Gemma 4 26B (MoE): VRAM Requirements & Local Setup Guide 2026

Google's efficient MoE model with 25.2B total parameters and only 4B active per token — the most VRAM-efficient large model in its class.

Provider: Google
Architecture: Mixture-of-Experts (MoE)
Total Parameters: 25.2B
Active Params/Token: 4B
Context Window: 256K tokens
License: open
Modalities: text, image
HuggingFace: google/gemma-4-26B-A4B-it

VRAM Requirements by Quantization & Context Length

All values in gigabytes (GB). Calculated using the KV-cache formula with model-specific head fractions. “–” means the context length exceeds this model’s maximum context window. Lower quantization = less VRAM but slightly reduced output quality.

Quantization	4K ctx	8K ctx	32K ctx	128K ctx
Q4_K_M	15.7 GB	15.8 GB	16.8 GB	20.5 GB
Q5_K_M	18.1 GB	18.2 GB	19.2 GB	22.9 GB
Q6_K	20.6 GB	20.8 GB	21.7 GB	25.5 GB
Q8_0	26.3 GB	26.4 GB	27.4 GB	31.1 GB
FP16	48.3 GB	48.4 GB	49.4 GB	53.1 GB

Weights = 25.2B params × GGUF bits per weight (Q4_K_M ≈ 4.9) — all experts are loaded; plus runtime overhead; plus fp16 KV cache from config.json: 8 KV heads × 256 dims (25 of 30 layers capped at a 1,024-token window) — ~240 KB per token at short context. Same engine as the Can I Run LLM calculator.

Best GPU for Gemma 4 26B (MoE) by Budget

Recommendations assume Q4_K_M quantization at 4K context unless stated otherwise. Higher-end quantizations or longer context windows require more VRAM — consult the table above.

Under $500 (Entry GPU) RTX 3060 12GB
$500–$1,000 (Mid-Range GPU) RTX 4070 (12GB)
$1,000–$3,000 (High-End GPU) RTX 4090
$3,000+ (Workstation / Cloud) A100 80GB

Benchmark Scores

Scores reported by Google or verified third-party evaluations. Higher is better for all benchmarks except where noted.

Benchmark	Score
MMLU-Pro	82.6
GPQA Diamond	82.3
LiveCodeBench	77.1
AIME 2024–25 (Epoch AI)	82.2

How to Run Gemma 4 26B (MoE) Locally

Before downloading, verify your GPU has sufficient VRAM using the table above. Insufficient VRAM will cause the model to fall back to CPU offloading, which is significantly slower.

Option A — Ollama (Recommended for Beginners)

Ollama is the easiest way to run Gemma 4 26B (MoE) locally. It handles model downloading, GGUF quantization selection, and serving automatically.

Install Ollama from ollama.com
Pull the model:
```
ollama pull gemma4:26b
```
Run interactively:
```
ollama run gemma4:26b
```

Option B — HuggingFace Hub

For more control over quantization format, download directly from HuggingFace:

pip install huggingface_hub
huggingface-cli download google/gemma-4-26B-A4B-it

Hardware note: Check the VRAM table above before downloading. Running Gemma 4 26B (MoE) requires substantial GPU memory — use Q4_K_M quantization for the lowest VRAM footprint while retaining most model quality.

Strengths and Use Cases

Extremely lightweight at inference time. 4B active params means it runs on consumer GPUs despite its 25.2B total parameter count. Multimodal.

When choosing a local LLM, Gemma 4 26B (MoE) is worth considering if your workload aligns with its design goals. As a Mixture-of-Experts (MoE) architecture from Google, it offers a distinct trade-off between compute efficiency and capability. With MoE architecture, only a fraction of parameters are active at inference time, dramatically reducing VRAM requirements compared to a dense model of equivalent total size. Whether you are building a local AI pipeline, experimenting with self-hosted chat, or running automated workflows, understanding this model's hardware envelope helps you plan infrastructure realistically.

For deployment, start with the Q4_K_M quantization unless you have headroom for higher precision. Q5_K_M and Q6_K offer improved output quality at the cost of additional VRAM. FP16 is generally only practical on high-VRAM workstation GPUs or cloud instances. Always verify context length requirements before selecting a quantization — longer context windows multiply KV-cache memory consumption significantly, as shown in the VRAM table above.

Ready to Check Your Hardware?

Use our interactive calculator to see exactly whether your GPU can run Gemma 4 26B (MoE) — and at what quantization level.

Check if your GPU can run this model → Compare on LLM Leaderboard

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