Does llama.cpp Support Gemma 4? GGUF Status, Fixes, and What Works

If you are searching for llama.cpp Gemma 4 support, the short answer is yes.

As of April 7, 2026, there are public GGUF pages under ggml-org for:

• Gemma 4 E2B
• Gemma 4 E4B
• Gemma 4 26B A4B
• Gemma 4 31B

And those GGUF pages explicitly recommend running the models with commands like:

llama-server -hf ggml-org/gemma-4-31B-it-GGUF

That is already enough to say llama.cpp supports Gemma 4 in a real, user-facing way.

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Does llama.cpp support Gemma 4? Short answer

Yes. The current public answer is:

• Google's Gemma docs list llama.cpp as an integration path
• ggml-org publishes Gemma 4 GGUF builds
• the GGUF model cards explicitly point you to llama.cpp tooling

So if your question is just compatibility, the answer is no longer ambiguous.

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Which Gemma 4 models work with llama.cpp?

Public GGUF pages currently exist for:

Model	Public GGUF path
Gemma 4 E2B	ggml-org / Gemma 4 E2B GGUF
Gemma 4 E4B	ggml-org / Gemma 4 E4B GGUF
Gemma 4 26B A4B	ggml-org / Gemma 4 26B A4B GGUF
Gemma 4 31B	ggml-org / Gemma 4 31B GGUF

That means llama.cpp Gemma 4 support is not limited to one model size. The full family is represented.

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What "supported" actually means here

This is the helpful distinction:

llama.cpp support for Gemma 4 clearly covers:

• GGUF loading
• local text inference
• local server workflows via llama-server
• command-line inference and automation

That is the core answer most people actually need.

For newer multimodal edges and brand-new release details, the safest move is still to use a fresh llama.cpp build instead of assuming an older binary will understand a newly released architecture perfectly.

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Why a current build matters

Gemma 4 landed with new model-family details, and the llama.cpp project merged Gemma 4-related fixes right after release, including:

• a Gemma 4 parser fix on April 2, 2026
• a Gemma 4 tokenizer fix on April 3, 2026

So while llama.cpp Gemma 4 support is real, you should still think in terms of current build, not stale build.

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Which Gemma 4 model should you run in llama.cpp?

The same practical model-picking rules still apply:

• E2B if you need the smallest footprint
• E4B if you want the stronger small model
• 26B A4B if you want the local sweet spot
• 31B if you want the maximum quality and can afford the memory

If you only want one strong local model in llama.cpp, the easiest recommendation remains 26B A4B.

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When llama.cpp is the right choice

Choose llama.cpp for Gemma 4 if you want:

• CLI control
• a local OpenAI-compatible server
• CPU-first or custom runtime workflows
• precise control over quantization and deployment

Choose LM Studio instead if you mainly want a GUI.

Choose Unsloth instead if you mainly want training or GGUF export workflows.

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FAQ

Does llama.cpp support Gemma 4 today?

Yes. Public GGUF builds exist for the full Gemma 4 family, and the model cards point directly to llama.cpp usage.

Which Gemma 4 models work in llama.cpp?

E2B, E4B, 26B A4B, and 31B all have public GGUF paths.

Should I use an older llama.cpp build?

It is safer to use a current build because Gemma 4-related fixes landed right after release in early April 2026.

Is llama.cpp or LM Studio better for Gemma 4?

Use llama.cpp if you want control and automation. Use LM Studio if you want the easiest GUI-first workflow.

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Official references

• Google Gemma docs: integrations and local paths
• ggml-org Gemma 4 31B GGUF
• ggml-org Gemma 4 26B A4B GGUF
• ggml-org Gemma 4 E4B GGUF
• ggml-org Gemma 4 E2B GGUF
• llama.cpp parser fix for Gemma 4
• llama.cpp tokenizer fix for Gemma 4

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Related guides

• How to run Gemma 4 with llama.cpp
• Gemma 4 26B vs 31B
• Gemma 4 31B VRAM requirements
• Gemma 4 VRAM calculator