Family Gemma · 26B parameters

Gemma 4 26B-A4B MoE

MoE variant of Gemma 4. 26B/4B active. Full multimodal (text+image+audio).

🇺🇸 Google·License Apache 2.0·Context 125k tokens·Output April 2026·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • Text + image + audio in a 26B MoE
  • 128k context
  • Apache 2.0
  • Strong reasoning
Limitations to know
  • —16 GB VRAM Q4: a 24 GB GPU or 32 GB Mac recommended
Architecture
MoE · 26B · Gemma 4 · text+image+audio multimodal · 128k context
Training
Google Gemma 4 MoE 26B — natively multimodal with audio, vision, and text.
Ideal for
Multimodal efficientMid-size MoE

04Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$ollama run gemma4:26b
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
16 GB
Q5_K_M
Good quality/size compromise
19 GB
Q8_0
Nearly indistinguishable from FP16
28 GB
FP16
Full precision — server use
52 GB
Fallback CPU · If you don't have a GPU, allow 28 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for Gemma 4 26B-A4B MoE?

To run Gemma 4 26B-A4B MoE locally with Q4 quantization, you need about 16 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.

Current offer: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395)
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Why this choice? Our complete guide on GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →

Affiliate links — commission possible at no extra cost to you; independent recommendations. As an Amazon Associate, BestLLMfor earns from qualifying purchases.

On the go: Gemma 4 26B-A4B MoE also runs on a RTX laptop PC (16 GB of VRAM) →

This model in your private ChatGPT, without the cloud

Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.

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03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~8t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~22t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~60t/s
RTX 4090, M4 Max, Radeon 7900