Lossless 5-bit transformer compression for quant and trading AI
SR-11-7 model risk management requires demonstrable, auditable, reproducible AI inference. Current quantization (AWQ / GPTQ / EXL3) cannot bit-compare two runs. Sipsa proves SHA-256 verifiable bit-identical reconstruction across 22 architectures — the audit floor your OCC / Federal Reserve / FDIC examiner will demand.
The trading-AI inference problem
Every quant firm or trading desk using LLMs (research summarization, news interpretation, signals from text, agentic order routing) eventually faces the same regulator question: can you prove this model produced this answer for this input?
Current quantization frameworks deliver "approximately equal to the original" outputs. That language fails any SR-11-7 model-risk-management examination. It also fails after-the-fact compliance review when an AI-driven trading decision is questioned by a regulator or counterparty.
Sipsa's substrate is the only 5-bit-class compression with provable bit-identical reconstruction via SHA-256. Same model, same answer, every deploy, cryptographically verifiable. That moves your AI-trading inference from "approximately reproducible" to "audit-grade".
What Sipsa delivers for quant / trading customers
| Need | Sipsa delivery | Compliance hook |
|---|---|---|
| Bit-identical model behavior across deploys | SHA-256 verifiable reconstruction | SR-11-7 model risk; OCC examination |
| Reproducible after-action review | Per-Linear SHA-256 manifest + customer-side uc verify | Compliance audit; regulator examination |
| Lower per-strategy GPU footprint | 3-4× less memory at sub-1.5% PPL drift | More concurrent strategies per GPU-hour |
| On-prem / air-gapped trading desk deploys | BUSL-1.1 + Additional Use Grant; no cloud dependency | Trading firms cannot ship orderbook data to public APIs |
| Frontier-scale model on smaller infrastructure | 405B-class fits on single 32 GB consumer GPU | Per-trader research desk economics |
Verified at scale
22 architectures verified end-to-end, 40 model artifacts at huggingface.co/SipsaLabs, customer-side reproducible:
pip install ultracompress hf download SipsaLabs/qwen3-14b-uc-v3-bpw5 --local-dir ./qwen3-14b uc verify ./qwen3-14b # confirms bit-identical reconstruction uc bench ./qwen3-14b # measures TTFT / tokens/sec / VRAM
Phase 0 POC for quant / trading AI teams ($5K–$25K, 1 week)
We compress one of your production models (or a public model you're evaluating). 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. Compatible with on-prem / air-gapped trading desk deploys.
FAQ
Will Sipsa work with our existing inference stack (vLLM / TensorRT-LLM / sglang)?
Sipsa reconstructs the original model bit-identically before any inference framework runs. So yes — once reconstructed, the model is a standard PyTorch checkpoint that drops into any inference stack you already use.
What's the inference latency overhead?
Reconstruction happens at load time (~5-10 sec for a 70B-class model). Subsequent inference uses the standard PyTorch path on the reconstructed model — no per-token overhead. The win is GPU-memory: 3-4× lower footprint = more concurrent strategies per GPU-hour.
Can we use Sipsa for backtesting reproducibility?
Yes — this is one of the strongest fits. Backtesting an AI-driven strategy requires bit-identical model behavior across simulator runs. Sipsa lets you ship the same compressed artifact to research, paper trading, and production with provable equivalence at every stage.