The efficiency layer for AI. We build compression and systems that fit any model onto the hardware you already have — near-losslessly, and with reconstruction you can verify.

Near-lossless 5-bit transformer compression (~1% perplexity cost; the 5-bit pack is lossy) with reproducible, SHA-256-verifiable reconstruction to the validated artifact — a deterministic decode back to the exact quantized weights we evaluated, not bit-identical to the original bf16 model. A 405B-parameter model runs end-to-end on a single 32 GB consumer GPU at a 1.0066× perplexity ratio.

pip install ultracompress

• UltraCompress — the compression engine (public CLI on PyPI).
• Sipsa Inference — OpenAI-compatible API serving compressed weights (api.sipsalabs.com/v1).
• Compression-as-a-Service — bring a model, get a verified pack you run yourself.

23 architectures verified end-to-end (22 PPL-verified + 1 ViT cosine; 0.6B–405B; dense + MoE + SSM + ViT) — reproducible public artifacts, not internal benchmarks. Hermes-3-Llama-3.1-405B reconstructs at 1.0066× on a single 32 GB GPU.

Models are outgrowing the hardware that runs them. Whoever makes any model run anywhere — cheaper, faster, provably intact — becomes the layer every AI deployment passes through. We start where verifiable quality is non-negotiable, and expand outward.

Built in public while the patents are pending. BUSL-1.1 — free for sub-$1M ARR + research.

Commercial: founder@sipsalabs.com · sipsalabs.com · PyPI