Gemma 4

Google Gemma family entry for open-weight testing across efficient local, app, and multimodal workflows.

Google · Gemma

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedJune 2026SourcesGoogle Gemma docs, Hugging Face

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Developers evaluating Google-backed open-weight models for efficient local apps, hosted prototypes, and multimodal workflows where supported.

Who should use it

Developers evaluating Google-backed open-weight models for efficient local apps, hosted prototypes, and multimodal workflows where supported.
Builders who want local or self-hosted testing options.

Common workflows

Efficient open models, multimodal apps, local and hosted prototypes
chat workflows
multimodal workflows
local workflows
efficient workflows

Deployment and hardware notes

Smaller variants are practical locally; larger variants need high-memory GPUs or server infrastructure.

License and usage notes

Apache 2.0 / check exact model card. Open weights. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

Open weights model option for Gemma workflows.
Developers evaluating Google-backed open-weight models for efficient local apps, hosted prototypes, and multimodal workflows where supported.
Smaller Gemma variants are practical for local testing; larger variants need more VRAM or unified memory.
Tracked as Frontier 2026 in the OpenSourcesAI model directory.

Limitations

Exact capabilities vary by Gemma variant; verify model size, modality support, and license for the checkpoint you use.
Smaller variants are practical locally; larger variants need high-memory GPUs or server infrastructure.
Context window and limits: Check current Gemma model card.
Verify the exact model card, provider docs, license, and serving support before production use.

Local workflow notes

Smaller Gemma variants are practical for local testing; larger variants need more VRAM or unified memory.

Local runtimes: Ollama, LM Studio, llama.cpp, Transformers

Platforms: Windows, macOS, Linux

Frontier-model verification note

This page is written to stay accurate as of the latest available 2026 public model information. Availability, licenses, context windows, API support, pricing, benchmark standing, and local-serving support can change quickly. Verify the official model card, provider docs, and license before using this model in production or commercial workflows.