Key Business Impact

Evaluated GenAI training economics and performance for production planning.

Project Overview

Distributed LLM training benchmarking on custom hardware to compare training time and cost economics.

Technical System Architecture

Operational data flow and system architecture designed for this solution:

input

Training Dataset

process

SageMaker Training

database

Trainium/Infern. HW

ai

Model Fine-Tuning

output

Model Deployment

Case Study & Delivery

Led adaptation and fine-tuning benchmarks for enterprise model training on specialized hardware.

Consulting Assessment & Strategy

As an AI consultant, the primary focus for this project was to establish a production-grade infrastructure that balances LLM performance, response latency, and system cost. This was achieved by introducing specific design patterns:

  • Agentic Orchestration: Decoupling tasks into dedicated specialized agents to reduce complexity and improve reasoning accuracy.
  • Custom Model Routing: Routing simple tasks to lightweight tier-2 models (e.g. AWS Nova Flash / Sonic) and reserving heavy reasoning for flagship models.
  • Security & Compliance Guardrails: Integrating strict input/output verification steps to prevent PII exposure and prompt injections.