Gemma 4 — Local AI Model by Google

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 10 lipca 2026

Google's fourth-generation open model family. The first wave (E2B, E4B, 26B-A4B MoE, 31B dense) launched March 31, 2026 under Apache 2.0 with a fully multimodal architecture — text, image, and audio across all sizes, plus video on the 31B. A separate 12B 'Unified' encoder-free model followed June 3, 2026, adding native audio+video to a laptop-friendly 16GB footprint. The E2B and E4B Efficient variants run on 4–6 GB VRAM GPUs, while the 26B-A4B MoE and 31B dense models deliver near-frontier quality on a single RTX 4090.

Hardware Requirements

Gemma 4 E2B	Min 4 GB VRAM · Q4 (QAT) · 128,000 ctx · `ollama run gemma4:e2b`
Gemma 4 E4B	Min 6 GB VRAM · Q4 (QAT) · 128,000 ctx · `ollama run gemma4:e4b`
Gemma 4 26B-A4B	Min 16 GB VRAM · Q4_K_M · 256,000 ctx · `ollama run gemma4:26b-a4b`
Gemma 4 31B	Min 20 GB VRAM · Q4_K_M · 256,000 ctx · `ollama run gemma4:31b`
Gemma 4 12B (Unified)	Min 8 GB VRAM · Q4_K_M · 128,000 ctx · `ollama run gemma4:12b`

Recommended GPU

The cheapest GPU that runs Gemma 4 locally (min 4 GB VRAM) is the Intel Arc B570 (10 GB).

Ujawnienie afiliacyjne: Niektóre odnośniki na tej stronie to linki afiliacyjne — jeśli dokonasz zakupu za ich pośrednictwem, LLM Configurator może otrzymać prowizję bez dodatkowych kosztów dla Ciebie. Jako uczestnik programu Amazon Associates, LLM Configurator zarabia na kwalifikujących się zakupach.

Intel Arc B570 10GB

Sugerowana cena premierowa: $219

Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.

Sprawdź cenę na Amazon

How to Run Locally

Install Ollama then run: ollama run gemma4:e2b

Minimum VRAM: 4 GB. For best results use Q4_K_M quantization.

Gemma 4 — Frequently Asked Questions

How much VRAM does Gemma 4 need?

Gemma 4 needs about 4 GB VRAM at Q4 (QAT) quantization for its smallest variant. Variants: Gemma 4 E2B (4 GB, Q4 (QAT)); Gemma 4 E4B (6 GB, Q4 (QAT)); Gemma 4 26B-A4B (16 GB, Q4_K_M); Gemma 4 31B (20 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run Gemma 4 on an RTX 4090 (24 GB)?

Yes — Gemma 4 runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4 (QAT).

What quantization should I use for Gemma 4?

Q4_K_M is the best balance of quality and VRAM for Gemma 4 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run Gemma 4 with Ollama?

Install Ollama, then run: ollama run gemma4:e2b. This downloads Gemma 4 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.