Gemma 4 26B-A4B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated March 31, 2026

Model libraryGemma 4 → Gemma 4 26B-A4B

Gemma 4's Mixture-of-Experts variant: 26B total parameters but only 4B active per forward pass. This gives 12B-class quality at 4B-class inference speed. Supports text, image, and audio. Needs ~16–18 GB at Q4_K_M — fits in an RTX 3090 or 4090. Excellent for developers who need a step above the E4B without occupying the full GPU.

Gemma 4 26B-A4B needs about 16 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters26 Billion (4B active per token)
Context window256,000
ArchitectureMixture-of-Experts + Multimodal Encoder (image + audio)
ProviderGoogle
LicenceApache 2.0
Specified atQ4_K_M
System RAM32 GB
Record updated2026-03-31

Licence

Apache-2.0commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K8.5 GB9.3 GB~195 tok/s (est.)Fits comfortably
Q3_K_M11.1 GB11.9 GB~178 tok/s (est.)Fits comfortably
Q4_K_M15.7 GB16.5 GB~152 tok/s (est.)Fits comfortably
Q5_K_M18.4 GB19.2 GB~140 tok/s (est.)Fits comfortably
Q6_K21.3 GB22.1 GB~130 tok/s (est.)Tight fit
Q8_027.6 GB28.4 GB~17 tok/s (est.)Offloads to system RAM (slow)
F1652.0 GB52.8 GB~11 tok/s (est.)Offloads to system RAM (slow)

Want the memory numbers alone, at every quantization level and your own context length? Use the Gemma 4 26B-A4B VRAM calculator.

Buy This HardwareAMD Radeon RX 7900 XT 20GB — 20 GB VRAM · 315 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs Gemma 4 26B-A4B is the AMD Radeon RX 9060 XT 16GB (16 GB).

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AMD Radeon RX 9060 XT 16GB
16 GB VRAM · 160 W board power
2026 prices are volatile — check the current listing.
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How to Run Gemma 4 26B-A4B

Install Ollama, then run:

ollama run gemma4:26b-a4b

Weights on Hugging Face: google/gemma-4-26B-A4B-it.

Published Benchmark Scores

Quality scores as published by the model's authors or an independent evaluator — not throughput, and not measured by us.

BenchmarkScoreProvenance
MMLU87.5 / 100 %reported
HumanEval83.7 / 100 %reported
MATH74.2 / 100 %reported

Best for: reasoning, multimodal, coding, long context, speed efficient.

Can I Run Gemma 4 26B-A4B on My GPU?

Other Gemma 4 Sizes

Gemma 4 26B-A4B — Frequently Asked Questions

How much VRAM does Gemma 4 26B-A4B need?
About 16 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Gemma 4 26B-A4B run on an RTX 4090 (24 GB)?
Yes. Gemma 4 26B-A4B needs about 16 GB at Q4_K_M, inside a 24 GB card, at an estimated 152 tokens/sec.
How do I run Gemma 4 26B-A4B locally?
Install Ollama and run `ollama run gemma4:26b-a4b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Gemma 4 come in?
Gemma 4 E2B (4 GB), Gemma 4 E4B (6 GB), Gemma 4 26B-A4B (16 GB), Gemma 4 31B (20 GB), Gemma 4 12B (Unified) (8 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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