Open-Weight Models on Vertex AI: Gemma 4, Llama 4, and When to Prefer Them Over Gemini

You don't need to leave Vertex AI to use an open-weight model -- Google already integrated Gemma, Llama, and Mistral into the same Model Garden where Gemini lives.

The 2026 open-weight landscape

Six major labs now deploy competitive open-weight models: Google (Gemma 4), Alibaba (Qwen 3.6 Plus), Meta (Llama 4), Mistral (Small 4), OpenAI (gpt-oss-120b), and Zhipu AI (GLM-5). On most benchmarks that matter for production workloads, the open-weight ecosystem has already closed or substantially narrowed the gap with closed frontier models.

Gemma 4: from 2B for mobile to a 270B flagship

Gemma 4 is the open-weight family most specifically designed for on-device and edge deployment, with a lineup ranging from a 2B variant suitable for mobile devices up to a 270B flagship -- within the same Vertex AI, you can choose the exact point on the line that matches your specific resource constraint.

Licenses matter as much as the benchmark

In 2026, the families with the most permissive licenses include Mistral (much of its line under Apache 2.0), DeepSeek and Microsoft Phi (both under MIT), and many Alibaba Qwen models (Apache 2.0) -- before committing to an open-weight model for commercial use, that specific version's license can matter as much as its benchmark performance.

How they're deployed inside Vertex AI

There are verified deployment configurations for close to 400 Hugging Face text-generation models -- including google/gemma-7b-it, meta-llama/Llama-2-7b-chat-hf, and mistralai/Mistral-7B-v0.1 -- directly on Vertex AI, meaning deployment doesn't require leaving the GCP ecosystem or managing your own inference infrastructure.

When to prefer open-weight over Gemini within the same platform

It makes sense when you need full control over the weights (deep fine-tuning, model auditing, data sovereignty requirements demanding that the model itself, not just the data, stay under verifiable control), or when a specific open-weight model outperforms Gemini on your own benchmark for a niche task. For most general use cases, Gemini remains the lowest-friction option within Vertex AI -- the open-weight option is the answer to a specific need, not the default starting point.