@realfxw: Local Large Models Get a Major Upgrade! Antigravity SDK Officially Announces Support for Local Model Execution – Developers Can Now Run Open-Source Models Like Gemma 4 Directly on Local Devices via Antigravity Harness, Achieving Zero Token Bills and Complete Code Privacy Isolation…
Summary
Antigravity SDK officially supports local model execution, like Gemma 4, achieving zero Token costs and complete code privacy, using a hybrid architecture to enhance development efficiency.
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Introducing Support for Local AI Models in the Antigravity SDK
Google announces that the Antigravity SDK now supports local AI model workflows, featuring Gemma 4 26B A4B via LiteRT, enabling offline agentic capabilities with benefits like privacy, cost efficiency, and hybrid orchestration.
@ai_xiaomu: Here comes a full-featured multimodal local model that runs on a MacBook with 16GB: 1. Download LM Studio; 2. Search for Gemma 4 12B and install it; 3. Ask Codex to configure the local API parameters for you; 4. Then enjoy the freedom of tokens.
Guides users on running the Gemma 4 12B multimodal local model on a MacBook with 16GB RAM using LM Studio and Codex, enabling free token usage.
@googledevs: Gemma 4 is now running locally on-device in the @Antigravity SDK. Build fully local or hybrid multi-agent workflows tha…
Gemma 4 is now available for local on-device execution via the Antigravity SDK, enabling fully local or hybrid multi-agent workflows with enhanced data privacy and zero API costs.
@servasyy_ai: This is absolutely insane! The local LLM landscape has just been turned upside down! Unsloth just released the latest Dynamic V3.1 quantized version of DeepSeek-V4.1-Flash, and now you can get it flying with just 64GB of RAM! Running a beast this size used to require racks of expensive servers, but this release smashes that barrier: …
Unsloth has released a Dynamic V3.1 quantized GGUF version of DeepSeek-V4.1-Flash-1017, enabling the model to run locally on devices with just 64GB of RAM. It claims up to 3× faster inference, support for a 1-million-token context window, and across-the-board leadership in benchmarks against comparable quantized models.
@xueyu1125: Running local large models requires at least 50 tokens/s for usability. Here are API output speeds for top models (Gemini Flash 300+) DeepSeek V4 Flash: 99.5 tokens/s GPT-5.6 Sol: 68.1 to…
Discusses the token speed requirements for running local large models and compares API output speeds of multiple top AI models.