Updated September 8, 2026. Quail 4 is the final stable release in the Android Studio Quail series, published September 1.
Android Studio Quail 4 brings Gemma 4 directly into the IDE through an integrated runtime. The model can assist Android development on the local computer without requiring an external provider or an internet connection for its core operations. This matters to teams working with confidential code, travelling without reliable connectivity or avoiding per-request cloud API charges.
The word “local” needs a precise definition. Gemma weights and inference run on the selected workstation. It does not prove that every other IDE feature is offline or that plugins, synchronization, telemetry and cloud services are disabled. The complete configuration still needs review.
How to enable local Gemma 4 in Android Studio Quail 4
The official Quail 4 release notes provide a direct path: Settings, Tools, AI, Model Providers and Gemma. Download and execution use the included runtime, so this integrated mode does not require LM Studio or Ollama.
The IDE release is Android Studio 2026.1.4. Its compatibility table supports Android Gradle Plugin versions 7.1 through 9.4. Before updating a company project, verify AGP, Gradle, JDK and plugins rather than blaming a toolchain failure on the AI model.
Once downloaded, the local provider can power compatible AI features. Google describes work such as feature design, refactoring and debugging. The practical benefit is iterating over a repository without transmitting each prompt and code fragment to a remote inference endpoint.
RAM and storage requirements
The current local-model guide recommends two main configurations. Gemma E4B requires 12 GB of total RAM and around 4 GB of storage. Gemma 26B MoE targets more powerful machines and requires 24 GB of total RAM plus 17 GB of storage.
The stated RAM includes Android Studio and Gemma, but real projects also run Gradle daemons, emulators, browsers, Docker and profiling tools. Treating 12 or 24 GB as the total machine’s comfortable capacity may lead to swapping and much worse latency. A workstation needs headroom above the table value.
| Model | Stated total RAM | Stated storage | Profile |
|---|---|---|---|
| Gemma E4B | 12 GB | 4 GB | More accessible machines |
| Gemma 26B MoE | 24 GB | 17 GB | Higher-end workstations |
GPU support, available acceleration and shared memory affect the experience. Official requirements indicate whether a model should run; they do not promise equal speed across Windows, macOS and Linux. The useful test measures response time while the build and emulator are active.
What the bundled Android Skills do
Quail 4 includes skills curated by the Android team to give the agent current platform knowledge about APIs, migrations and recommended practices. A general model may suggest deprecated classes or old patterns; a maintained skill adds procedural context tied to the current toolchain.
Skills do not make every output correct. A model can still edit unnecessary files, invent an API or suggest an incompatible migration. Diffs, tests, lint and builds remain the decisive checks. Our GPT-6 Astra vs GPT-5.6 Sol comparison illustrates the same principle: a stronger model does not remove system-level verification.
The Quail series also introduces Firebase work in Agent Mode, parallel conversations, testing tools and profiler integrations. These should not all be attributed to local Gemma. Some may depend on Gemini, enterprise accounts or external services.
What privacy does local execution provide?
With the integrated Gemma provider, prompts, supplied code and inference can stay on the device. That is useful for private prototypes and policies that prohibit sending repositories to third-party model services. It is not by itself a universal compliance certification.
Teams should inspect crash reporting, updates, plugins, remote version control, Firebase, Gemini accounts and data-collection settings. Switching providers inside the same IDE changes the data path. The active provider should always be visible before sensitive material is submitted.
Google’s official Gemma 4 launch describes an open-weight family with local coding and tool-use capabilities. Open weights provide deployment control, but license compliance, dependency security and workstation protection remain organizational responsibilities.
Limits compared with cloud Gemini
Google cautions that local models may be less accurate, slower and compatible with fewer features than Gemini in Android Studio. A smaller model runs within finite hardware and may understand large repositories or complex long-context requests less reliably.
The choice need not be ideological. Local Gemma can handle straightforward refactors, explanations and offline work, while a cloud model tackles difficult planning or broader context. Work can be routed according to code sensitivity, task value and risk rather than sending everything through one provider.
Our article on Qwen 3.8-27B for local use provides another open generative-model reference outside Google’s integrated path. Quail 4 reduces setup work because the Gemma download and runtime sit directly inside the IDE.
Is Android Studio Quail 4 worth installing?
Android Studio Quail 4 is compelling for Android developers who want a ready local agent in a stable release rather than a Canary build. Before company-wide adoption, test a copy of the repository, verify AGP compatibility, measure memory during builds and emulation, and identify which agent actions still invoke cloud services.
Its most concrete advantage is not that Gemma 4 replaces every remote model. It restores choice: local execution for privacy, offline work and predictable inference cost; cloud models for tasks requiring greater capability. That makes Android Studio Quail 4 both a fresh search topic and a tool worth evaluating with production-like tests.
