Full Deployment gemma-4-26B-A4B-it-QAT-MLX-4bit Quantized GGUF

The fastest method for installing this model locally is by using Docker.

Follow the sequence of steps detailed below.

The setup auto-streams the model assets (expect a multi-GB download).

The deployment tool scans your environment and automatically chooses the ideal parameters for your OS.

🗂 Hash: e713d9b62182435001f2c731025c13c6 • Last Updated: 2026-06-22
  • Processor: 6-core 3.5 GHz minimum required
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space:70 GB free space for full FP16 weights storage
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

gemma-4-26B-A4B-it-QAT-MLX-4bit is a large language model built on the Gemma architecture with 26 billion parameters and optimized for instruction following. It leverages A4B design principles to improve inference efficiency while maintaining high fidelity in generation tasks. Through quantized aware training (QAT) and MLX optimizations, the model achieves compact 4‑bit representation without significant loss in accuracy. The resulting model excels in multilingual understanding, reasoning, and code generation, making it suitable for both research and production environments. Its reduced memory footprint enables deployment on consumer hardware and edge devices, broadening accessibility for developers. A quick reference of its core specs is provided below.

Parameters 26 B
Quantization 4‑bit QAT with MLX
  • Script downloading IP-Adapter-FaceID weights for local consistent character pipelines
  • gemma-4-26B-A4B-it-QAT-MLX-4bit via WebGPU (Browser) Windows FREE
  • Downloader pulling multi-platform standardized model formats for universal client execution
  • Deploy gemma-4-26B-A4B-it-QAT-MLX-4bit Windows 11 For Low VRAM (6GB/8GB) Local Guide FREE
  • Installer deploying Jan.ai desktop client with pre-loaded LLM engines
  • Run gemma-4-26B-A4B-it-QAT-MLX-4bit Locally via Ollama 2 with Native FP4 Full Method FREE
  • Setup tool configuring MemGPT agent memory layers with local GGUF nodes
  • Deploy gemma-4-26B-A4B-it-QAT-MLX-4bit Offline on PC FREE