LLM Foundations Applied: Optimizing Large Language Models for Apple M2 Ultra Consumer Hardware

This technical report bridges the gap between textbook large language model (LLM) theory and practical deployment on consumer-grade Apple Silicon hardware. We apply key results from Foundations of Large Language Models (Xiao & Zhu, 2025) to the specific constraints and capabilities of the Mac Studio M2 Ultra (192 GB unified memory, 76 GPU cores) running in the Hayula AI ecosystem. We present actionable guidance across five dimensions: (1) inference optimization — KV cache management, continuous batching, and speculative decoding on Metal backend; (2) scaling laws for local deployment — deriving the optimal model size of 30B–40B parameters at Q4_K_M quantization for 192 GB systems; (3) long sequence modeling — extending DeepSeek V4 Flash from 8K to 16K+ context using position interpolation and grouped-query attention; (4) prompting strategies for bug bounty analysis — chain-of-thought, self-refinement, and self-consistency pipelines integrated with Hayula's SAIF specialist swarm; and (5) inference-time scaling — using Best-of-N sampling and thinking paths to substitute inference compute for model size. Each section includes concrete mappings to Yahya's existing GGUF/Ollama pipeline, SAIF multi-agent system, and Hayula Swarm orchestrator. We conclude with an implementation roadmap for deploying the next generation of the Beyond v4 security analysis platform.