GLM 5.3 Flash is a free, open-weight AI model that generates images, designs games, and creates 3D scenes by using only 5% of its 320 billion parameters per token. It cuts memory costs through linear attention, sparse attention, and an index pool that compresses long-range context, making powerful AI dramatically cheaper to run.
I am running this AI at home, and it can do light simulations computing a beautiful image like this. Look at that! Yummy caustics too. Oh my. It can write up a fun strategy game. It can model a beautiful full 3D scene in free and open source software like Blender. It is incredibly good. And anyone can have all this at the grand total of, let me see, hmm, nothing! Yes! It's free and Open weights. This is called GLM 5.3 Flash. Plus its big brother, GLM 5.3. Flash was first stealthily released under a different name, and I mean, what? Look at that! It quickly overtook even DeepSeek in usage. I understand, novelty and all, but this is crazy. And now, hold on to your papers Fellow Scholars, because if you let it think for a while, both the bigger and smaller models can get up close to Fable level on some benchmarks. In my opinion, this does not feel like Fable level in general, but in some experiments, it is not that far away. I can't believe that I am saying this, but I think within this year, in a few months, Fable might be surpassed by free AI systems. What a time to be alive! Okay, so how is this black magic possible? How did they do it? Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér. Well, GLM-3.5 Flash is about cramming more intelligence into less compute. 320 billion parameters, about 95% of which is not activated per token. The number of layers was 92 before, now it's roughly cut in half. And it has some new tricks up the sleeve. Instead of comparing every token to every other one, it can summarize nearby context into a small package. They call this linear attention. And it is dramatically cheaper than sparse attention, which they also use. Also, ever experience talking to an AI in a session for a long while, and as you talk longer and longer, it gets worse and worse? Yes! If you have a bunch of documents, a huge codebase, or a long discussion, searching through that context gets really expensive. This can compress an index of this stored context before searching it, so the model can look back far further and use less memory and compute. They call this index pool. And if you add this all together, you have a system that is built from the ground up to be fast, smart, and inexpensive. Now, it still needs beefy hardware in the order of thousands of dollars. But what I love about you brilliant Fellow Scholars is that you are always tirelessly working on making it run on more modest hardware. This is once again an open weight system, a project that we should work on together and add to it, so thank you so much to all of you who contribute by improving it, anyone who runs experiments with it to share with us, or just by talking about it and spreading the word. We have some of the most amazing audience on this channel, and it is an honor to make these videos for you. This is paper video number 1072. And I am having more fun with you than ever. This is why I said no to millions of dollars from private equity firms that wanted to buy our channel. Every single other one they approached, I know and heard about, was sold. But we didn't sell. Although, I would have a great deal better hardware, that's for sure. Yes, about that. The main limitation for me here is, of course, hardware. You need lots of firepower to run this, it's not in the billions anymore, which is fantastic. But it's still not cheap. I also don't have all the hardware to run the big one at home, Lambda helps though. So, many of us will use smaller, compressed, quantized versions, and these can start looping like crazy, like it did for me here. So, do not expect perfection, but expect to have a damn good time tinkering. And I am incredibly grateful for this. Thank you so much!
I use Lambda to reproduce AI research papers, often in minutes. It's also great to train your own models, or fine-tune an existing one. Run inference, or text to image or video?
Easy peasy.
Running a DeepSeek chatbot or agent?
Super fast, super reliable.
Lambda gives you powerful NVIDIA GPUs to run your own experiments. I test ideas from the papers I cover, and moments later, results.
Love it!
Seriously, try it out now at lambda.ai/papers.
