I will conduct a local hardware & model compatibility audit

Running local LLMs and RAG pipelines requires a precise balance of hardware resources. If your model parameters exceed your available GPU VRAM or system RAM, execution speed drops to near-zero as your OS is forced to use system storage.

This Micro Zinn provides a complete compatibility audit of your hardware before you buy or install any software. We evaluate your physical system—whether it is an Apple Silicon Mac, an NVIDIA CUDA workstation, or a standard Windows/Linux server—to tell you exactly how to achieve optimal local performance.

What we analyze:
1. GPU VRAM constraints: We map your dedicated graphics memory to find your maximum model parameter ceiling (e.g., 7B, 13B, or 34B models).
2. System RAM allocation: We determine your CPU-inference thresholds and offloading limits if you lack a dedicated GPU or run on shared system memory.
3. Quantization mapping: We recommend the exact quantization level (such as Q4_K_M, Q5_K_M, or Q8_0) to balance processing speed and model intelligence.
4. Context window limits: We calculate your safe maximum token limits to prevent system out-of-memory crashes during heavy document retrieval.

Stop guessing which open-source weights to download or why your local setup is slow. Get an engineered, hardware-specific map for your local AI environment from operators who build these systems on physical metal daily.