Gemma 4 26B-A4B
Gemma 4 26B-A4B is Google's MoE variant of the Gemma 4 family (26B total, 4B active), released alongside the dense Gemma 4 lineup in 2026. The 4B active-parameter footprint gives faster decode than the dense 12B/31B siblings at a similar quality tier.
Overview
Gemma 4 26B-A4B is Google's MoE variant of the Gemma 4 family (26B total, 4B active), released alongside the dense Gemma 4 lineup in 2026. The 4B active-parameter footprint gives faster decode than the dense 12B/31B siblings at a similar quality tier.
Strengths
- MoE design decodes faster than dense Gemma 4 siblings at comparable quality
- Gemma Terms of Use permits commercial deployment
- Fits fully in 24-32GB VRAM at Q4_K_M with context headroom
Weaknesses
- 26B total weight footprint (16GB Q4) exceeds most consumer 16GB cards' comfortable margin
- Less real-world adoption data than the dense 12B Gemma 4 variant
Reviewed quality benchmarks
First-party rows were run by RunLocalAI; reviewed community rows are labeled in the data. Every row links to the raw test-run log.
| Benchmark | Quant | Runtime / Hardware | Score | Raw log |
|---|---|---|---|---|
HumanEval+ tested 2026-07-20 | Q4_K_M | ollama-0.32.1-vast5090 rtx-5090 | 93.3/100 | Gist → |
Q4_K_M note:First-party HumanEval+ on a rented Vast.ai RTX 5090 32GB instance via Ollama 0.32.1. Generation via evalplus_openai_generate.py, scoring via evalplus.evaluate (native Linux, no Windows shim needed). No DB credentials were placed on the rented host — results were pulled back and ingested from the operator's machine.
Want to verify? Every row links to its Gist with full stdout and stderr of the run. The runner script is in the public repo (scripts/run-humaneval-plus.ts) — reproducible end-to-end. Browse all coding scores at /benchmarks/coding.
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 16.0 GB | 20 GB |
Get the model
Ollama
One-line install
ollama run gemma4:26b-a4b-it-q4_K_MRead our Ollama review →Hardware that runs this
Cards with enough VRAM for at least one quantization of Gemma 4 26B-A4B.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Gemma 4 26B-A4B?
Can I use Gemma 4 26B-A4B commercially?
What's the context length of Gemma 4 26B-A4B?
How do I install Gemma 4 26B-A4B with Ollama?
Source: Vendor official documentation
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Gemma 4 26B-A4B runs on your specific hardware before committing money.