Maple AI Runs 5x Faster Than Gemma 4
Get the Agent OS 👉 https://www.skool.com/ai-profit-lab-7462/about Want to make money and save time with AI? Join here: https://www.skool.com/ai-profit-lab-7462/about Video notes + links to the tools 👉 https://www.skool.com/ai-profit-lab-7462/about Get a FREE AI Course + Community + 1,000 AI Agents 👉 https://www.skool.com/ai-seo-with-julian-goldie-1553/about Get a FREE AI SEO Strategy Session → https://go.juliangoldie.com/strategy-session?utm=julian Get 200+ Free AI SEO Prompts → https://go.juliangoldie.com/chat-gpt-prompts Get out SEO link building book here 👉 https://go.juliangoldie.com/opt-in?utm=julian Maple Preview: Fast Open-Source Local Reasoning Model (Ternary Weights, 20B/1B) + Agentic OS Demo The script reviews Maple Preview, a new open-source local reasoning LLM described as 20B with a 1B runtime cost (A1B) using ternary weights, emphasizing its speed on devices like a Mac Studio and even an iPhone. The presenter demos quick local coding tasks (e.g., snake game, landing page) inside a local agentic operating system, highlighting offline use, free local runs, workspace previews, and swapping models alongside others like LFM 2.5/2.6B with Hermes Agent. It explains Maple’s ternary weights (-/0/+) shrinking model size (e.g., ~38GB to ~5GB) and an expert system with 256 specialists activating 8 per token, plus a 128k context window and MIT license. Benchmarks suggest strong speed/quality, though the presenter notes coding quality isn’t on par with Gemma 4, GLM 4.7 Flash, GPT-RSS, or Claude Sonnet 5. Viewers are directed to chat.deepgrove.ai, Hugging Face weights, and the AI Profit Boardroom community for the full agentic OS and training. 00:00 Maple Preview Demo 01:00 Speed vs Quality Benchmarks 01:57 Mobile Focus and Comparisons 03:05 Autonomy and Where to Try 03:49 Local OS Coding Workflow 04:48 Ternary Weights Explained 05:25 Expert Routing Architecture 06:05 Real World Results and Takeaways 06:50 Agentic OS and Community Offer 07:58 Wrap Up
