Gemma 4
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Headlines · 15
- Google’s Suncatcher satellite runs Gemma 4 in orbit
Google’s Project Suncatcher prototype satellite completed roughly three weeks of in-orbit commissioning and began communicating with Earth, according to Google and multiple news reports. Ground engineers prompted the preloaded Gemma 4 model to create a space-themed haiku, while the satellite also handled limerick and telemetry prompts. Google said its TPUs can run for only about 20 minutes per session because of power constraints, and it plans further tests under thermal stress and radiation exposure.
TechCrunch AI · 🔥 29 - Calibration-Free Top-k Selection Reduces MoE Expert Reads
A paper by Geunhyoung Kim and Sung Ju Hwang proposes top-k channel selection for Mixture-of-Experts decoding, avoiding the offline calibration data used by threshold-based selectors. The authors report that top-k matched or exceeded threshold variants in 40 of 43 comparisons at 13% and 18% read budgets, including all 23 comparisons on generation tasks, and avoided repetition-loop collapse observed with calibrated thresholds on Gemma 4. Their separate reader retained 98.1% of full-model accuracy while reading 17.8% of routed-expert weights across three post-trained MoE models. The authors also report that its Triton kernels ran MoE blocks 1.60–1.90 times faster than optimized vLLM at batch size one and up to 2.69 times faster at larger batch sizes.
Hugging Face · Papers · 🔥 0 - Hob launches open-source desktop AI for local model workflows
Creator Christian announced Hob, a free MIT-licensed desktop app for Mac, Windows and Linux that runs open-weight models locally and can work with local files. The app reportedly selects a model based on installed hardware and supports sandboxed Python, web search powered by SearXNG, MCP connectors, image recognition and filesystem operations; its privacy, sandboxing and model-selection behavior has not been independently verified.
Hacker News 首页（100+ 分） · 🔥 0 - NVIDIA releases Nemotron 3.5 Bolt E4B for edge devices
NVIDIA released Nemotron 3.5 Bolt E4B as a small open model for local assistants, interactive characters, robots, voice interfaces, and embedded devices. The model card says the 4GB version is post-trained from Gemma-4-E4B-it and improves multi-turn tool use, instruction following, and input grounding, while UnslothAI reports that it supports vision and can run locally with 6GB of RAM. NVIDIA AI lists the 4GB model as available, and the model is licensed for commercial or non-commercial use.
UnslothAI (X) · 🔥 4 - Ollama v0.40.10 Adds NVIDIA Nemotron 3.5 Bolt E4B
Ollama v0.40.10 adds support for NVIDIA’s Nemotron 3.5 Bolt E4B, a smaller vision-language model based on Gemma 4 and available through Ollama. According to the release notes, the model supports local conversations, image understanding, tool use, a 128K-token context window, and DFlash draft-based speculative decoding. The release also updates Gemma renderers and adds MLX support for Gemma 4 targets.
Ollama Releases · 🔥 0 - 2-bit draft plus residual verifier scheme targets models that don't fit VRAM
A Reddit user posting as Happy_Guitar3521 in r/LocalLLaMA proposed keeping a 2-bit copy of a model in GPU VRAM and the 6–8-bit residual weights in system RAM, with the GPU drafting tokens and the CPU verifying them so only activations cross PCIe. The author reports self-measured results from dequantized weights run in PyTorch: a 2-bit plus 8-bit residual setup was closer to bf16 than Q8_0 at about half the KL divergence, a 6-bit residual reached Q8_0-level quality while reading roughly 28% fewer bytes from RAM, and the 2-bit draft reusing the verifier's KV cache accepted 25–30% more tokens per check than Google's official Gemma 4 31B drafter (5.23 vs 4.20). These results are self-reported and not independently verified; the post provides no tok/s figures, says the approach only makes sense when Q8 does not fit in VRAM, and shares a public Hugging Face repository for others to experiment with.
r/LocalLLaMA · 🔥 0 - 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 - Surface Laptop Ultra review: RTX Spark brings AI power, Windows 11 lags
Tom’s Hardware’s review of the Microsoft Surface Laptop Ultra finds the Nvidia RTX Spark N1X machine (20 CPU cores, 6,144 CUDA cores, 32GB unified memory) is among the first Windows on Arm laptops without a Qualcomm chip, and one of the best-built Surfaces yet. It praises the 15-inch mini-LED display, three USB4 Type-C ports and the new Magnetic Connect charger, but notes the 4.41-pound body and the $3,699.99 as-configured price. In benchmarks it scored 3,151 single-core and 20,203 multi-core on Geekbench 6, trailing both the M5 Max MacBook Pro and a Snapdragon X2 Elite Extreme laptop, while edging the MacBook Pro by one second in a Handbrake 4K-to-1080p transcode and running smaller local models such as Gemma 4 12B and Qwen 3.6 35B Q2. The reviewer stresses that Windows 11 still ships no system-level AI features for the chip—Nvidia OpenShell, the new Copilot and taskbar natural-language search are not ready—so apart from local model tinkering, the experience feels largely unchanged.
Tom’s Hardware · 🔥 0 - Janas streams MoE experts from SSD, runs 80B models on a 32GB laptop
Janas is an experimental, GPL-3 open-source inference engine by Maurizio Cammalleri that streams mixture-of-experts weights from an NVMe SSD so models larger than RAM can run on ordinary hardware. Its own janas-bench numbers on a Debian 13 laptop (Intel Core Ultra 9 185H, integrated Arc GPU, 32 GB RAM) put the 48.4 GB Qwen3-Next-80B-A3B at 62 tok/s reading a 1,024-token prompt and 24 tok/s writing, against 16 and 2.8 tok/s for llama.cpp on the same files. Janas reports faster prompt reading and generation than llama.cpp on most of the compared models, with one exception: a 3-bit Qwen3-30B-A3B reads prompts slower than llama.cpp.
Hacker News · AI（100+ 分） · 🔥 0 - Unsloth releases free notebook to train decision models locally
Unsloth published a free Colab notebook and companion guide showing how to train Qwen3.5-4B locally into a “decision model” that outputs decisions instead of text, using only 8GB of VRAM. Unsloth says its open-source repo can turn any model, such as Qwen3.8 or Gemma 4, into a decision model, and claims it raised Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across three decision benchmarks on just 4GB of VRAM (these figures are Unsloth’s own and the benchmark methodology was not disclosed). The walkthrough covers data preparation (state, questions, gold answers), training and serving, and likens the result to Jev.
UnslothAI (X) · 🔥 0 - Google AI roundup: SynthID.com global, Nano Banana 2.1, EmbeddingGemma 2
In an October 9 weekly roundup post, Google AI said its SynthID.com detector portal is now available globally in English, letting anyone verify whether images, video or audio were AI-generated by Google or industry partners. The post also described Nano Banana 2.1 as offering improvements in visual design, subject consistency and precise spot editing, and called EmbeddingGemma 2 its first natively multimodal open model, unifying text, images, video, audio and code for on-device embeddings. It further mentioned Gemma 4 paired with BOTANIC-1, an AI trained on plant DNA, to pinpoint crop-benefiting mutations, a universal 24/7 cloud Gemini agent for business, and Guided Vision in Gemini Live for real-time visual assistance; all capability descriptions come from Google's own promotional post.
GoogleAI (X) · 🔥 0 - Google launches AI Edge Foresight, an offline macOS notetaker
Google released Google AI Edge Foresight, an experimental macOS note-taking app that transcribes meetings and audio entirely offline using the on-device 740M-parameter EmbeddingGemma 2 model, with a Gemma 4-powered assistant for chat. It offers a split-screen view for manual shorthand notes alongside AI-polished notes, and supports importing PDFs, Google Docs, Microsoft Office files, plain text, Markdown and web bookmarks as a knowledge base it can reference in real time. Google says your files, meeting audio and notes never leave your computer; the app is free and currently optimized only for Apple Silicon Macs.
TechCrunch AI · 🔥 0 - Transformers v5.19.0 adds EmbeddingGemma 2 support
Hugging Face Transformers v5.19.0 adds support for EmbeddingGemma 2, Google’s multimodal embedding model for text, images, audio, and video in a shared 768-dimensional vector space. The release also changes MoE router-logit outputs, expands expert-parallel token dispatch, updates continuous-batching attention behavior, and fixes quantized and per-layer cache handling.
Transformers Releases · 🔥 0 - OpenTPU open-sources an AI accelerator built by AI
FeSens has published OpenTPU, an open-source AI accelerator whose hardware, instruction set, simulator, compiler, and host software are contained in one monorepo. The project reports running models including LFM2.5-230M, Qwen3, Qwen3.5, Gemma 4, SmolLM3, and Phi-4-mini on an FPGA card with bit-for-bit agreement between the simulator and hardware.
Hacker News · AI（100+ 分） · 🔥 0 - 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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