The O Routing Intelligence (ORI) is a cutting-edge AI routing system designed to optimize query processing and model selection for a wide range of applications. This comprehensive system maintains a vast registry of over 100,000 models and connects to more than 200 Inference-as-a-Service providers, making it a powerful tool for AI-driven tasks. The ORI's key features include an ethical assessment model, a query breakdown module, a benchmark matching engine, and a model-to-model routing system, all working together to ensure efficient and responsible AI operations. The system also incorporates advanced inference metrics management, a user-friendly API layer, and robust scalability features, with a current capacity of 1,000 requests per second (RPS) and a target of 1,000,000 RPS. Leveraging high-performance hardware infrastructure, including Cerebras WSE-3 clusters and IO.NET Ray Clusters, the ORI ensures rapid inference and processing while maintaining compliance with industry standards such as HIPAA and SOC2. With its extensive external API integration, optimization techniques, and plans for expansion, the ORI represents a significant advancement in AI routing technology, offering users a powerful and flexible solution for their AI needs.
Objectives
Maintain a registry of over 100,000 models, mirroring HuggingFace's open-source model library
Connect to 200+ Inference-as-a-Service providers
Key Technical Features
1. Ethical Assessment Model
Utilizes a fine-tuned 7-10B parameter expert LLM
Filters queries based on predefined ethical criteria
Trained on human-reviewed data for ethical decision making
2. Query Breakdown Module
Implements a chain-of-thought approach
Uses a specialized 7-10B parameter expert LLM
Decomposes complex queries into atomic sub-queries
3. Benchmark Matching Engine
Employs knowledge graphs, vector embeddings, and classification models
Maintains a dynamic registry of 100K+ models
Selects optimal models based on performance on standardized benchmarks (e.g., IFEval, BBH, MATH, GPQA, MuSR, MMLU, BFCL)
4. Model-to-Model Routing System
Facilitates inter-model and agent-to-model communications
Implements cost-aware and latency-aware routing strategies
Utilizes a 1-5B parameter LLM for evaluating response quality
5. Inference Metrics Management
Employs in-memory data caching (e.g., Redis)
Tracks latency, inference speed (tokens/second), and cost metrics
Dynamically updates metrics through real-time ML pipelines
6. API Layer
Handles user queries and preference specifications
Supports both RESTful endpoints and CLI interfaces
Provides detailed logging and transparency options
7. Scalability Features
Current capacity: 1,000 RPS (Requests Per Second)
Target capacity: 1,000,000 RPS
Leverages Cerebras WSE-3 clusters for high-throughput inference
8. Infrastructure
Primary deployment on ATLAS.O (Cerebras WSE-3 cluster)
Secondary deployment on IO.NET Ray Clusters (GPU: H100, A100)
Ensures HIPAA/SOC2/TEE compliance
9. External API Integration
Incorporates 200+ external APIs for extended functionality
Includes search, social media, image/video processing, and financial data APIs
10. Optimization Techniques
Utilizes compiler-level optimizations for structured LLM tasks
Implements TEE (Trusted Execution Environment) for secure computing
Hardware Infrastructure Hosting the ORI & models
Inference Endpoints Registry
Aims for 100K+ models in the registry (mirroring HuggingFace open-source models).