Definition
Candidate Model Architecture refers to a modular AI system design that enables organisations to test and deploy alternative large language models (LLMs) for specific tasks while maintaining production stability.
This architecture allows customers to seamlessly swap underlying LLMs without disrupting workflows.
Key aspects of candidate model architecture:
Modular interfaces
Standardised APIs and containerization ensure compatibility across LLM providers
Dynamic routing
Real-time performance monitoring automatically shifts workloads between “blessed” (production) and candidate (experimental) models based on metrics like latency or accuracy.
A/B testing framework
Parallel inference pipelines enable side-by-side comparison of model outputs for quality assurance.
Context preservation
Maintains session memory and application state during model swaps to ensure user experience continuity.
Summary
In enterprise contexts, this architecture empowers businesses to leverage specialized LLMs while maintaining baseline services through proven models. According to Databricks benchmarks, organisations using this approach reduce LLM operational costs by 35-50% through optimised model-task matching and GPU utilisation.
