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Open-weight models are driving the latest excitement in the AI landscape. Running powerful models locally improves privacy, cuts costs, and enables offline use. But the open-source models are far and few! But Google‘s Gemma 4 is here to change that! This guide walks through what Gemma 4 is, would explores its variants, and outlines the […] The post Running Gemma 4 Locally with Ollama on Your PC appeared first on Analytics Vidhya.
Read full articleA practical comparison between rule-based PDF extraction using pytesseract and an LLM-based approach with Ollama and LLaMA 3, based on a realistic B2B order scenario. The post I Built the Same B2B Document Extractor Twice: Rules vs. LLM appeared first on Towards Data Science.
Learn how to build an AI-powered Learning Management System from scratch using Ollama, FastAPI, and React. A step-by-step guide for beginner and intermediate developers.
Google's new Multi-Token Prediction drafters can make Gemma 4 run up to 3x faster on your own hardware—no cloud required, and no quality lost.
Up to 3x the speed with no loss of quality—is it too good to be true?
Large language models are getting incredibly powerful, but let’s be honest—their inference speed is still a massive headache for anyone trying to use them in production. Google just launched Multi-Token Prediction (MTP) drafters for the Gemma 4 model family. This specialized speculative decoding architecture can actually triple (3x) your speed at inference time, all without […] The post Google AI Releases Multi-Token Prediction (MTP) Drafters for Gemma 4: Delivering Up to 3x Faster Inference Without Quality Loss appeared first on MarkTechPost.
The release of Gemma 4 has added energy to the discussion of local models and their importance. Models that you can download and run on hardware you own are becoming competitive with the “frontier models” hosted by large AI providers. These models have gotten good enough for production use, good enough for tasks that until […]
Google’s Gemma 4 comes touted as the latest evolution of Google’s multi-modal model offerings. Gemma 4 not only offers reasoning and tool use, but vision and audio functionality, and it’s available in a range of model sizes that target servers and local devices. What’s striking about Gemma 4 is that even at the higher end of its size range, it’s still decently performant on personal hardware. Google claims this is due to innovations in the architecture of the model, but the proof is in the trying. Gemma 4 is quite responsive. To that end, I took Gemma 4 for a spin on my own hardware to see how it fared for its advertised tasks. Gemma 4 model sizes Gemma 4 comes in four basic sizes or “densities”: E2B: 2.3 billion effective parameters, 5.1 billion total, 128K max context window. E4B: 4.5 billion efffective parameters, 8 billion total, 128K max context window. 31B: 31 billion parameters (the “dense” version), 256K max context window. (You will probably not use this one on your own machi
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