Local LLM Inference Stack – Multi-Agent Intelligence

Overview

Engineered a high-performance local inference stack to serve a multi-agent trading system, utilizing hardware acceleration for real-time market analysis and automated execution.

Key Contributions

• Optimized a local inference stack using llama.cpp and SYCL to leverage hardware-specific acceleration on consumer GPUs
• Deployed quantized 32B parameter models to balance high-reasoning capabilities with low-latency execution
• Architected a sequential multi-agent workflow where specialized agents handle sentiment analysis and trade decisioning
• Implemented automated execution pipelines for real-time market response based on LLM-driven insights