---
title: "Google"
description: "Global technology leader developing innovative software, cloud services, and AI solutions across mobile, web, and enterprise platforms"
---

# Google
> Global technology leader developing innovative software, cloud services, and AI solutions across mobile, web, and enterprise platforms

## Articles

### [Bringing Gemma 4 12B to your Laptop: Unlocking Local, Agentic Workflows with Google AI Edge](https://yomu.fyi/post/bringing-gemma-4-12b-to-your-laptop-unlocking-local-agentic-workflows.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Google AI Edge Team
- Published: Jun 3, 2026
- Type: Announcement

Google DeepMind’s Gemma 4 12B is presented as an open model for running agentic, multimodal capabilities locally on everyday laptops through the Google AI Edge stack. The release covers Google AI Edge Gallery for local data analysis and coding, including Python generation, execution, chart rendering, and a reported 3D rendering workflow that can generate dependencies, code, and self-corrections in one turn. Google AI Edge Eloquent adds fully on-device dictation, transcription, and Voice Edit commands for rewriting or translating selected text, while LiteRT-LM’s new serve command exposes the model through a local, OpenAI-compatible endpoint for tools and frameworks. The post says Gemma 4 12B delivers a 60%+ overall quality improvement over prior models for Eloquent’s use case and keeps data on-device, with performance and memory benchmarks provided in the model card.


### [Gemma 4 12B: The Developer Guide](https://yomu.fyi/post/gemma-4-12b-the-developer-guide.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: André Susano Pinto, Andreas Steiner, Karolis Misiunas, Karsten Roth, Michael Tschannen, Omar Sanseviero
- Published: Jun 3, 2026
- Type: Announcement

Gemma 4 12B is a dense multimodal model with a unified, encoder-free architecture, native audio input, and a size intended for local execution on dedicated GPU laptops with 16GB VRAM or unified memory. Instead of separate vision and audio encoders, it uses a 35M-parameter vision embedder that projects raw 48x48 patches and a linear projection for raw 16 kHz audio sliced into 40ms frames; both feed a decoder-only transformer with shared weights. That design reduces multimodal latency and fragmented memory footprints, while allowing downstream adapters such as LoRA or full tuning to update the multimodal token loop in one pass. Capabilities include automatic speech recognition, agentic reasoning, diarization, video understanding, and coding, with examples covering a locally built image-processing app and five minutes of video processed at 1 FPS with audio; LiteRT-LM adds offline macOS apps, sandboxed Python execution, and an OpenAI-compatible local API server with stateless prefix caching.


### [Supercharge your integration workflow with the Google Pay & Wallet Developer MCP server](https://yomu.fyi/post/supercharge-your-integration-workflow-with-the-google-pay-wallet-devel.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Jose Ugia
- Published: May 28, 2026
- Type: Announcement

Google Pay & Wallet Developer MCP server is announced to help AI assistants and IDEs complete Google Pay and Google Wallet API integrations with less context switching. It uses Model Context Protocol to connect tools including Antigravity, Cursor, and Visual Studio Code with current documentation, account details, and integration status. Its search\_documentation tool applies Retrieval-Augmented Generation to return answers and code samples from official developer sites, while other tools validate Wallet pass JWTs or JSON, expose performance metrics and error trends, and manage merchant accounts and Google Pay API integrations. The stated goal is to reduce friction, improve accuracy with current documentation and real account data, and speed troubleshooting from setup through launch. A public guide and tool reference are available, with further capabilities planned based on developer feedback.


### [Blazing fast on-device GenAI with LiteRT-LM](https://yomu.fyi/post/blazing-fast-on-device-genai-with-litert-lm.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Tenghui Zhu, Yu-hui Chen, Ram Iyengar
- Published: May 19, 2026
- Type: Announcement

LiteRT-LM is presented as an on-device runtime for deploying Gemma 4 across Android, iOS, and the web despite constrained memory, limited compute, and varied hardware. It combines LiteRT inference, quantization, XNNPACK and MLDrift kernels, CPU/GPU/NPU backends, transfer-avoiding pipelines, Multi-Token Prediction (MTP), and session management. Reported decode rates reach 52 tokens/sec on Android GPU, 56 on iOS Metal, and up to 76 on WebGPU; MTP provides up to a 2.2x speedup, while a ~2.58GB E2B model uses 607MB of physical memory on Apple mobile CPUs. The runtime also supports save/restore of KV caches, native vision and audio loading, Thinking Mode, constrained decoding, and function calling. New Swift and JavaScript APIs extend the integration surface, including WASM/WebGPU browser inference designed for serverless, secure, privacy-preserving applications.


### [Build Better AI Agents: 5 Developer Tips from the Agent Bake-Off](https://yomu.fyi/post/build-better-ai-agents-5-developer-tips-from-the-agent-bake-off.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Frank Guan, Abraham Gomez
- Published: Apr 14, 2026
- Type: Tutorial

The Google Cloud AI Agent Bake-Off is presented as a test of building autonomous agents under severe time constraints for e-commerce, banking modernization, and startup go-to-market problems. Its five patterns recommend decomposing monolithic prompts into specialized sub-agents under a supervisor, designing modularly because model capabilities can quickly replace custom harnesses, and treating multimodality as native. It also advocates open protocols—including MCP, A2A, UCP, AP2, A2UI, and AG-UI—for agent and tool connections instead of bespoke wrappers. Finally, it separates probabilistic reasoning from execution: strict JSON schemas such as Pydantic validate LLM outputs before deterministic Python functions or SQL perform calculations and writes. The stated conclusion is that reliable production agents depend on rigorous software architecture rather than elaborate prompts or flashy single-shot demos.


### [Boost Training Goodput: How Continuous Checkpointing Optimizes Reliability in Orbax and MaxText](https://yomu.fyi/post/boost-training-goodput-how-continuous-checkpointing-optimizes-reliabil.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Shutong Li, Colin Gaffney
- Published: Mar 31, 2026
- Type: Benchmark

Orbax and MaxText introduce continuous checkpointing to balance training reliability and performance when hardware failures or preemption events interrupt long-running jobs. Fixed checkpoint intervals create a trade-off: infrequent saves risk substantial lost work, while frequent saves can block or bottleneck training, particularly over unstable networks. MaxText enables asynchronous checkpointing that attempts a new save only after the previous background request is fulfilled; its continuous mode overrides checkpoint\_period, and max\_num\_checkpoints\_to\_keep can limit storage use. A llama-3.1-70B benchmark on two v5p-128 slices found smaller P50 checkpoint intervals, but average training step time increased because of more frequent device-to-host transfers; the analysis estimated resource conservation against mean-time-between-failure assumptions. Orbax also supports minimum intervals and customizable preservation policies, while co-locating storage with training is recommended because cross-metro bandwidth can reduce checkpointing speed and reliability.


### [MediaTek NPU and LiteRT: Powering the next generation of on-device AI](https://yomu.fyi/post/mediatek-npu-and-litert-powering-the-next-generation-of-on-device-ai.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Lu Wang, Arian Arfaian, Luke Boyer
- Published: Dec 8, 2025
- Type: Announcement

MediaTek’s diverse NPU hardware and specialized compilation requirements have made on-device AI deployment difficult, especially for sophisticated generative models. The LiteRT NeuroPilot Accelerator, described as a ground-up successor to the TFLite NeuroPilot delegate, provides a unified API over MediaTek NPUs and supports both offline Ahead-of-Time (AOT) and on-device compilation. Its workflow can compile models for target SoCs, package Android assets and runtimes for Google Play’s Play for On-device AI, and invoke NPU inference through LiteRT with GPU or CPU fallback options. A new C++ API and Native Hardware Buffer Interoperability enable zero-copy transfers from AHardwareBuffer and OpenGL/OpenCL buffers, supporting real-time camera and video pipelines. On supported MediaTek chipsets, listed Gemma and Qwen models receive NPU-specific optimizations; the post reports up to 12x CPU and 10x GPU acceleration, including over 1600 tokens/sec prefill and 28 tokens/sec decode for Gemma 3n E2B.


### [Unlocking Peak Performance on Qualcomm NPU with LiteRT](https://yomu.fyi/post/unlocking-peak-performance-on-qualcomm-npu-with-litert.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Lu Wang, Weiyi Wang, Andrew Zhang
- Published: Nov 24, 2025
- Type: Announcement

Google introduces the LiteRT Qualcomm AI Engine Direct (QNN) Accelerator, developed with Qualcomm to replace the previous TFLite QNN delegate and simplify NPU deployment across supported Qualcomm SoCs. It integrates vendor compilers and runtimes behind a unified workflow, supports ahead-of-time and on-device compilation, and can fall back to GPU or CPU when needed. Across 72 canonical vision, audio, and NLP models, the accelerator supports 90 LiteRT ops, enables full NPU delegation for 64, and reports up to 100x CPU and 10x GPU speedups. On Snapdragon 8 Elite Gen 5, FastVLM-0.5B uses int8 weights, int16 activations, and specialized transformer kernels to reach 0.12 second time-to-first-token, over 11,000 prefill tokens per second, and over 100 decode tokens per second.


### [Unleashing new AI capabilities for popular frameworks in Firebase Studio](https://yomu.fyi/post/unleashing-new-ai-capabilities-for-popular-frameworks-in-firebase-stud.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Jeanine Banks, Vikas Anand
- Published: Jul 23, 2025
- Type: Announcement

Firebase Studio announced updates to its cloud-based AI workspace that combine Gemini with Agent modes, Model Context Protocol support, and Gemini CLI across Flutter, Angular, React, Next.js, and Web projects. AI-optimized templates default to autonomous Agent mode and include airules.md files that give Gemini framework-specific guidance on coding standards, error handling, dependencies, and development practices. Developers can prompt Gemini to recommend Firebase backend services, import libraries or packages, update code, and explain additional setup steps. Workspace forking preserves an identical copy and the original’s entire agent chat history, supporting backups, experimentation, debugging, and collaboration, while enhanced prompts and a 100MB project upload limit broaden the workflow. The editor’s note says Firebase Studio will sunset on March 22, 2027, with lessons from its preview moving to Google AI Studio and Google Antigravity.


[Newer posts](https://yomu.fyi/company/google/page/3.md)