Google Antigravity SDK Local AI Models: LiteRT & Gemma 4 Production Guide
Google announced on September 23, 2026 that the Antigravity SDK supports local agent workflows, with initial optimized support for Gemma 4 26B A4B through LiteRT and support for OpenAI-compatible local servers such as Ollama, LM Studio and vLLM through LocalOpenAIAgentConfig. Local execution can keep code and prompts on the device, avoid cloud API token charges for local work, continue offline, and participate in hybrid workflows where a cloud model plans while local models perform bounded tasks. Google recommends more than 24 GB of VRAM or unified memory for its Gemma 4 26B Antigravity example. Google's separate Gemma 4 12B guidance targets laptops with 16 GB VRAM or unified memory and a LiteRT-LM OpenAI-compatible server. Treat those as distinct deployment examples rather than universal hardware promises. Production use still needs scoped tools, tests, review, performance measurement and an explicit data boundary.
A team wants agentic coding without sending proprietary source code to a cloud model for every task
Cloud token cost or rate limits make repetitive local coding or review expensive
An agent workflow needs to keep operating when internet connectivity is unavailable
A team assumes any local model will work well on any laptop without measuring memory or latency
A hybrid workflow sends sensitive source code to a cloud planner even though the intended privacy boundary was local
Possible causes
The Antigravity SDK can now orchestrate local model execution instead of requiring every agent step to use a cloud model
LiteRT and LiteRT-LM provide local inference paths for supported Gemma models
LocalOpenAIAgentConfig can connect to compatible local servers such as Ollama, LM Studio and vLLM
Model size and runtime choice materially affect memory requirements, latency and usable context
Hybrid orchestration only preserves privacy when the cloud/local data boundary is deliberately designed
HOW TO FIX IT
Work from the safest step to the harder repair.
Step 1. Classify which tasks genuinely benefit from local privacy or offline execution and which still require an approved cloud model or external service.
Step 2. Match the model to the hardware. Google's September 23 example recommends more than 24 GB of VRAM or unified memory for Gemma 4 26B A4B, while its June Gemma 4 12B guidance targets a smaller laptop-class option with 16 GB.
Step 3. If the team already runs Ollama, LM Studio, vLLM or another OpenAI-compatible local server, evaluate Antigravity's LocalOpenAIAgentConfig path before adding another serving stack.
Step 4. Define the privacy boundary before building a hybrid workflow. Specify exactly what metadata may leave the machine and verify that source code stays local when that is the requirement.
Step 5. Benchmark representative tasks with cold and warm measurements for latency, memory, tool-call success and quality after review.
Step 6. Keep agent permissions scoped to the files and actions required for the task, and keep production credentials out of local-model prompts and generated artifacts.
Step 7. Run the normal engineering gates after agent changes: typecheck, tests, security checks where relevant, diff review and a reversible deployment.
Step 8. Compare local and cloud paths on total cost per successful task, including hardware, developer wait time, maintenance, review effort and any remaining cloud usage.
Step 9. Recheck Google's Antigravity SDK and LiteRT guidance before standardizing a deployment because local model support is new and recommendations can change quickly.
Need the actual code? Go to GenesisCodeDoctor.com to search the Code Store or request code for the exact platform, error, and repair you are working on.
1. Must source code or private data remain on-device? If yes, start with a fully local path and verify no remote component crosses that boundary.
2. Can the target machine support the chosen model with acceptable memory and latency? If no, test a smaller model or keep the task on an approved cloud path.
3. Does the team already operate an OpenAI-compatible local server? If yes, evaluate LocalOpenAIAgentConfig before duplicating serving infrastructure.
4. Does task quality meet the engineering acceptance bar after tests and review? If no, change the model, prompts, tools or orchestration before scaling.
5. Would a hybrid planner materially improve the task? If yes, define and test the data boundary before enabling it.
REPAIR FLOW
A visual path from symptom to verified production.
Observe→
Protect→
Isolate→
Repair→
Test→
Publish→
Verify live
BEFORE YOU PASTE CODE
Protect the working site first.
Keep local inference endpoints limited to the intended machine or trusted network scope.
Keep secrets out of prompts, generated files, logs and model caches unless an approved workflow requires them.
Use scoped workspaces and tool permissions for agent actions.
Record the model, runtime, hardware profile and test results used for production evaluation.
STOP AND GET HELP WHEN
Do not turn a repair into a larger outage.
Do not claim local execution eliminates all cost; it can remove cloud API token charges for local work while still consuming hardware, power and engineering time.
Do not promise Gemma 4 26B will run acceptably on every machine; Google's published example recommends more than 24 GB of VRAM or unified memory.
Do not represent Google's hybrid demo as proof that every hybrid agent keeps source code local; privacy depends on the actual data boundary.
Do not treat an OpenAI-compatible local API as automatically secure merely because it is local.
Do not bypass tests, review or deployment controls because the model runs on-device.
HOW GENESIS HANDLES IT
Diagnose the exact failure before choosing a repair.
Genesis separates the visible symptom from the underlying technical cause. Run the supported diagnostic first, review the evidence, and then use a matching repair only when the failure is actually verified.
Platform scope
Google Antigravity SDK · Gemma 4 · LiteRT · LiteRT-LM · Ollama · LM Studio · vLLM · local AI agents
Category
Local AI · Agentic development · Privacy & cost control
Last updated
2026-09-24
REPAIR PROFILE
Know the complexity before you edit.
Difficulty
Intermediate
Change risk
Medium
These labels describe implementation complexity and blast radius, not a guaranteed repair time.
AUTHORITATIVE SOURCES
Verify time-sensitive platform details at the source.
Can Google Antigravity run AI agents completely offline?
Google says the Antigravity SDK now supports local workflows that can run agentic assistance completely offline when the model and required tools are local.
How much memory does Gemma 4 need for Antigravity?
Google recommends more than 24 GB of VRAM or unified memory for its Gemma 4 26B A4B Antigravity example. Its separate Gemma 4 12B laptop guidance targets systems with 16 GB VRAM or unified memory.
Does Antigravity require LiteRT for local models?
No. Google highlights optimized LiteRT support for Gemma 4 and also says LocalOpenAIAgentConfig can connect to OpenAI-compatible local servers such as Ollama, LM Studio and vLLM.
Is a local AI agent automatically private?
No. Local inference can keep prompts and code on the device, but privacy also depends on tools, telemetry, remote resources and any hybrid cloud planner.
Does local AI mean there are no costs?
It can avoid cloud API token charges for work performed locally, but hardware, electricity, maintenance, latency and review still contribute to total cost.
Start with a free diagnostic. If Genesis verifies a problem and a compatible treatment exists, continue to the matching Code Store product or repair path. If you cannot find the exact code you need, request it at GenesisCodeDoctor.com rather than forcing a generic snippet into the wrong platform.
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