🙌 Congrats Google DeepMind, Google AI for Developers on the release of your Gemma 4 models!🎉 The new multimodal and multilingual models are built for fast, efficient, and secure AI across devices – and optimized to run locally on NVIDIA RTX, RTX PRO, DGX Spark, and Jetson. 👉 Prototype the 31B model and start experimenting for free on https://lnkd.in/gttfrsCb 🔗Check out the details to get started in our Technical Blog: https://lnkd.in/gC8iTd2m
Gemma 4 is here. 💻 We’ve built a new family of open models based on the same world class research and tech as Gemini 3. “Open” means the model weights are yours to download, customize, and run on your own hardware. ⚖️ Four sizes: High-performance versions for workstations (31B Dense & 26B MoE) and highly optimized “Edge” versions (E4B & E2B) built specifically for mobile. 🧠 Advanced reasoning: Capable of multi-step planning and deep logic with native vision and audio support. 🤖 Built for agents: Native tool use lets you build autonomous systems that can actually do things, like search databases or trigger APIs. 🔒 Apache 2.0 License: Complete flexibility to build, fine-tune, and deploy however you want. Start building with Gemma 4 now in Google AI Studio. You can also download the model weights from Hugging Face, Kaggle, or Ollama. Find out more → https://goo.gle/4cb8LBE
Everyone is focusing on “Open” and model size, but the more interesting signal is native tool use plus local deployment. That combo pushes these models much closer to actual agent infrastructure instead of just another benchmark event. The hard part now isn’t access, it’s whether teams have the evals and guardrails to stop a capable local model from creating very confident chaos.
Thank you so much for sharing!🇵🇰🇺🇸🇹🇼🇻🇳🇫🇷🇮🇳
Native tool use on a 26B MoE that runs locally is the real unlock here. That moves open models from "good for chat" to "usable in actual agent pipelines" territory.
What makes releases like this matter isn’t just the model, it’s the deployment flexibility. Once teams can run multimodal models locally across very different hardware tiers, AI stops being a central platform privilege and starts becoming an operating layer inside real workflows. That’s when you see the biggest shift, not in demos, but in all the boring internal processes that suddenly become automatable.