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

==== Short answer: ====
🧠 Pieces of this exist separately in the wild.
⚙️ A fully general, drop-in, “semantic GPU memory compressor” like this is not something I’m aware of as a normal product/library.

Let’s compare to what does exist:

===== Things like: =====
* 8-bit / 4-bit quantization libraries
* Pruning / structured sparsity
* LoRA / low-rank factorization

These absolutely reduce memory and often increase effective capacity.

But:
* They require explicit changes to checkpoints, model definitions, or training pipelines.
* You don’t just “slip a .so in front of CUDA and get 3–7x VRAM” – you opt into a specific format and usually retrain or at least re-calibrate.

So: similar goals, but not transparent, and not plug-and-play at the CUDA boundary.

===== DeepSpeed ZeRO, FSDP, and similar: =====
* Let you run huge models by: - Offloading some states to CPU/NVMe - Sharding weights/gradients across multiple GPUs.

They achieve “I can fit bigger models than my raw VRAM suggests,” but:
* They change your training/inference stack explicitly.
* Require configuration, wrapping models, or specific APIs.
* Usually focus on distributed setups, not “single 3090 pretending to be H100.”

So: conceptually comparable in “effective capacity” but not the same mechanism, and not a transparent GPU-hook layer.

===== Libraries like vLLM and others: =====
* Use paged KV caches and smart memory layouts.
* Let you serve more concurrent requests / longer context on the same VRAM by paging things in/out efficiently.

Again:
* You switch to a custom inference engine.
* You don’t just bolt it in under PyTorch without touching anything.

So: memory smart, but not a generic, driver-level compression shim.

===== There is prior art around: =====
* Compressed RAM on CPUs (zram, zswap).
* Research and some vendor work on GPU compressed memory and “buddy compression”: - Where parts of VRAM or backing store are transparently compressed to increase effective capacity.

These are:
* Very similar in philosophy: “present a larger virtual memory pool via transparent compression.”
* Usually implemented at the driver/hardware/OS level, not as a userland LLM-specific hook.
* Not generally exposed as: “drop this user .so in and your LLM fits now.”

So: closest conceptual cousin, but:
* Not LoreToken-aware
* Not specialized for LLM tensors
* Not something you casually LD_PRELOAD into an existing stack.