Model Performance & Optimization

Overview

In the field of compute optimization, custom kernel implementations, memory-mapped model loading, and batch processing optimization are crucial for enhancing performance. For cost optimization, strategies like intelligent model routing, caching, and resource sharing are essential to efficiently manage resources and reduce expenses.

Model Performance Summary

Technical Implementation

Memory Management:

• Scrolling window approach for real-time analysis

• 15-20MB in live RAM per 2-hour stream allocation

• Efficient embedding storage and retrieval

Processing Optimization:

• Custom chunking function with dynamic sizing

• ICD codes as ground truth anchors for medical reasoning

• Built-in self-calibration for adaptive weight adjustment

Performance Metrics

LLaMA-4 Optimization	DeepSeek Performance	Nano Model (4.1)
Successfully optimized to reach theoretical maximum TPS for 400B parameter model Fits alongside DeepSeek in memory (total 1.1T parameters simultaneously) Startup time: 8-12 minutes KV cache calculation: ~30 minutes (one-time per startup) Attention head recalibration: 2-3 minutes	Running at 30,000 tokens/second for 700B parameter model on single node Production-ready performance for large-scale inference	Optimized for evaluator tasks with lower compute requirements Showing promising results for reference-free evaluations Current testing indicates potential for 20 notes/second generation speed

Performance Optimization Strategies

Adaptive Processing

Dynamic Resource Allocation:

• Automatic scaling based on reasoning complexity

• Context-aware compute allocation

• Priority-based processing queues

Model Selection:

• Nano models for simple evaluator tasks

• Medium models (2.8B) for speculative generation

• Large models (400B+) for complex reasoning verification

Efficiency Optimizations

Compute Optimization:

• Custom kernel implementations for specific operations

• Memory-mapped model loading for faster startup

• Batch processing optimization for concurrent requests

Cost Optimization:

• Intelligent model routing based on complexity requirements

• Caching strategies for repeated reasoning patterns

• Resource sharing across concurrent sessions