Local Deployment: Running Open Models on Consumer Hardware using Ollama

Hardware & Memory Requirements

• Memory Management Rule-of-Thumb (Q4_K_M): Requires ~0.6GB per billion parameters.
• Safety Margin: Target a usable budget that is approximately 70% of your total system RAM/VRAM to allow room for OS, context windowing, and KV cache.
• Tiered Capacity Guidelines:
  8GB RAM: Maximize up to ~8B params (~5.6GB usage)
  16GB RAM: Maximize up to ~14B params (~11GB usage)
  24GB RAM: Maximize up to ~27B params (~17GB usage)
  32GB RAM: Maximize up to ~35B params (~22GB usage)
  48GB RAM: Maximize up to ~47B params (~34GB usage)
  64GB RAM: Maximize up to ~70B params (~45GB usage)
  128GB RAM: Maximize up to ~122B params (~90GB usage)
• Platform Compatibility: Strong coverage on Apple Silicon and consumer NVIDIA GPUs or any setup using Ollama engines via CLI.

Installation & Launch Guide

1. Environment Setup (Ollama): Install the Ollama engine as suggested by Tina Huang's guide for local execution through a command-line interface.

   # Example way to install/run certain models if available in registry

2. Model Selection (Quantization check): Ensure you are downloading weights compatible with your hardware tier, specifically targeting Q4_K_M quants to stay within the 70% memory budget mentioned above. For example, if running Gemma 4 26B A4B, ensure enough headroom exists near the 24GB+ mark properly managed.
3. Execution Command: Use the ollama run commands provided per model type:

   ollama run [modelname]

Optimization & Performance Tips

• RAG Optimization: To prevent prompt overloading, use Retrieval Augmented Generation (RAG). Instead of injecting hundreds of possible actions into every turn, only inject contextually relevant ones based on player message/context. This keeps prompts lean and fast.
• Speculative Decoding: Utilize speculative decoding or other sota approaches allowed by high-end local engines to maximize tokens per second (tps) without increasing heavy overhead.
• Memory Headroom Management: Always reserve ~30% of RAM so that long contexts do not cause crashes due to KV cache expansion.

The main advantage is total data privacy where LLMs like Anthropic and OpenAI cannot access personal contents, combined with unlimited free usage for fine-tuning any dataset you want locally.

! DYOR (Do Your Own Research)