MiniMax M2.7 Goes Open-Weight to Let You Run Agents Locally

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

Imagine asking your AI model, “What’s the weather in Tokyo right now?” and instead of hallucinating an answer, it calls your actual Python function, fetches live data, and responds correctly. That’s how empowering the tool call functions in the Gemma 4 from Google are. A truly exciting addition to open-weight AI: this function calling is […] The post Gemma 4 Tool Calling Explained: Build AI Agents with Function Calling (Step-by-Step Guide) appeared first on Analytics Vidhya.

The open-weights model ecosystem shifted recently with the release of the <a href="https://blog.

Google, my favourite tech firm for reasons exactly as this one, has done it once again. It has got the worldwide community of developers supercharged with one new product. This one is called Gemma 4. What’s the hype? Well, a completely open-source model that competes with AI models 20 times its size. And this one […] The post Top 10 Gemma 4 Projects That Will Blow Your Mind appeared first on Analytics Vidhya.

MiniMax, the AI research company behind the MiniMax omni-modal model stack, has released MMX-CLI — Node.js-based command-line interface that exposes the MiniMax AI platform’s full suite of generative capabilities, both to human developers working in a terminal and to AI agents running in tools like Cursor, Claude Code, and OpenCode. What Problem Is MMX-CLI Solving? […] The post MiniMax Releases MMX-CLI: A Command-Line Interface That Gives AI Agents Native Access to Image, Video, Speech, Music, Vision, and Search appeared first on MarkTechPost.