Quant & research
Auditable attention paths. Measured energy. Public packs.
Infrastructure for deterministic, receipt-attested, energy-aware AI compute — with numbers you can open on GitHub.
Core capabilities
TRADE residual stacks
Device-resident GPU stacks for multi-layer residual compute. Public 7B-class path: 28 layers × h=3584 on H100 NVL with multi-run thr and sustain-only J/token.
AUDIT & receipts
Where the product lane demands it: cryptographic receipts, free-ride residual checks, and null-space behavior under load — not just marketing “determinism.”
WNSM free-ride
Null-space payload bus under real CUDA load. Public pack: free-ride vs side-channel H2D, null residual ~1e-8 class, single-layer null-inject drift 0 in the stack test path.
Long-context O(N) memory
Waller streaming state scales ~O(N) in memory vs dense scores ~O(N²). Public ladder through 32k (and analytical 131k memory reduction).
Convert + serve path
HF → Luxi native convert for 7B-class weights; serve and TRADE examples for operator evaluation. Implementation lives in the engineering repo; proof is public on LuxiDemo.
Deterministic math demo
Standalone binary: JSON expressions in, results + SHA256 out. Useful for integration smoke tests — secondary to the inference-energy thesis on this site.
Where we win vs where we are honest
| Axis | Status | Public proof |
|---|---|---|
| 7B-class board J/token @ seq≥128 | ~0.63 J/tok · ~403 tok/s | h100-7b-class-TRADE |
| Short-seq thr vs Flash | Flash-class may win (published H2H loss) | h100-stack12-H2H |
| Free-ride / AUDIT under load | Differentiated thesis | h100-WNSM-free-ride |
| Long-ctx memory scaling | O(N) vs O(N²) | h100-LONGCTX-scaling |