SemanticScuttle - klotz.me

klotz: multimodal* + gemma 4*

0 bookmark(s) - Sort by: Date ↓ / Title / - Bookmarks from other users for this tag

1. Gemma 4 sees, hears, and reads on my 16GB laptop, and it never phones home

   The article discusses the benefits of running Google’s Gemma 4 models locally on personal hardware to ensure data privacy and independence from cloud services. By executing these multimodal models on a laptop, users can process images and audio without exposing sensitive information to third-party servers. The text highlights how efficient architecture allows for vision capabilities and speech recognition even with limited VRAM.
   
   * Localized processing avoids the risks of uploading private or regulated data
   * Native audio support in specific model variants like E2B and E4B
   * Large context windows allow for deep analysis of lengthy documents and codebases
   * Reduced reliance on internet connectivity during mobile workflows

   2026-07-12 Tags: gemma 4, google, local inference, multimodal, privacy, llm, android by klotz
2. Google’s Gemma 4 Changes Everything for Open Source AI

   Google's release of Gemma 4 marks a major turning point for open-source AI, offering a versatile family of multimodal models under a permissive Apache 2.0 license. Built using Gemini 3 technology, these models demonstrate massive leaps in math and coding performance, rivaling much larger proprietary systems while remaining efficient enough to run on local hardware ranging from smartphones to high-end GPUs. This release positions Google as a formidable competitor in the open-weights ecosystem, prioritizing user ownership and deployment efficiency.
   
   * Apache 2.0 license
   * Multimodal intelligence
   * Local hardware deployment
   * Massive benchmark leaps
   * Efficient MoE architecture
   
   **Models**
   * E2B: Mobile efficiency
   * E4B: Edge specialist
   * 26B MoE: Speed meets intelligence
   * 31B Dense: Top-tier performance

   2026-07-04 Tags: google, gemma 4, llm, open source, deepmind, multimodal, apache 2.0, machine learning, self-hosting, moe by klotz
3. Google DeepMind Releases Gemma 4 12B: An Encoder-Free Multimodal Model with Native audio that runs on a 16 GB laptop

   Google DeepMind has released the Gemma 4 12B, a dense multimodal model featuring an encoder-free architecture. Unlike previous iterations that used separate vision and audio encoders, this model allows these modalities to flow directly into the LLM backbone. This streamlined design reduces latency and memory overhead, allowing the model to perform agentic reasoning tasks on consumer laptops with as little as 16 GB of VRAM while approaching the performance levels of much larger models like the 26B MoE variant.
   
   - Unified decoder-only architecture for text, image, video, and native audio input.
   - Encoder-free design using a 35M vision embedder and direct raw audio wave projection.
   - Optimized to run locally on Apple Silicon Macs and consumer GPU laptops.
   - Released under an Apache 2.0 license with support for llama.cpp, MLX, vLLM, and Ollama.

   2026-06-06 Tags: google deepmind, gemma 4, multimodal, audio, large language model, machine learning, agents by klotz
4. I finally found an open-source local LLM that actually competes with cloud AI

   The author explores the utility of Google DeepMind's Gemma 4 as a powerful option for running large language models locally on consumer hardware. By testing the E4B variant using tools like LM Studio and llama.cpp, they demonstrate how open-weight models can handle multimodal tasks including text, image analysis, and audio processing with impressive precision and privacy.

   2026-05-12 Tags: gemma 4, google deepmind, local llm, multimodal, llama.cpp by klotz
5. Gemma 4

   This document details how to run Google's Gemma 4 models locally, including the E2B, E4B, 26B-A4B, and 31B variants. Gemma 4 is a family of open models supporting over 140 languages and up to 256K context, available in both dense and MoE configurations. The E2B and E4B models support image and audio input. These models can be run locally on your device and fine-tuned using Unsloth Studio. The document outlines hardware requirements, recommended settings, and best practices for prompting and multimodal use, including guidance on context length and thinking mode.

   2026-04-02 Tags: gemma 4, llm, local ai, unsloth, models, inference, fine-tuning, llama.cpp, multimodal by klotz

Top of the page

First / Previous / Next / Last / Page 1 of 0