Models & Labs
Google DeepMind launches Gemini 3.5 Live Translate
Google DeepMindhigh confidence
Why it matters
- →Enables real-time, fluid translation across 70+ languages, enhancing global communication.
- →Integrates into widely used platforms like Google Meet and Google Translate, broadening accessibility.
- →Supports developers in building voice translation apps with ease, fostering innovation in multilingual communication.
Google DeepMind has introduced Gemini 3.5 Live Translate, a new model for real-time speech-to-speech translation. This model supports over 70 languages and provides continuous translation that mirrors the speaker's natural intonation and pacing. It is available for developers through the Gemini Live API and is being integrated into Google Meet and the Google Translate app. The technology aims to improve multilingual communication by offering seamless translations in real-time, enhancing user experience across various platforms.
Read originalMore in Models & Labs
Models & Labsmodels
llama.cpp b10955 release addresses heap corruption
The latest release of llama.cpp, b10955, tackles a critical issue of heap corruption by disabling the ggml-cpu precompiled header and fixing CACHE_LINE_SIZE ambiguity. This update ensures consistent CACHE_LINE_SIZE values across C++ kernels and C work-buffer sizing code, preventing heap-buffer-overflow and subsequent crashes. By restoring the natural include order and removing the std::hardware_destructive_interference_size branch, the update makes the value deterministic and include-order independent. This release is a technical fix that stabilizes the runtime environment for developers using llama.cpp.
llama.cpp Releases
Models & Labsmodels
llama.cpp b10956 release enhances SYCL backend
The latest llama.cpp release, b10956, introduces significant improvements to the SYCL backend, particularly for handling large k values in TOP_K operations. By implementing a radix select method, the update allows for efficient GPU-resident processing, avoiding previous limitations that forced operations to fall back to the CPU. This change enhances performance, especially in scenarios requiring large k values, such as qwen4exp's sparse-attention indexer. The update ensures that operations are more efficient and scalable, providing a notable boost in processing speed without regressing any measured shapes.