Summary
Fahd Mirza walks through a complete, reproducible tutorial for running GLM-5.3 Flash — ZAI’s 320-billion-parameter sparse mixture-of-experts model (8-18B parameters active) — entirely on CPU and system RAM with zero GPU involvement, then demonstrates partial and full GPU offloading for comparison. The enabling technology is an Unsloth dynamic 3-bit QDIQ3 quantization that compresses the model to 125 GB of RAM while preserving approximately 82% of original accuracy at 81% smaller file size.
The tutorial requires a special Unsloth fork of llama.cpp with native GLM-5.3 Flash support — standard llama.cpp installations are incompatible. Mirza covers apt package installation, CMake compilation with GPU support flags, Hugging Face model download (requiring 120-130 GB of free disk), and CLI invocation specifying zero GPU layers. Running on a 16-core CPU with 128 GB RAM, the model loads successfully with all cores pinned at 99-100% utilization. A llama-server mode is also demonstrated for browser-based interaction.
The primary quality benchmark involves generating a fully interactive tabbed webpage covering national animals from 60+ countries across six continents, with multilingual labels and country flags — a test designed to stress structured data handling, nested UI logic, and world knowledge simultaneously. Despite aggressive quantization, the model completes the task without hallucination loops, a common failure mode for heavily quantized models. Partial GPU offloading (12 layers onto a Blackwell GPU with 96 GB VRAM) is shown as a practical middle ground that dramatically increases token-per-second throughput.
📺 Source: Fahd Mirza · Published August 28, 2026
🏷️ Format: Tutorial Demo
