Eval Based System
Overview
Our Adaptive Reasoning Models represent a breakthrough in large-scale inference optimization and real-time decision support systems. Built on our Service Fabric architecture, these models dynamically adjust their reasoning processes based on context, computational constraints, and performance requirements.
The system leverages multi-tiered processing, speculative decoding, and self-calibration to deliver unprecedented performance while maintaining accuracy and reliability in production environments.
Core Architecture
Multi-Tiered Processing System
Our adaptive reasoning system employs a sophisticated multi-tiered architecture designed for real-time analysis and decision support:
Processing Intervals:
1-second checks: Immediate response validation and basic reasoning
5-second checks: Intermediate reasoning with context evaluation
60-second checks: Deep reasoning and comprehensive analysis
Concurrent Processing Capabilities:
Support for up to 144,000 concurrent streaming sessions
224 CPU cores processing approximately 650 checks per core per second at peak
Memory-efficient design with ~20MB of embeddings per 2-hour stream
Speculative Decoding Architecture
Our implementation uses an innovative speculative decoding approach:
2.8B parameter model generates 5 possible continuations
400B parameter model (LLaMA-4) verifies and selects optimal predictions
Custom kernels optimize the verification process
Self-calibration system adjusts weights during runtime
Infrastructure & Cost Analysis
Current Usage & Projections
Current Anthropic usage: ~$900 every 1-2 days ($5.4k month-to-date)
Significant cost optimization opportunity through self-hosted infrastructure
$35k/month for two nodes at peak capacity
$20k reduction in GCP costs
Potential to absorb entire OpenAI bill through optimized self-hosting
Hardware Configuration
Production Setup:
48 NVL-72s running 24/7 for maximum availability
Docker image size: 46GB (compiled binary for optimization)
Multi-node architecture supporting horizontal scaling
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