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ENTITY Gemma 4

Gemma 4

PulseAugur coverage of Gemma 4 — every cluster mentioning Gemma 4 across labs, papers, and developer communities, ranked by signal.

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Total · 30d
214
214 over 90d
Releases · 30d
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Papers · 30d
31
31 over 90d
TIER MIX · 90D
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TIMELINE
  1. 2026-06-21 product_launch Deployment guide for the 12B Gemma 4 QAT model on Google Cloud Run with NVIDIA L4 GPUs. source
  2. 2026-06-15 product_launch Google released the Gemma 4 open model, enabling users to run AI without needing to purchase GPUs. source
  3. 2026-06-15 product_launch Google DeepMind's Gemma 4 models are now available on Amazon Bedrock. source
  4. 2026-06-13 product_launch Google released quantization-aware-trained checkpoints for the Gemma 4 family of models. source
  5. 2026-06-09 product_launch Google has expanded its Gemma AI model family with the release of Gemma 4, featuring an Apache 2.0 license and longer context windows. source
  6. 2026-06-06 product_launch Google released Gemma 4 checkpoints optimized for Quantization-Aware Training. source
  7. 2026-06-06 product_launch Google released smaller Gemma 4 models optimized for local AI deployment. source
  8. 2026-06-06 product_launch Google released Gemma 4 QAT checkpoints, allowing a 12B model to run on 8GB VRAM. source
  9. 2026-06-05 product_launch Google released quantization-aware training checkpoints for the Gemma 4 model family. source
  10. 2026-06-04 product_launch Google released the Gemma 4 AI model, optimized for on-device performance on laptops. source
  11. 2026-06-03 product_launch Google is preparing to release new models within the Gemma 4 family, with a potential 120B parameter version speculated. source
  12. 2026-06-03 product_launch Google DeepMind has launched the Gemma 4 family of open-weight multimodal models. source
  13. 2026-06-02 product_launch Google launched the Gemma 4 family of open-source AI models. source
  14. 2026-05-23 research_milestone Gemma 4 model achieves a 37.5% score on competition mathematics. source
  15. 2026-05-23 product_launch Google DeepMind released the Gemma 4 family of open multimodal models. source
SENTIMENT · 30D

28 day(s) with sentiment data

RECENT · PAGE 1/10 · 200 TOTAL
  1. TOOL · CL_113921 ·

    AI agents framework and uncensored chatbot emerge on Mastodon

    A new AI agent framework allows users to run and share AI agents, offering flexibility for privacy or broad distribution. Separately, a model named Obliteration Gemma 4 is being promoted for an uncensored AI experience.
  2. COMMENTARY · CL_113681 ·

    Newsletter highlights NNS-Python, AI learning resources, and agent orchestration book

    This week's newsletter highlights the open-source project NNS-Python, created by Fred Viole. It also introduces new learning resources covering topics such as AI research second brains, agentic AI loops, building Telegr…
  3. TOOL · CL_113872 ·

    Orthrus to release Qwen 3.5/3.6 and Gemma 4 models with open-source code

    Orthrus, a project focused on training large language models, is preparing to release support for Qwen 3.5, Qwen 3.6, and Gemma 4 models. Alongside the model checkpoints, Orthrus will also open-source its complete train…
  4. COMMENTARY · CL_112973 ·

    Cheapest LLM APIs for Startups in 2026: Open-Weights Models Offer Major Savings

    For startups in 2026, utilizing open-weights LLM APIs through platforms like OpenRouter offers a significant cost advantage. Models such as Meta's Llama 3.1 8B Instruct and Microsoft's Phi-4 provide substantial savings,…
  5. SIGNIFICANT · CL_112332 ·

    Google unveils Gemini 3 Pro with native multimodal understanding and faster inference

    Google has launched its latest AI model, Gemini 3 Pro, featuring a significant architectural overhaul for enhanced reasoning, multimodality, and coding capabilities. This new model processes text, audio, and video strea…
  6. TOOL · CL_112345 ·

    Google's Gemma models reach 200M downloads in 2.5 months

    Google DeepMind announced that its Gemma family of models has surpassed 200 million downloads in just two and a half months. This milestone highlights significant community adoption and rapid growth, with the number of …
  7. COMMENTARY · CL_111410 ·

    OpenAI internal AI usage surges; new open models and agent capabilities emerge

    OpenAI has reported a significant increase in internal AI model usage across various departments, with median output tokens for Codex growing by as much as 56x in Research since November 2025. This surge in token consum…
  8. SIGNIFICANT · CL_110786 ·

    DeepReinforce releases Ornith-1.0 open-source coding models that learn RL scaffolds

    DeepReinforce has launched Ornith-1.0, a family of open-source coding models available under the MIT license. These models, built upon Gemma 4 and Qwen 3.5, are designed for agentic coding tasks and uniquely learn their…
  9. TOOL · CL_110371 ·

    MLOps pipeline detailed for AI stylist application Dresscode

    This article details the MLOps pipeline for an AI stylist application called Dresscode, which utilizes both computer vision and generative AI. The author outlines the seven key steps of a typical MLOps process, from dat…
  10. COMMENTARY · CL_110103 ·

    MTP feature degrades output quality for Qwen 3.6 and Gemma 4 models

    A user on r/LocalLLaMA reported a significant decrease in output quality when using the MTP (Multi-Turn Processing) feature with Qwen 3.6 and Gemma 4 models. Despite MTP offering higher token generation speeds, the user…
  11. RESEARCH · CL_107768 ·

    African languages face significant tokenization penalty in frontier LLMs

    A new research paper reveals a significant "African Language Tax" in frontier large language models, where tokenizers assign substantially more subword tokens to African languages compared to English. This results in hi…
  12. COMMENTARY · CL_104463 ·

    Gemma 4's potential questioned amid lack of community fine-tunes

    The user is inquiring about the potential of Gemma 4 to become as popular and widely fine-tuned as Mistral AI models, despite Gemma 4's superior base performance and features like quantization-aware training (QAT) and b…
  13. TOOL · CL_106510 ·

    Gemma 4 deployed on Google Cloud Run with NVIDIA Blackwell GPUs

    This article details a deployment guide for Gemma 4, a 12B parameter model, utilizing Google Cloud Run with GPU capabilities. It outlines the use of the MCP (Model Control Plane) framework, NVIDIA Blackwell 6000 GPUs, a…
  14. TOOL · CL_103497 ·

    Qwen models lead local vision AI benchmark across hardware tiers

    A recent benchmark update for local vision models reveals Qwen3.6 27B (nothink) at Q4 quantization as the top performer for systems with 24GB+ VRAM, achieving a score of 79.6/100. For mid-tier hardware (12-16GB VRAM), Q…
  15. TOOL · CL_106412 ·

    Gemma 4 12B Model Deployed on Cloud Run with NVIDIA L4 GPUs

    This article details a deployment guide for the 12B Gemma 4 QAT model on a Google Cloud Run instance equipped with NVIDIA L4 GPUs. It focuses on implementing speculative decoding to enhance the model's efficiency and pe…
  16. TOOL · CL_106354 ·

    Local AI coding agent replaces Codex with Gemma 4 and Ollama

    A developer replaced GitHub Copilot's Codex with Google's Gemma 4 model, running locally via Ollama, and found the setup to be a superior local AI coding agent. This new setup allowed for more efficient refactoring of l…
  17. FRONTIER RELEASE · CL_111214 ·

    DeepReinforce AI releases Ornith-1.0 family of open-source coding models

    DeepReinforce AI has released the Ornith-1.0 family of open-source models, designed for agentic coding tasks. The models, available in various sizes including 9B, 35B, and 397B parameters, are built upon Gemma 4 and Qwe…
  18. TOOL · CL_102300 ·

    GGUF format explained: what's included and what's missing

    The GGUF file format, used by llama.cpp for AI language models, offers several advantages including being a single, self-contained file. It stores crucial information beyond just model weights, such as chat templates de…
  19. TOOL · CL_102174 ·

    Google Gemma 4 models detailed: VRAM needs from phones to high-end GPUs

    Google has released Gemma 4, offering four model variants with varying VRAM requirements. The smallest model is suitable for devices with minimal memory, while the largest, a 31B Dense model, requires at least 22GB of V…
  20. TOOL · CL_101783 ·

    llama.cpp SYCL benchmarks show mixed performance for Gemma and Qwen models

    Benchmarks for the llama.cpp project using the SYCL backend have been released, showcasing performance metrics for various models. The tests included Gemma 4 models of different sizes (4.65B, 11.91B, and 25.23B paramete…