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LiteRT

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Headlines · 7

  1. Google adds local-model support to Antigravity SDK

    InfoQ reported that Google added local-model support to the Antigravity SDK, allowing developers to run agent workflows on their machines while using cloud models for higher-level tasks. In Google's hybrid Architect-Builder pattern, the Architect plans in the cloud using filenames and task descriptions without accessing source code, while local Builders reproduce vulnerabilities, propose fixes, review patches and run regression tests. In a vendor demonstration involving three vulnerable Python modules, Google reported that 95 of roughly 3,400 tokens were used for cloud planning and 97.2% were consumed locally. A community participant questioned whether token share alone shows whether local models run faster or more cheaply than cloud models.

    InfoQ 中文 AI&大模型 · 🔥 0
  2. Google and Meta Open-Source TorchTPU for PyTorch on TPUs

    Google and Meta’s PyTorch team released TorchTPU as an open-source native backend for running PyTorch workloads on Google Cloud TPUs. It supports standard PyTorch APIs, eager-mode debugging and torch.compile optimization, with integrations spanning Hugging Face Transformers and Diffusers, vLLM, SGLang, llm-d, TorchTitan and LiteRT. Google says its training setup was validated on up to 8,000 Ironwood TPUs and achieved as much as 52% model FLOPS utilization with Qwen3-8B; these figures are vendor claims.

    Hugging Face · 🔥 0
  3. Arm brings visual graph debugging to edge compilers with Model Explorer

    Google Developers Blog says Arm built open-source adapters that extend Google AI Edge Model Explorer to custom compiler representations including TOSA, VGF, and PTE. The integrations can expose unintended operators, compare CPU and NPU-delegated graphs side by side, and overlay cycle-level profiling traces onto execution graphs. Arm said it used these workflows to find an extra TRANSPOSE regression and identify a TRANSPOSE node as the main latency bottleneck in an Ethos-U test.

    Google Developers Blog · 🔥 0
  4. Google Releases LiteRT Tensor API for Native Edge Model Authoring

    Google announced the LiteRT Tensor API, a C++ authoring layer that lets developers construct computation graphs operation by operator for on-device AI. Google says the same graph can run through LiteRT on CPUs, ML Drift-powered GPUs and NPUs, or directly through XNNPACK on CPU, with reference implementations including SAM 2 and Gemma models.

    Google Developers Blog · 🔥 0
  5. TensorFlow appears on GitHub Trending

    TensorFlow is an end-to-end open-source machine learning platform originally developed by researchers and engineers on the Machine Intelligence team at Google Brain to advance machine learning and neural network research. It offers stable Python and C++ APIs plus an ecosystem of tools, libraries and community resources, with installation via pip (including CUDA GPU support), Docker containers or source builds, along with a CPU-only tensorflow-cpu package and tf-nightly builds. The project is licensed under Apache License 2.0 and showed up in the GitHub Trending daily list.

    GitHub Trending（每日） · 🔥 0
  6. Google open-sources ML Drift GPU inference engine for on-device AI

    Google's AI Edge team announced on October 8 the open-source release of ML Drift, an Apache 2.0-licensed, cross-platform GPU compute engine built for on-device AI/ML inference. The engine abstracts low-level on-device GPU APIs including OpenGL ES, OpenCL, Metal and WebGPU, and serves both as the core GPU acceleration engine inside LiteRT and as a standalone library. Google says ML Drift already runs in production across Chrome, YouTube Shorts, Photos and Meet; it reports up to 12% lower memory overhead than other frameworks in Gemma benchmarks and up to 40% lower average frame latency for Shorts segmentation effects, both vendor claims. Desktop previews on Windows and Linux run through Dawn, Chromium's WebGPU implementation.

    Google Developers Blog · 🔥 0
  7. Google releases EmbeddingGemma 2 for on-device multimodal retrieval

    Google released EmbeddingGemma 2, a 740M-parameter model licensed under Apache 2.0 that maps text, code, images, video, and audio into a shared 768-dimensional embedding space. Based on Gemma 4, it has a modular design with a 270M-parameter text component and optional 170M-parameter vision and 300M-parameter audio encoders. It supports an 8K-token context window and Matryoshka Representation Learning, allowing embeddings to be truncated to reduce storage. The reports say models are available through Hugging Face and LiteRT; arXiv metadata lists the accompanying paper separately.

    Google Developers Blog · 🔥 3

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