Lossless 5-bit transformer compression for healthcare AI inference
FDA Software-as-Medical-Device requires bit-identical model behavior across deploys. AWQ / GPTQ / EXL3 leave reproducibility ambiguous — you cannot bit-compare two runs. Sipsa proves SHA-256 verifiable bit-identical reconstruction across 22 architectures, every deploy, every customer-bound model.
The healthcare-AI inference problem
Every healthcare-AI customer that ships a clinical-decision-support model under FDA Software-as-Medical-Device (SaMD) regulation hits the same compliance wall: regulators require provable reproducibility of model behavior.
Current quantization frameworks (AWQ, GPTQ, EXL3, QTIP) deliver "approximately equal to the original" model output. That language fails FDA Computational Model Validation. It also fails HIPAA model-risk audit when a healthcare provider needs to demonstrate why the same patient input produced a different inference output across two deploys.
Sipsa's substrate is the only 5-bit-class compression that proves bit-identical reconstruction via SHA-256, every run, every deploy. That moves a clinical AI deploy from "approximately reproducible" to "cryptographically verifiable" — the regulatory-equivalence floor your FDA submission needs.
What Sipsa delivers for healthcare AI customers
| Need | Sipsa delivery | Compliance hook |
|---|---|---|
| Same model, same answer, every deploy | SHA-256 verifiable bit-identical reconstruction | FDA SaMD; FDA Computational Model Validation |
| Auditable model versioning | Per-Linear SHA-256 manifest + customer-side uc verify | HIPAA model risk; OCR audit |
| Smaller GPU footprint per clinical deploy | 3-4× lower memory at sub-1.5% PPL drift | Per-clinician TCO reduction at hospital scale |
| On-prem deploy (HIPAA / no-cloud customers) | BUSL-1.1 + Additional Use Grant (no cloud dependency) | Air-gapped hospital + clinic deploys |
| Defense / pharma / biotech adjacency | Same substrate also used for AFWERX / NIH SBIR / proteomics | Cross-vertical platform credibility |
Verified at scale
22 architectures verified end-to-end, including 405B-class dense (Hermes-3-Llama-3.1-405B at 1.0066× PPL on a single 32 GB GPU), MoE (Mixtral-8x7B at 1.00368×), and state-space (Mamba-2.8B). 40 model artifacts at huggingface.co/SipsaLabs, customer-side reproducible:
pip install ultracompress hf download SipsaLabs/qwen3-8b-uc-v3-bpw5 --local-dir ./qwen3-8b uc verify ./qwen3-8b # confirms bit-identical reconstruction uc bench ./qwen3-8b # measures TTFT / tokens/sec / VRAM
Phase 0 POC for healthcare-AI teams ($5K–$25K, 1 week)
We compress one of your production models, deliver the lossless artifact + SHA-256 manifest + customer-side uc verify dashboard. You confirm bit-identical reconstruction against your bf16 reference. If we miss the spec, you don't pay. Phase 1 commercial license follows if Phase 0 lands.
FAQ
How is this different from AWQ / GPTQ / EXL3?
Those frameworks leave reproducibility ambiguous — you cannot bit-compare two runs. Sipsa's substrate produces a per-Linear SHA-256 fingerprint that the customer can verify locally. For FDA / HIPAA / SR-11-7 audit purposes, "bit-identical" is qualitatively different from "approximately equal".
Does this require special hardware?
No. Standard PyTorch path on the reconstructed model — runs on any CUDA GPU. We have customer-side reproducibility on consumer GPUs (RTX 5090) and datacenter GPUs (A100 / H100) alike.
What about NIH SBIR alignment?
Sipsa Labs has a NIH SBIR Phase I draft submission-ready with FDA bit-identical reproducibility as the science-merit hook. Healthcare-AI customers who partner with us at Phase 0 may be cited as Letters of Support in the NIH submission, accelerating both timelines.