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NVIDIA's Neural Texture Compression Cuts VRAM Use From 6.5 GB to 970 MB
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this post was submitted on 04 Apr 2026
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Nvidia
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Nvidia Corporation is an American multinational corporation and technology company. It is a software and fabless company which designs and supplies graphics processing units (GPUs), application programming interfaces (APIs) for data science and high-performance computing, as well as system on a chip units (SoCs) for the mobile computing and automotive market. Nvidia is also a dominant supplier of artificial intelligence (AI) hardware and software.
Nvidia's professional line of GPUs are used for edge-to-cloud computing and in supercomputers and workstations for applications in such fields as architecture, engineering and construction, media and entertainment, automotive, scientific research, and manufacturing design. Its GeForce line of GPUs are aimed at the consumer market and are used in applications such as video editing, 3D rendering and PC gaming. The company expanded its presence in the gaming industry with the introduction of the Shield Portable (a handheld game console), Shield Tablet (a gaming tablet) and Shield TV (a digital media player), as well as its cloud gaming service GeForce Now.
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This is actually a good idea.
Latent encoding/decoding is basically extreme, "good enough" image compression. It's a pretty old ML technique, and the models to do it can be small (eg fast-ish). A cross-platform implementation is doable.
If I were a game dev, I wouldn't overuse it, but it seems like a great way to store huge, somewhat less frequently-used textures in VRAM.
...But one thing I'm wondering is what they decode to? If its a huge bitmap (instead of a compressed texture format), that seems suboptimal. And I don't think it's decoded per pixel like BCx compression, unless I'm misremembering how it works. That granular access is kind of the point of texture compression.
That’s one thing that’s been bothering me a lot about this AI worship era that I don’t see mentioned enough anywhere. Machine learning is a fucking incredible tool that can surely be used to do a lot of things in a novel or better way. Instead all of the investment and eyeballs are on overplayed party tricks
That's what OpenAI wants.
They want "Our AI vs no AI." They want to stoke the simplistic ML hate because the thing they fear most is it being viewed for what it is: dirt cheap, dumb, albeit useful tools. Like a set of specialized hammers.
It's what I keep trying to tell the "Fuck AI" crowd, but no one wants to hear it, and they're playing right into what these stupid Tech Bros want.