# Meta’s LLAMA 4: The Infinite AI!

## Метаданные

- **Канал:** Two Minute Papers
- **YouTube:** https://www.youtube.com/watch?v=j8tssDT73Yc
- **Дата:** 07.04.2025
- **Длительность:** 4:10
- **Просмотры:** 64,720
- **Источник:** https://ekstraktznaniy.ru/video/12475

## Описание

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers

Guide for using DeepSeek on Lambda:
https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video

Or just run it with Ollama when the model appears:
https://ollama.com/search?q=llama%204

📝 LLAMA 4:
https://ai.meta.com/blog/llama-4-multimodal-intelligence/

📝 My paper on simulations that look almost like reality is available for free here:
https://rdcu.be/cWPfD 

Or this is the orig. Nature Physics link with clickable citations:
https://www.nature.com/articles/s41567-022-01788-5

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli GallizziIf y

## Транскрипт

### Segment 1 (00:00 - 04:00) []

Meta's new Lama 4 AI just dropped during the weekend and we'll see if it can survive a bunch of challenges. First, it goes without saying that the usual coding vibe check with the bouncing ball works well. Everyone can do that. Wait a minute. The handling of collisions was not exactly correct there. So, is this just a one-off cherrypicked result? Let's check again. H, there it goes again. Okay, so what is going on here? Well, here are two new AI models you can talk to, Scout and Maverick, available right now for free. And the bigger brother, Behemoth, is still training. He still needs some more apps in the gym. And it is also used to teach the smaller networks. And they have more surprises up the sleeve. When we give a bunch of data to the new Deep Seek, it recalls it nearly perfectly. All green. Imagine giving it a huge textbook and then asking questions. And when doing the same with lama 4, would you look at that? Is this worse? What happened? Dear fellow scholars, this is two minute papers with Dr. Koa. Well, this happened. Oh my goodness. This has a context length of 10 million tokens. That is nearly 80 times more data given to it than what DeepC can handle. That is an insane amount of data. You just chuck in about 10 hours of video and can ask questions about it. Wow. This is a unique property that none of the other AI systems have yet. For text, this feels like infinity. You can talk to it for years and years, and it will know your preferences and history really well. That is super useful. And yes, it is not perfect. So, it might occasionally forget about your wedding anniversary. That sounds like human intelligence to me. Scout and Maverick fit on a single graphics card, although it needs to be a beefy one. Or I just rent one at Lambda for cheap and run it there privately. With the long context window, you can give it a huge code base and ask for changes. And even though it is not the best at coding, there might be code bases where this is the only tool that can do anything at all. It looks wonderful on benchmarks too, but we don't take those too seriously around here. It uses a mixture of expert model. So, it is essentially a committee of little specialized AIS, and that also means that you don't need to have the whole neural network active at the same time. So, if you have a high-end MacBook Pro or Mac Studio with a little quantization, it runs really fast. The details are in the video description. So that's the good. Now the bad. There are already smaller independent studies that are stress testing the context memory and say not so fast. Also, it is not under an MIT license. So make sure to have a look at that. So what is it good for? Well, this is a free tool for big context projects. For many other things, whatever kind of quality and cost pair you desire, Gemini appears to be dominating the Parto Frontier. But for Lama 4, there is genuine innovation here. Loving the almost infinite text. And it also shows how spoiled we are with these cheap AI models. And great news, the future of AI is likely models that are free and open. Open science. Loving it. So what would you fellow scholars use this for? Let me know in the comments below. Here you see me running the full DeepSseek AI model through Lambda GPU cloud. 671 billion parameters running super fast and super reliably. This is insane. I love it and I use it on a regular basis. Lambda provides you with powerful NVIDIA GPUs to run your own chatbots and experiments. Seriously, try it out now at lambda. ai/papers AI/papers or click the link in the description.