gemma-4-26B-A4B-it-GGUF

Gemma 4 26B A4B IT GGUF

License: Apache 2.0

Authors: Google DeepMind

A polished GGUF release of Gemma 4 26B A4B IT, prepared for efficient local inference with llama.cpp and compatible runtimes.

Demo: Gemma 4 26B A4B IT

Overview

Gemma 4 26B A4B IT GGUF is a quantized distribution of the original instruction-tuned base model. The goal of this release is straightforward: preserve as much of the original model quality as possible while making the model easier to run locally and more flexible to deploy.

GGUF is especially useful for users who want:

• fast local inference
• straightforward compatibility with modern llama.cpp-based tooling
• predictable memory requirements
• support across desktop and self-hosted environments
• a simple model format with broad ecosystem support

Available Quantizations

The following quantizations are included in this release:

If you are unsure which one to choose, Q5_K_M is usually the most practical middle ground. It keeps the model relatively light while preserving a solid amount of the original behavior.

Why This Release Exists

Large models often become impractical the moment they leave hosted inference environments. Memory, throughput, startup time, and runtime compatibility all become real constraints. GGUF solves part of that problem by making the model more accessible for local execution without changing the fundamental character of the underlying model.

This release is aimed at users who want a model that feels polished in practice:

• easy to load
• easy to run
• easy to integrate
• easy to reason about

The emphasis is not only on compression, but on usability.

Supported Runtimes

This model is compatible with:

• ModelD
• llama.cpp
• LM Studio
• Ollama
• Open WebUI
• KoboldCpp
• other GGUF-compatible tooling

For best results, use a recent version of the runtime you prefer. GGUF support continues to improve quickly, and newer builds generally provide better performance and more stable behavior.

Recommended Use Cases

This model is well suited for:

• chat and instruction following
• local assistant workflows
• writing support
• summarization
• idea generation
• code-adjacent reasoning
• offline experimentation
• evaluation and prototyping

It is especially useful when you want strong general-purpose behavior without depending on hosted APIs.

Getting Started

A typical local workflow looks like this:

1. Download the GGUF file that matches your hardware and runtime requirements.
2. Load it in your preferred GGUF-compatible application.
3. Use the instruction-tuned model as you would in a chat or assistant setting.
4. Adjust context, sampling, and runtime parameters to match your use case.

Exact launch commands depend on the runtime you use, but most GGUF tools follow a similar setup pattern: model path, context size, threads or GPU offload settings, and a chat template or system prompt.

Notes on Performance

Actual performance depends on:

• the selected quantization
• CPU and GPU capabilities
• available RAM and VRAM
• context length
• runtime configuration
• sampling settings
• batching and offload behavior

In practice, there is no single best configuration. The best choice depends on whether you value responsiveness, memory efficiency, or output quality most.

Model Identity

This repository contains quantizations based on:

• google/gemma-4-26b-a4b-it

The original model defines the behavior and overall capabilities. This repository focuses on packaging and deployment convenience through GGUF quantization.

ModelD Deployment

If you want to serve this model locally with a polished self-hosted interface, use ModelD:

• Repository: https://github.com/prokopsafranek/modeld
• Overview: a local AI model server for managing models, APIs, auth, and monitoring
• Documentation: see the ModelD README in that repository for installation and deployment details

ModelD is a good fit when you want a single place to upload, manage, and run GGUF models behind a clean web UI.

Attribution

Quantized by:

Prokop Šafránek

Based on:

google/gemma-4-26b-a4b-it

License

This repository follows the Apache 2.0 license.