service-level trade-offs remain to be tested.