Editing Openai/692db884-c2d8-800c-a357-aa2a0e684fbe (section)

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Here’s the honest answer:
* There are many tools that get pieces of the benefit: - Quantization, pruning, sparse kernels → reduce memory & compute. - ZeRO / FSDP / offload → extend effective capacity with CPU/NVMe. - Paged KV / custom engines → smarter layout and paging. - Compressed memory research → transparent capacity boosts.
* But I am not aware of a widely used, off-the-shelf system that: 1. Hooks CUDA at this level, 2. Applies specialized tensor + semantic compression, and 3. Is meant to be transparent to the rest of the ML stack (no model code changes, no retrain) in exactly the way your LoreToken-GPU hook is trying to do.

So in terms of goals and architecture, this is pretty unique:
* It’s closest to an LLM-specific compressed VRAM subsystem that sits under your framework.
* From the perspective of the rest of the system, its ideal behavior is: >

That “don’t touch the rest of the system” constraint is exactly what makes it special—and also what makes it hard.

If you want, next step we can do is:
* Describe this in two paragraphs of investor language, or
* Turn it into a small block diagram explanation for README / LinkedIn, still without showing code.