NEW Microsoft AI Update is INSANE!

Microsoft just dropped something wild. You can now run 100 billion parameter AI on your laptop. No GPU needed. Six times faster than normal models. Uses 82% less power. This changes everything for local AI. And listen, what I'm about to show you is actually insane. Microsoft just released an update that lets you run AI models on your regular computer CPU. No fancy GPU needed. And we're not talking about some weak model. We're talking about models that compete with Llama and Quen. Okay, so here's what happened. Microsoft has this thing called bitnet. cpp. They launched it in October 2024 and they just dropped a massive update in 2025 that adds GPU support and new models. But here's the crazy

part. These models use something called 1. 58 bit quantization. That sounds technical, but stick with me because this is wild. Normal AI models use 16 bit or 8 bit numbers for their weights. Bitnet uses turnary weights. That means each weight can only be negative 1 0 or positive 1. three options. That's it. You're probably thinking, "Doesn't that make the model dumb? " Nope. And I'm going to show you the benchmarks in a second that will blow your mind. So, why does this matter? Because when you only have three possible values, your computer doesn't need to do complex math. It just does addition and subtraction, no multiplication. That's way faster and uses way less energy. Let

me give you the numbers. The BitNet B1. 58 model with 2 billion parameters uses only 0. 4 GB of memory. Compare that to Llama 3. 21b, which uses 2 GB. That's five times smaller. But here's where it gets really good. The Bitnet model actually performs better on benchmarks. On GSM8K, which tests math reasoning, Bitnet scored 58%. Llama 3. 21b scored only 38%. That's a huge difference. And get this, Bitnet processes each token in 29 milliseconds on a CPU. Llama takes 48 milliseconds. So, Bitnet is faster and smarter and smaller. The energy usage is even more insane. Bitnet uses 0. 028 jewels per token. Llama uses 0. 258 jewels. That's almost 10 times more energy for worse performance. Now, imagine you're running the AI profit boardroom and you want to automate your customer support. You could run Bitnet on a cheap server with no GPU and handle thousands of queries per day. The energy costs would be basically nothing. Or say you want to build an AI assistant that helps your community members with AI automation questions. You could deploy this on edge devices. It could run on phones, on IoT devices, on cheap hardware. Here's what makes this even

better. Microsoft tested a simulated 100 billion parameter model on a single CPU core. It ran at 5 to 7 tokens per second. That's human reading speed on one CPU core, no GPU. Think about what that means. Right now, if you want to run a 100b model, you need expensive cloud GPUs. You're paying dollars per hour. With Bitnet, you could run it locally for almost free. All right, I want to show you how to actually use this because unlike a lot of AI news, you can download this right now and test it yourself. If you want to learn how to save time and scale your operations with AI tools like BitNet and hundreds of other automation strategies, you need to check out the AI profit boardroom. We show you exactly how to implement AI in your business. From customer support to content creation to data analysis, you get step-by-step processes, templates, and a community of people who are already crushing it with AI. Link in the

description. First, you go to GitHub and clone the repository. It's github. com/microsoft/bitnet. It has over 24,000 stars. This is legit. You'll need to create a environment. Just run create mbitnet- cppy equals 3. 9. Then activate it. Next, you download the model from hugging face. Microsoft released the bitet b1. 582b4 model in gguf format. You can grab it with hugging face- cli microsoft/bitnet-b8-2b-40-guf. Then you run the setup script python setup_mv. py with the model path and quantization flag i2_s. That's the optimized kernel they use. And then you just run inference python run_inference. p pi with your model and a prompt. You could try something like, "Write me a customer on boarding email for the AI profit boardroom that explains how our community helps people automate their business with AI. " And boom, it generates the text right there on your CPU. Fast, efficient, local. Now, here's what I love about this. You're not sending your data to the cloud. Everything stays on your machine. That's huge for privacy. That's huge for businesses that can't send customer data to third parties. All right. So, let me

tell you why this matters beyond just the cool tech. First, this makes AI accessible to everyone. You don't need a $10,000 GPU. You don't need to pay for API calls. You can run powerful models on a laptop from 2020. Second, this is way better for the environment. AI training and inference use massive amounts of energy. Data centers are eating up electricity. With Bitnet using 82% less energy, that's a huge win. Third, this enables edge AI. You can put AI on phones, on cameras, on robots, on IoT devices. Imagine a security camera that can analyze video locally with no internet connection or a drone that can navigate using AI without sending data back to a server. Fourth, this is faster when you're not waiting for API calls or cloud latency. Everything just works instantly. For the AI profit boardroom, if we wanted to build a real-time AI assistant that helps members troubleshoot automation issues, we could deploy Bitnet locally and get instant responses. Now, let me talk about the

technical details for a second because they're actually really clever. Bitnet uses something called ABS mean scaling. That means it scales the turnary weights by their absolute mean value. That keeps the model accurate even with such low precision. The activations are still 8 bit. So, you have 1. 58 bit weights, but 8 bit activations. That's where the name comes from. They use optimized kernels called i2S and TL. These are custom CPU and GPU operations that are specifically designed for Turnary Math. That's why it's so fast. And here's something wild. The latest updates in May 2025 added GPU support. So now you can run Bitnet on GPUs, too. And it's even faster. They support models up to 10 billion parameters. Let me give you another benchmark. When you compare Bitnet to Quen 2. 5 1. 5B, Bitnet uses 0. 4G guy versus 2. 6 GB. So Bitnet has 29 minutes latency versus 65 minutes for Quen. And Bitnet uses 0. 028 jewels versus 0. 347 jewels for Quen. On GSM8K, Bitnet scored 58. 38% and Quen scored 56. 79%. So they're basically tied in accuracy, but Bitnet is way smaller, way faster, and way more efficient. The MMLU benchmark, which tests general knowledge, shows Quen slightly ahead at 60. 25% versus Bitnet at 53. 17%. So, bit is a bit weaker on pure knowledge, but for most practical tasks, that difference doesn't matter. Now, here's what I think is going to happen. Microsoft is going to keep improving this. They're working on larger models. They're optimizing the kernels even more, and other companies are going to start copying this approach. We're already seeing derivatives. There's the Aramus 2B model, which is a bit variant. There are community projects on GitHub experimenting with different quantization schemes. This is going to become the standard for local AI. Why would you use a 2GB model when you can use a 0. 4 GB model that's faster and better? For content creators, this is huge. You could run AI writing assistants locally. For developers, you can embed AI into apps without cloud costs. For businesses, you can deploy AI at scale without massive infrastructure. Imagine you're building an AI automation agency. You could offer your clients AI solutions that run on their hardware. No subscription fees, no API costs, just onetime setup and they're good to go. Or say you're in the AI profit boardroom and you want to build a custom AI tool for your specific niche. You could fine-tune a Bitnet model on your data, deploy it locally, and have a completely private AI assistant. The possibilities are endless. Now, you might be wondering, what's the catch? Is there a downside? The main limitation is that Bitnet is still relatively new. The model selection is limited compared to the thousands of models available in 8 bit or 16- bit formats, but that's changing fast. Also, while Bitnet is great for inference, it doesn't change the training process. You still need GPUs to train these models. But once they're trained, anyone can run them. And some tasks that need really high precision might not work as well with 1. 58 bit models. But for most use cases, language generation, question answering, content creation, it works great. Here's

my prediction. Within a year, most local AI tools will switch to Bitnet or similar one-bit approaches. It just makes too much sense. You get better performance, lower cost, better privacy, and it works on any hardware. That's a no-brainer. If you want to try this yourself, go to the GitHub repo. The instructions are clear. You can have it running in 10 minutes. And if you get stuck, there are YouTube tutorials and guides that walk through every step. And if you want to learn how to actually use AI tools like this to grow your business, automate your workflows, and save hours every week, join the AI profit boardroom. You'll get complete processes, SOPs, and real examples of how to implement AI automation. Link 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. and you'll get all the video notes from there, plus access to our community of 40,000 members who are crushing it with AI. This Microsoft update is genuinely one of the biggest AI developments of 2025. Local AI just became real. No more excuses about needing expensive hardware. API costs. You can run powerful AI on your laptop right now. Go test it and let me know in the comments what you build with it. Thanks for watching. See you in the next one.