This NEW Chinese AI AGENT is INSANE! 🤯

Julian Goldie SEO · completed · 8:02 · Published 2026-02-08
ai machine-learning open-source multimodal-ai speech-recognition computer-vision local-ai automation chinese-ai real-time-interaction
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Abstract

Mini CPM-o 4.5 is a groundbreaking open-source AI model from China that enables full-duplex omnimodal interaction—seeing, listening, and speaking in real-time like a natural conversation. Despite having only 9 billion parameters, it outperforms GPT-4o and Gemini 2.0 Pro on vision benchmarks while running completely locally on your own computer with no cloud dependency or API costs. The key takeaway is that this represents a new frontier in accessible AI automation, offering businesses and developers a powerful, customizable alternative to proprietary models for applications like customer support, accessibility tools, and live screen assistance.

Summary

0:00 Introduction to Mini CPM-o 4.5 and Its Unique Capabilities

Introduction to Mini CPM-o 4.5 and Its Unique Capabilities Introduction to Mini CPM-o 4.5 and Its Unique Capabilities Introduction to Mini CPM-o 4.5 and Its Unique Capabilities
Mini CPM-o 4.5 is the first open-source AI capable of full-duplex omnimodal interaction, meaning it can simultaneously see through a camera, listen through a microphone, and respond with voice in real-time. Unlike traditional AI assistants that operate in a back-and-forth pattern—waiting for you to finish, processing, then responding—this model works like an actual phone call where both parties can interrupt each other naturally. The model runs entirely locally on your own computer without requiring cloud services or incurring API costs, making it completely private and cost-effective. Potential applications include automating customer support with actual video calls, building AI assistants that watch your screen and provide help, and creating accessibility tools that narrate the world in real-time.

1:00 Technical Architecture and Efficiency

Technical Architecture and Efficiency Technical Architecture and Efficiency
The model contains only 9 billion parameters yet achieves a 77.6 score on OpenCompass vision-language benchmarks, outperforming GPT-4o and Gemini 2.0 Pro despite those models likely having over 100 times more parameters (GPT-4 estimated at over a trillion). This remarkable efficiency comes from the team at OpenBMB combining best-in-class open-source components: Whisper for speech recognition, Qwen 2.5 as the language foundation, Cozy Voice 2 for text-to-speech, and SigLIP 2 for vision understanding. The integration of these components creates a cohesive system that feels like conversing with a person rather than a robot, all while being efficient enough to run on consumer hardware.

2:00 Core Features and Capabilities

Core Features and Capabilities
The model handles high-resolution images up to 1.8 million pixels, allowing it to understand entire dashboards, complex infographics, or detailed charts. It performs OCR exceptionally well, enabling automated document processing for business applications. Video processing runs at 10 frames per second, meaning you can point a webcam at something and receive real-time narration—useful for live customer support, manufacturing quality control, or visual assistance tools. The voice interaction goes beyond simple text-to-speech with natural intonation and emotion expression, plus the ability to customize voices to sound professional or casual. It even supports voice cloning and works in multiple languages including English and Chinese. Unlike passive AI assistants, Mini CPM-o 4.5 is proactive, commenting on what it sees, providing context-based reminders, and making suggestions without being prompted.

3:00 Local Deployment and Accessibility

Local Deployment and Accessibility
The model is completely open-source and available on HuggingFace in multiple formats including standard versions and quantized versions (INT4 and GGUF) optimized for smaller hardware. This means it can run on a decent gaming PC or even higher-end laptops without requiring data center infrastructure. Users can employ various tools for deployment: llama.cpp for CPU inference, Ollama for a simple interface, or VLM and SGLang for optimized performance. There's even a WebRTC demo that enables live video and audio streaming—simply plug in your webcam and microphone to start conversing with the model immediately.

4:00 Benchmark Performance and Component Quality

Benchmark Performance and Component Quality
The OpenCompass benchmark tests AI models on vision and language tasks including image understanding, OCR, reasoning, and answering questions about visual content. Mini CPM-o 4.5's score of 77.6 matches models like GPT-4o and Gemini 2.0 Pro, which require massive infrastructure and are expensive to run. The efficiency advantage is staggering—you can run this 9-billion parameter model on a single GPU or even CPU with quantized versions. Each component represents industry-leading technology: Whisper for speech recognition is already considered best-in-class, Cozy Voice 2 produces incredibly natural voices, and Qwen 2.5 is one of the best Chinese language models available. This combination of top-tier components working together delivers exceptional performance.

5:00 Customization and Open-Source Advantages

Customization and Open-Source Advantages
Being open-source provides unprecedented flexibility that proprietary models cannot match. Users can fine-tune the model on specific business data, customize voices to match brand identity, and add custom tools and integrations. This level of control and customization is transformative for businesses that need AI solutions tailored to their specific workflows and requirements rather than one-size-fits-all commercial offerings.

6:00 Step-by-Step Implementation Guide

Step-by-Step Implementation Guide Step-by-Step Implementation Guide Step-by-Step Implementation Guide Step-by-Step Implementation Guide
To get started: First, go to HuggingFace and search for Mini CPM-o 4.5 to find the model page with all files. Second, choose your format—standard version if you have a good GPU, or quantized version for CPU or limited hardware. Third, pick your inference framework: Ollama is easiest for beginners, llama.cpp gives more control, and VLM is best for production speed. Fourth, download the model files (several gigabytes, so it takes time). Fifth, run the WebRTC demo from the official repository to see live video and audio interaction in action. Sixth, once you understand how it works, integrate it into your own projects and start building custom applications.

7:00 Conclusion and Call to Action

Conclusion and Call to Action
Mini CPM-o 4.5 represents one of the most impressive open-source AI releases, combining full-duplex multimodal interaction with vision, speech, and text working together in real-time. Its performance rivals the best proprietary models while running on personal hardware. For those interested in practical AI automation implementation beyond just hype, the presenter recommends exploring the AI Profit Boardroom for actual workflow automation strategies and the AI Success Lab, a free community with over 40,000 members, for SOPs and 100+ AI use cases. The model represents a significant step forward in making advanced AI capabilities accessible to developers and businesses without the constraints of proprietary platforms.
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Transcript

Source: youtube_captions · en
Full transcript (8585 chars)
This new Chinese AI agent just dropped and it's absolutely wild. It can see, listen, and talk to you all at the same time. Like actually, realtime conversation with vision and voice and it beats GPT40 on vision tasks. But here's the crazy part. It runs on your own computer. No cloud, no API costs, completely local. You could use this to automate customer support with actual video calls or build an AI assistant that watches your screen and helps you work or create accessibility tools that narrate the world in real time. The possibilities are genuinely endless and I'm going to show you exactly how it works plus how you can start using it today. This is the future of AI automation and it's completely open source. Let's dive in. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. Julian Goldie reads every comment, so make sure you comment below. All right, so today I'm showing you something that genuinely blew my mind. It's called Mini CPMO 4.5. And before you click away thinking this is just another AI model, hold on. This thing is different. is the first open- source AI that can do full duplex omnimodal interaction. What does that even mean? It means it can see you through your camera, listen to you through your microphone, and talk back to you with voice all at the same time, like a real conversation, not the clunky back and forth you get with most AI assistants. This is continuous real-time interaction. Think about it like this. Most AI tools you talk to, they wait for you to finish talking, then they process, then they respond. It's like texting back and forth. But mini CPM04.5 works like an actual phone call. You can interrupt it. It can interrupt you. It sees what's happening in real time. It's wild. And here's what makes this absolutely insane. This model has only 9 billion parameters. For context, GPT4 probably has over a trillion. Yet mini CPMO 4.5 scores higher than GPT40. And Gemini 2.0 Pro on vision language benchmarks. It got a 77.6 on Open Compass. That's better than models that are literally 100 times bigger. How is that even possible? It's because the team at OpenB&B in China built this thing smart. They combined the best open-source components. Whisper for speech recognition, Quen 33 as the language foundation, Cozy Voice 2 for text to speech, Sigip 2 for vision understanding. They stitched it all together into one cohesive system. And the result is something that feels like talking to a person, not a robot. Now, let me show you what this can actually do because the features are genuinely impressive. First, it handles highresolution images up to 1.8 million pixels. That's insane detail. You could show it a screenshot of your entire dashboard or a complex infographic or a detailed chart and it would understand all of it. Second, it does OCR like a beast. You know how sometimes you need to pull text from an image or a PDF? This thing reads it perfectly, which means you could automate document processing for your business. Third, it can process video at 10 frames per second. So you could literally point your webcam at something and it would narrate what it sees in real time. Imagine using this for live customer support or quality control in manufacturing or accessibility tools for people who need visual assistance. Fourth, the voice interaction is next level. It's not just reading text out loud. It has natural intonation, emotion expression. You can even customize the voice. Want it to sound professional? Done. Want it to sound casual and friendly? Done. It even supports voice cloning. And it works in multiple languages, English and Chinese out of the box. But here's where it gets really interesting. This model is proactive. Most AI just sits there waiting for you to ask questions. But Mini CPMO 4.5 can actually comment on what it sees, give you reminders based on context, make suggestions. It's like having an AI assistant that actually pays attention. Now, you might be thinking, "Okay, this sounds cool, but how do I actually use it?" Here's the best part. You can run this completely locally on your own computer. No sending data to the cloud. No API costs, no privacy concerns. The model is open source and available on HuggingFace right now. They've released it in multiple formats, standard versions, quantized versions for smaller hardware. There's even INT4 and GGUF formats, which means you can run this on a decent gaming PC or even some higherend laptops. You don't need a data center. And the community has already built tools to make it easy. You can use llama.cpp for CPU inference or lama if you want a simple interface or VLM and SG lang if you want optimized performance. There's even a Web RTC demo which lets you do live video and audio streaming with the model. Like literally plug in your webcam and microphone and start talking. Now, here's what really impresses me about this model. The benchmarks. I mentioned the Open Compass score earlier, but let me break down what that actually means. Open Compass tests AI models on vision and language tasks. Things like image understanding, OCR, reasoning, answering questions about visual content. Mini CPMO 4.5 scored 77.6. GPT40, which is one of the best proprietary models out there, scores in a similar range. Gemini 2.0 Pro, same thing. But here's the kicker. Those models are massive. They require huge infrastructure. They're expensive to run. Mini CPMO 4.5 is 9 billion parameters. You can run it on a single GPU or even on CPU if you use the quantized versions. That's absolutely insane efficiency. And it's not just vision benchmarks. The speech recognition is powered by whisper which is already industry-leading. The text to speech uses cozy voice 2 which produces incredibly natural voices. The language model is based on quenthry which is one of the best Chinese LLMs. Everything about this model is bestin-class components working together. And because it's open source you can customize it. Want to fine-tune it on your specific business data? You can do that. Want to change the voice to match your brand? You can do that. Want to add your own tools and integrations? You can do that. This level of flexibility is something you never get with closed source models. Now, let me give you the quick technical overview. If you want to try this yourself, here's what you do. Step one is go to hugging face, search for mini CPM04.5. You'll find the model page with all the files. Step two is choose your format. If you have a good GPU, grab the standard version. If you're running on CPU or limited hardware, get the quantized version. Step three is pick your inference framework. Lama is easiest for beginners. llama.cpp gives you more control. VLM is best for production speed. Step four is download the model. This might take a while. It's several gigabytes. Step five is run the demo. The official repo has a web RTC demo you can try. It shows live video and audio interaction. You'll literally see the model responding to you in real time. Step six is start building. Once you see how it works, you can integrate it into your own projects. All right, let me wrap this up. Mini CPM04.5 is genuinely one of the most impressive open-source AI releases I've seen this year. Full duplex multimodal interaction, vision, speech, and text all working together in real time. Performance that rivals the best proprietary models, all in a package you can run on your own hardware. If you're into AI automation, this is something you need to explore. And if you want to learn how to save time and automate your business with cuttingedge AI tools like mini CPMO 4.5, check out the AI profit boardroom. We show you the actual implementation. How to take tools like this and turn them into real workflow automation that saves you hours every week. Not just the hype, but the practical stuff that actually works. Links in the description. And if you want the full process, SOPs, and 100 plus AI use cases like this one, join the AI success lab. It's our free AI community. links in the comments and description. You'll get all the video notes from there, plus access to our community of 40,000 members who are crushing it with AI. All right, that's it for today. Go check out Mini CPMO 4.5. Play with it, build something cool, and let me know in the comments what you create with it. Julian reads every single comment, so drop your thoughts below. I'll see you in the next