NVIDIA's New Free AI - A Gift To Humanity

[00:00] This AI is not Neotron 3 Super. No, this

[00:05] is Neotron 3 Ultra, Nvidia's newest free

[00:09] and open AI model, and I've been

[00:11] delighted, disappointed, and confused by

[00:14] it. But I think I got it now. You see,

[00:17] you can look at the benchmarks all you

[00:19] want, but we are fellow scholars here.

[00:21] We don't just believe stuff. We test it

[00:24] for ourselves. That is the way of the

[00:27] scholar. So, I had an early look at it

[00:29] and ran some of my experiments day and

[00:32] night. First impression is that it is

[00:34] incredibly fast. Blazing fast. Love

[00:38] that. But then my coding experiments did

[00:40] not go that well. When I ask it to write

[00:43] a light simulation program, this is my

[00:45] original area of research and I get a

[00:47] black screen. Nothing. When I ask it to

[00:50] fix it, it does a bunch of things and

[00:52] same. And then I said, "Okay, let's

[00:55] debug this by hand." It had some

[00:57] mistakes. After fixing that, well, we

[01:00] get something. But maybe it's a scene

[01:02] that does not work at all. Other even

[01:05] smaller systems can do this task with

[01:07] relative ease. And the other thing is,

[01:09] goodness, it wrote up more than a

[01:12] thousand lines of code. You don't need

[01:15] that much. My handwritten solution from

[01:17] my research is about 250 lines and

[01:20] renders this scene. Fully open source,

[01:23] free for everyone, forever. Now, let's

[01:26] write a realtime strategy game. Yes. Oh,

[01:29] no.

[01:31] Black screen again. Almost. We got a

[01:33] square. But if you ask Deepseek 4 Flash

[01:36] with the same prompt, you get something

[01:39] really cool. But not here. So, what is

[01:41] going on here? Well, I went back and

[01:43] forth with Nvidia and reported some of

[01:46] the issues and later there were some

[01:48] improvements. But still, this kind of

[01:50] coding is not something I would

[01:52] personally use this for. So I said, you

[01:54] know, maybe let's not use this AI. But

[01:57] then I thought, wait, it is super fast

[02:00] and probably good at other things. So I

[02:02] gave it aic things. Fixing broken

[02:05] installations on my machine from the

[02:07] terminal, excellent. Whipping up quick

[02:10] experiments, organizing files,

[02:12] excellent, super fast. And over time, I

[02:15] found myself reaching out to it more and

[02:18] more. And I found it to be useful

[02:21] basically for everything other than

[02:23] challenging coding tasks. Now that is

[02:26] excellent because this might be the

[02:28] openest AI model ever. Weights are open.

[02:31] The research paper on how it was made is

[02:34] open. Training data and recipes are

[02:36] being released at least for the

[02:38] redistributable parts. Now that is

[02:40] pretty crazy. Now hold on to your papers

[02:43] fellow scholars because it gets even

[02:45] better. Licensing. Super important

[02:48] question, very overlooked. We are always

[02:51] hoping for Apache 2.0. This is the do

[02:54] whatever you want license. For me, this

[02:57] is 10 out of 10. Now, Nvidia started

[03:00] publishing their models under their own

[03:02] proprietary license, which I would rate

[03:05] 7 out of 10. Derivative works and

[03:07] commercial use is fine. On the other

[03:09] hand, it needs a bit of attribution and

[03:12] a little stricter on patent grants. Now,

[03:15] this has the open MDW license. This is

[03:19] basically Apache 2.0 tailored for

[03:22] machine learning weights. This is

[03:25] absolutely fantastic news. Glorious. I

[03:29] think this might be a 9 out of 10, maybe

[03:32] as close to 10 out of 10 as you can get

[03:35] from a big company like Nvidia. Allows

[03:38] basically everything, but less battle

[03:40] tested. And my understanding is that if

[03:42] you sue claiming this model infringes

[03:45] your rights, you lose the license. Huge

[03:48] improvement. Double thumbs up. Thank

[03:50] you. Now, can you run it yourself? Hm.

[03:53] Um, yes and no. Yes, because completely

[03:56] open. Download it. It is yours forever.

[03:59] No limits, no funny business. However,

[04:02] no, because I would love to run it

[04:04] locally, too. But it's huge. 550 billion

[04:09] parameters. You need hundreds of

[04:11] gigabytes of GPU memory for that. This

[04:13] is why I will probably use it on Lambda.

[04:16] Also, 1 million token long context

[04:18] window.

[04:20] Great. Have a larger code base with a

[04:22] bug hiding somewhere. No worries.

[04:25] Massive box. Easy. Okay. How about

[04:28] images and videos? Well, it does not

[04:30] have vision capabilities. Not multimodel

[04:33] text only. Oh man, how much I would love

[04:39] a multimodel version of this. Goodness,

[04:41] please.

[04:43] Okay, and I also had a realization. You

[04:45] don't need one model to do everything.

[04:48] You need a roster of models that cover

[04:51] your use cases. For instance, I can't

[04:54] add vision capabilities to Neatron 3

[04:56] Ultra, but I can bolt Gemma 4 to it with

[05:00] a screwdriver. It's like a seeing eye

[05:02] dog guiding a smarter blind man along.

[05:06] It is hilarious and it kind of works.

[05:09] Kind of. So, we finally have more

[05:12] competition in the open AI model space

[05:14] and that is glorious. So, how does it

[05:17] work? Well, one trick is that it is

[05:19] huge, but not all of it runs at once.

[05:23] 550 billion parameters total, but only

[05:26] about 10% of that is active per token.

[05:30] These are specialist mini brains that

[05:32] are being activated at a time. We call

[05:34] that mixture of experts. But you wise

[05:38] fellow scholars know that already. So

[05:40] what else? Now they also use mambber

[05:43] layers. Why member? Is this like a snake

[05:46] or like the fruity chew? I don't know. I

[05:50] don't even know why I brought this up.

[05:52] So what do these do? Well, traditional

[05:54] AI systems have a bit of a memory

[05:57] problem. They work like a student who

[06:00] constantly rereads the textbook over and

[06:03] over again when they are given a

[06:05] question. But memory is precious. So

[06:08] instead read the book only once and take

[06:11] highly compressed notes. So this kind of

[06:14] memory remembers important details about

[06:17] the conversation. However, it is also

[06:19] smart enough to throw away the filler

[06:22] words. Thus, this system can process

[06:25] massive amounts of data efficiently. It

[06:28] also uses low precision numbers, so you

[06:30] have to do less number crunching when

[06:33] running this. They call it NVFP4. And

[06:36] this doesn't rely on predicting tokens

[06:38] one by one. No, it has multiple heads

[06:42] that draft multiple future tokens at the

[06:45] same time. Once again, many things that

[06:48] make it blazing fast. And we get all of

[06:51] this for free forever. What a time to be

[06:54] alive. Thank you to everyone who worked

[06:57] on this and absolutely everyone

[06:59] everywhere who is working on open-source

[07:01] projects and open models. You are all

[07:04] heroes. And look, this system is great,

[07:07] but it could be tiny. It could be bad,

[07:10] ugly. I don't care. As long as it is

[07:12] open science and open models, it pushes

[07:15] humanity forward. Thank you. What a time

[07:18] to be alive. Here you see me running the

[07:21] full Deepseek AI model through Lambda

[07:24] GPU cloud. 671

[07:28] billion parameters running super fast

[07:30] and super reliably. This is insane. I

[07:34] love it and I use it on a regular basis.

[07:37] Lambda provides you with powerful Nvidia

[07:40] GPUs to run your own chatbots and

[07:43] experiments. Seriously, try it out now

[07:45] at lambda.ai/papers AI/papers

[07:48] or click the link in the description.