Workflows/Set up private Gemma 4 chat

Text / WORKFLOW

Set up private Gemma 4 chat

Choose official QAT packages for Gemma 4 E2B, E4B, or 12B, or a clearly marked community abliterated E4B package; follow Mac, Windows, or Linux instructions for your choice.

Verified sourceSource checked 9/29/2026

LOCALRENTED GPU

Freshness 47 out of 100. Release age: 196 days old, Apr 2, 2026. Measures age, not quality.

Popularity 89 out of 100. 4,193,163 downloads in 30 days; 1,721 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.

What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source

Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source

Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

Before you begin

Hardware

Short text-chat estimates for the exact packages shown, ordered by model tier. Published weights plus estimated runtime headroom inform these rows; AMD and Intel still need a working GPU backend.

Machine	Gemma 4 E2B · QAT	Gemma 4 E4B · QAT	Gemma 4 12B · QAT	Gemma 4 E4B · abliterated Q4
NVIDIA GPU · 8 GB	Runs at short context; modest headroom	Runs at short context; tight headroom	Can run with CPU offload; slower	Runs at short context; modest headroom
NVIDIA GPU · 12 GB	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat
NVIDIA GPU · 16 GB	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat
NVIDIA GPU · 24 GB	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat
NVIDIA GPU · 32 GB	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat
AMD Radeon · 8 GB	Runs at short context; modest headroom; verify GPU backend	Runs at short context; tight headroom; verify GPU backend	Can run with CPU offload; slower; verify GPU backend	Runs at short context; modest headroom; verify GPU backend
AMD Radeon · 16 GB	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend
AMD Radeon · 24 GB	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend
AMD Radeon · 32 GB	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend
Intel Arc · 8 GB	Runs at short context; modest headroom; verify GPU backend	Runs at short context; tight headroom; verify GPU backend	Can run with CPU offload; slower; verify GPU backend	Runs at short context; modest headroom; verify GPU backend
Intel Arc · 16 GB	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend
Intel Arc · 24 GB	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend
Intel Arc · 32 GB	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend	Runs with room for short chat; verify GPU backend
Apple Silicon · 16 GB unified	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat
Apple Silicon · 24 GB unified	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat
Apple Silicon · 32 GB unified	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat
Apple Silicon · 64 GB unified	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat	Runs with room for short chat
CPU-only · 16 GB RAM	Runs in RAM at short context; slower	Runs in RAM at short context; slower	Runs in RAM at short context; slower	Runs in RAM at short context; slower
CPU-only · 32 GB RAM	Runs in RAM at short context; slower	Runs in RAM at short context; slower	Runs in RAM at short context; slower	Runs in RAM at short context; slower

An 8 GB GPU can run E2B and E4B QAT at short 4K text context; 12B QAT may require slower CPU offload and enough system RAM. These are capacity estimates. Start at 4K, then raise context while checking ollama ps for the GPU/CPU split; image input needs more memory.

TEXT / LOCAL GEMMA CHATSOURCE-LINKED SETUP

Pick the Gemma package that fits your machine.

Model tiers are ordered E2B → E4B → 12B; download size varies with quantization. These official QAT weights have smaller published downloads than the default tags. Select a package to switch the commands for your operating system.

Choose a package Run its commands Check the result

BEFORE YOU BEGIN

Choose your package

Select the weights you want. The instructions, downloads and settings change with your choice.

Save your machine in My Hardware to get an automatic starting choice. You can always choose any package yourself.

CURRENT SETUP

Gemma 4 E2B · QAT · official

Start with 4K text context on an 8 GB GPU or 16 GB Apple Silicon Mac. The 4.3 GB download leaves capacity for runtime and KV cache at that short setting; increase toward 8K only after checking ollama ps and free memory. Longer context and image input need more memory.

Freshness 47 out of 100. Release age: 196 days old, Apr 2, 2026. Measures age, not quality.

Popularity 86 out of 100. 2,974,727 downloads in 30 days; 1,044 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.

What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

Check the date source

Popularity · adoption

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source

Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

View weight source

THE WORKFLOW

Set up Gemma 4 E2B · QAT on your machine

Choose your computer first, then its GPU path. The instructions below update to match your selection.

CHOOSE YOUR SETUPMac

Use Terminal. Metal acceleration is supported on Apple Silicon.

Install Ollama for macOS

Install and launch the macOS app, then open Terminal.

Download Ollama

Check Apple Silicon

Confirm the Ollama app is open and Metal is available on this Apple Silicon Mac.

Ollama hardware support

Check the local runtime

In Terminal, confirm Ollama is installed before downloading weights.

ollama --version

Start the selected Gemma package

Run this exact official package. The first launch downloads it and opens a local text chat.

Official Gemma packages

ollama run gemma4:e2b-it-qat

Test a private reply

Ask a short question with a known answer, then follow up. This first check uses text only; image input and longer context need more memory.

Check where the model runs

In a second Terminal window, see whether Ollama loaded the model on the GPU or CPU and how much context it allocated.

ollama ps

SUCCESS CHECK · Gemma 4 E2B · QAT

Your first local Gemma reply

Ask a short, verifiable question and confirm a reply appears in the local Ollama session. Run ollama list in another terminal to verify the exact selected tag is installed.

The abliterated package is a third-party modification. Package sizes come from the linked sources; hardware tiers are estimates and have not been physically tested by YouRunAI.

Back to setup steps

When it doesn’t go to plan

The selected model fails to load

Check the exact tag with ollama list, close other GPU-heavy apps, and choose the E2B QAT package if VRAM is tight. If Ollama offloads to CPU, verify you have enough system RAM.

The community variant does not behave like the official model

Community-modified weights can change output quality and safety behavior. Switch back to the official E4B package to compare on the same prompt.

REFERENCE LIBRARY

Sources and files

Original instructions, model files, and compatibility notes behind this setup.

5 SOURCES

01Official Gemma 4 packages and sizesollama.com 02Official Ollama GPU and driver supportdocs.ollama.com 03Ollama context length and memory guidancedocs.ollama.com 04Official Ollama installerollama.com 05Community abliterated E4B model cardhuggingface.co

The model behind this workflow

Gemma 4 · E4B

Keep exploring

Text

SETUP GUIDE

Qwen3.5 · 9B private chat setup

Source-backed package and platform-route plan for private local chat with Qwen3.5 · 9B. Preserves the official Ollama 9B package, a smaller 4B family fallback, and a clearly labeled community uncensored GGUF option.

Freshness 41 out of 100. Release age: 230 days old, Feb 27, 2026. Measures age, not quality.

Build a private coding assistant with Qwen3.8 27B

Run Qwen3.8 27B with Ollama, connect it to Cline in VS Code, and make one reviewable code change in a tiny local project.

Freshness 76 out of 100. Repository age: 71 days old, Aug 5, 2026. Measures age, not quality.

Arena Open Source Web coding: rank 10 of 55. Ranked model: qwen3.8-27b. Original model ranking, not a benchmark of this package. Export dated Oct 13, 2026. Source checked Oct 15, 2026. Human preference ranking, not hardware suitability.+3

SOFTWAREOllama + Visual Studio Code + Cline + Python

Create images locally with FLUX.2 Klein

Generate images from text prompts in ComfyUI with the fast distilled or more adjustable base 4B model. Save your results and reuse the visual workflow.

Freshness 35 out of 100. Release age: 273 days old, Jan 15, 2026. Measures age, not quality.

SOFTWAREComfyUI

LOCALRENTED GPU

Verified source