Gemma 4 26B-A4B MoE

Overview

Google's MoE variant of Gemma 4 with 26B total / 4B active params and full text+image+audio multimodality. The smallest open model with native audio understanding at this quality.

When to pick this model

• Multimodal apps that need text, image, and audio in one model
• Voice-driven assistants and audio analysis pipelines
• Long-context reasoning over mixed-media inputs (128k)
• On-prem deployments where Google's tooling integrates cleanly
• Replacing three separate models with one

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	16 GB
Q5_K_M	19 GB
Q8_0	28 GB
FP16 (no quantization)	52 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Gemma 4 26B-A4B MoE needs a 16 GB card at Q4_K_M (16 GB). Stepping up to Q8_0 nearly doubles the footprint to 28 GB, and unquantized FP16 weights take 52 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Gemma 4 26B-A4B MoE needs roughly 28 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 8 tokens/sec on entry-level GPUs, on the order of 22 tokens/sec on a mid-range card, and up to 60 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Gemma 4 26B-A4B MoE to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

GPU memory	Example cards	Best fit for Gemma 4 26B-A4B MoE
8 GB	RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB	Does not fit — needs 16 GB at Q4_K_M
12 GB	RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop	Does not fit — needs 16 GB at Q4_K_M
16 GB	RTX 5080, RTX 4080 Super, Radeon RX 9070 XT	Q4_K_M (16 GB used)
24 GB	RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop	Q5_K_M (19 GB used)
32 GB	RTX 5090	Q8_0 (28 GB used)

Which GPU should you buy to run Gemma 4 26B-A4B MoE?

To run Gemma 4 26B-A4B MoE locally at Q4, you need ~16 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).

Strengths

• Unified text, image, and audio in 26B/4B-active MoE
• 128k context
• Strong reasoning relative to size
• Backed by Google's training infrastructure and corpus
• 4B active params keep inference cheap

Limitations

• Around 16 GB VRAM in Q4
• Gated on Hugging Face with click-through agreement
• Gemma license has more restrictions than Apache or MIT

Typical workloads

In our catalog grid, Gemma 4 26B-A4B MoE is filed under Efficient Multimodal, Mid-Size MoE — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents; audio understanding; multilingual workloads.

The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the Gemma license — commercial use is generally possible but read the specific terms before embedding it in a product.

Architecture & training

Architecture: MoE · 26B · Gemma 4 · multimodal text+image+audio · 128k context

Training: Google Gemma 4 MoE 26B — natively multimodal with audio, vision, and text.

Quick start

ollama run gemma4:26b-moe

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does Gemma 4 26B-A4B MoE need?

At the recommended Q4_K_M quantization, Gemma 4 26B-A4B MoE needs about 16 GB of VRAM. Q8_0 takes 28 GB, and unquantized FP16 weights take 52 GB.

Can Gemma 4 26B-A4B MoE run without a GPU?

Yes — with roughly 28 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Gemma 4 26B-A4B MoE support?

Gemma 4 26B-A4B MoE supports a 125k-token context window (128,000 tokens).

Can I use Gemma 4 26B-A4B MoE commercially?

Gemma 4 26B-A4B MoE ships under the Gemma license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.

How fast is Gemma 4 26B-A4B MoE on consumer hardware?

Our compatibility engine estimates on the order of 22 tokens/sec on a mid-range GPU and up to 60 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Gemma 4 26B-A4B MoE should I download first?

Start with Q4_K_M (16 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.