UltraCompress

Extreme compression for large language models. Patent pending — USPTO 64/049,511 + 64/049,517

Run language models on less hardware than they were supposed to need.

UltraCompress is the patent-pending compression infrastructure for transformer language models. The Track A method targets sub-3 bits per weight — ~30% smaller than bitsandbytes NF4 with zero catastrophic failures on a 6-model head-to-head cohort in our internal benchmark. The CLI is shipped on PyPI today; pre-compressed reference models roll out on Hugging Face Hub through April–May 2026.

Who this is for

• Engineers running into the 4-bits-per-weight cliff that public methods (bitsandbytes, GPTQ, AWQ, HQQ) fall off below 4 bpw
• Product teams targeting on-device deployment — phones, cars, robots, embedded systems
• Inference platforms whose margins are GPU-memory-bound at scale
• Hardware partners (chip vendors, OEMs) evaluating compression infrastructure for licensing

v0.1 alpha: pre-compressed reference models are uploading to Hugging Face Hub throughout April–May 2026. Run uc list for the live catalog. Examples below show expected post-launch usage.

Install

pip install ultracompress

Quickstart

# Today: scripted demo (no Hub artifacts required)
uc demo

# Today: query the live HF Hub catalog (returns "No pre-compressed models
# published yet" until the first rolling-release artifact lands)
uc list

# Post-artifact example usage (works once an artifact is on the Hub):
uc pull sipsalabs/<model-id>
uc info ./models/<model-id>
uc bench ./models/<model-id> --tasks hellaswag --limit 500

What's available today (v0.1 — alpha)

The CLI itself is shipped on PyPI. The Hugging Face Hub catalog is rolling out through April–May 2026; until the first reference compressed model lands, uc list against the live Hub returns "No pre-compressed models published yet."

• uc demo — scripted CLI demo for screen recording (works without any Hub artifacts).
• uc list — query the live sipsalabs collection on the Hugging Face Hub. Returns the actual current catalog; expect "no models published yet" until the first rolling-release artifact lands.
• uc pull <model-id> — download a pre-compressed model when one is available on the Hub.
• uc info <path> — inspect the compression metadata of an already-downloaded artifact.
• uc bench <path> --tasks <list> — run downstream benchmarks via lm-eval-harness on a downloaded artifact.

What's coming (v0.2 — Q3 2026)

• uc compress <hf-model-id> --bpw 2.8 — self-compression (gated on patent prosecution timeline).
• uc serve <path> — inference server with OpenAI-compatible API.
• uc export --format gguf — export to llama.cpp GGUF format.
• uc export --format coreml — export to Apple CoreML for on-device inference.

Why UltraCompress

The 4-bit-per-weight cliff

Every public LLM compression method (bitsandbytes, GPTQ, AWQ, HQQ) is stable at and above 4 bits per weight. Below 4 bpw, model quality falls off a cliff — most methods produce models whose downstream-task accuracy collapses to near-random. We measure this with a T_cat threshold; on a 6-model cohort, public sub-3-bpw methods produce catastrophic failures on the majority of the cohort.

UltraCompress doesn't.

Track A — post-training row-overlay quantization (USPTO 64/049,511) — shipping now

On a 6-model × 8-method × 500-sample head-to-head benchmark:

Method	Bits per weight	Cohort median T1 retention	Catastrophic failures
bitsandbytes int8	8.000	99.75%	0/6
bitsandbytes nf4	4.000	98.31%	0/6
HQQ 4-bit g64	4.500	97.72%	0/6
UltraCompress 2.8 bpw	2.798	95.63%	0/6
HQQ 3-bit g64	3.500	72.46%	1/6
HQQ 2-bit g64	2.500	3.46%	6/6

Top-k retention curves (top-1, top-10, top-32, top-64, top-128, top-256) will ship in the per-model card on each artifact's Hugging Face Hub repository as the reference compressed models roll out through April–May 2026. T1 alone is the wrong metric for autocomplete, candidate generation, or RAG re-ranking — most customer use cases care about top-k structure.

Track B — Fractal Residual Recursion (USPTO 64/049,517) — v0.2 (Q3 2026)

Architectural compression beyond published academic ratios for transformer language models. Combined with Track A on the v0.2 stack: the strongest end-to-end ratio we've measured for transformer language model architectures in our cohort. Gated on patent prosecution timing.

Track B evidence is separate from Track A shipping artifacts; see docs/evidence/matrix.md for Track B detail. Do not combine retention numbers across tracks as a single quality curve.

Patent status

The UltraCompress compression methods are the subject of pending U.S. patent applications. Pre-compressed models are distributed under a separate licensing arrangement described in LICENSE. The CLI code in this repository is Apache-2.0.

Reporting issues, security, and commercial inquiries

• Bugs and feature requests: open an issue.
• Security vulnerabilities: see SECURITY.md — report privately to security@sipsalabs.com.
• Commercial / design-partner / pilot inquiries: founder@sipsalabs.com.
• Patent / licensing: legal@sipsalabs.com.

Contributing: see CONTRIBUTING.md. Changes that touch packaging, CI, docs, and the public CLI surface are very welcome. Pull requests adding the proprietary compression methods will be closed.

Citation

@misc{sipsalabs2026ultracompress,
  title        = {UltraCompress: Extreme Compression for Large Language Models},
  author       = {{Sipsa Labs, Inc.}},
  year         = {2026},
  note         = {U.S.\ patent applications 64/049,511 and 64/049,517, patent pending},
  howpublished = {\url{https://sipsalabs.com}}
}

About

UltraCompress is built by Sipsa Labs — a research lab spanning Systems · Intelligence · Precision.

Patent pending — USPTO 64/049,511 + 64/049,517.