Questions.

No. The math comes from physicists who simulated quantum systems on ordinary computers. It runs on ordinary GPUs, and no quantum hardware is involved.

The memory a training run takes up on the GPU. Training has to hold the model and everything needed to update it at the same time, and memory is usually what runs out first. We're working on more than one part of that, and we measure each part on its own.

Capacity. A model that didn't fit on a GPU now fits, and the same GPU holds a bigger training job. We don't claim faster training or a dollar saving.

You can, and it helps until the next model arrives. Models have been growing much faster than the memory on a single GPU, so the question that lasts is how much training you can get out of each one.

Quantization rounds numbers down to fewer bits, and teams accept some lost quality to make things fit. Our methods are designed to keep that loss small. The math looks for the pattern in what's stored and keeps the pattern. We check the result against an uncompressed run on held-out data.

It can, and compressing harder usually costs more speed. We measure speed alongside memory and quality, so you can see the trade-off and choose the point that works for your team.

Both. Whichever you run, the GPU has to hold the model and what it needs to update it, and that's where we work.

We've tested on common open model families so far. How much a specific model saves is something we measure. We don't guess at it.

No, we keep how it works in-house. As results come in, we'll publish what each one saves and what it costs, along with the model, hardware and settings behind every number.

It starts with a conversation. Tell us what you train, what you train it on, and what you've been designing around to make it fit. hello@akavion.com