VVE targets deterministic O(N²) eigensolving for structured symmetric matrices. It is measured against named LAPACK paths, accuracy-gated, and bit-reproducible across the supported machines and toolchains in scope. Start with an evaluation build, then measure your own workload.
For sixty-five years, the limiting step in eigendecomposition has been the same.
When the problem doubles, the work grows eightfold. At real sizes, that exponent decides which simulations, optimizations and certification workflows never leave the roadmap.
Faster chips don't change an exponent. More GPUs don't change an exponent. So we changed the mathematics. For matrices with structure, VVE EigenCore computes eigenvalues in O(n²). Not a tuned version of the old method. A different complexity class, and the advantage widens with every increase in problem size.
Scope: the O(n²) advantage is the eigenvalue / eigensolve phase on structured matrices. We show the curve, never a bare headline number.
At every N you can set here, the eigenvalue phase that defined the limit is computed in the time the old class spends warming up. Reproducible. Same result.
Then there's the second thing, quieter, and in the end more important than speed.
The result is deterministic. Run it on a laptop, a server or an ARM core and you get the same bits, every time. That one property, reproducibility, is what turns a fast answer into a provable one. It is the hinge the rest of this story turns on.
The evaluation starts by deciding whether the workload belongs here. VVE is strongest when structure, reproducibility and deployment constraints are real requirements.
Speed was never the point. When something that took twelve days finishes before lunch, you don't just save time, you change what you're allowed to attempt. And when the answer is provable, you change what you're allowed to ship. Five things were waiting on the other side.
A digital twin that keeps pace with the machine it mirrors. Structural health monitored as it happens, not reviewed after the fact. A thousand design candidates explored in the window that used to fit one.
These weren't slow before. They were off the table. O(n²) puts them on it.
Not faster. Newly possible.
Run the same model twice and you can get two different answers. The hardware reorders a few operations, the numbers drift, and the output moves with them. For a chatbot, that's harmless. For an autopilot, a diagnosis or a trade, it's the reason the door stays shut, because you cannot certify what you cannot reproduce. VVE CertifiAI closes the door behind it: every result bit-identical, verifiable by its SHA-256 fingerprint, the same on every machine.
Demonstration ready. It uses a fixed demo identity string; the determinism property is real.
Determinism is the precondition for certification: a bit-reproducible result is one a regulator can re-derive. That is the product property, not a claim.
If you can't reproduce it, you can't certify it. Now you can do both.
Whole markets were closed to AI not because the models weren't capable, but because their answers couldn't be proven. This is the key. The full technical evaluation, methodology, validated scaling anchors and conformance evidence are shared under NDA.
During eigendecomposition, much of a modern accelerator sits idle; the work simply doesn't map onto the silicon. The industry's answer has been to buy more of it, and pay for the power to keep it warm.
Our answer is to need less of it: O(n²) on the math, and a purpose-built silicon roadmap underneath. Same result. A different relationship with the meter.
Compute the bill the old approach was hiding from you.
Accelerator utilization during eigendecomposition: most tiles idle, all of them metered.
Illustrative, modeled on the divergence above; your figure is calibrated in a technical evaluation.
A model that only reads a sequence is structurally blind to them. VVE SpectraGuard extracts the frequency components that matter, selectively, deterministically and reproducibly, opening a certified view of a domain most AI never looks at.
It is not a faster transform. It is a different, verifiable lens on the same data, scoped for evaluation rather than asserted as a universal replacement.
A certified, deterministic view of the signal everyone else is missing.
Every serious optimizer wants to know the shape of the problem it's descending: the full curvature, not a guess. Computing that picture directly has often been too expensive, so the field approximates: a diagonal here, a low-rank sketch there, and the optimizer skids down the walls of a valley it can't quite see.
For structured cases, the evaluation question changes from whether the full picture is affordable to whether the harness shows enough advantage on your workload.
An accuracy-gated answer where the field often had only an estimate.
VVE doesn't sell a piece of software. We license the mathematics, the way ARM licenses the design inside most of the world's processors. One proven foundation, the O(n²) eigensolve, carries a stack above it. Every layer multiplies where the IP can be licensed; the foundation is already proven, and everything else compounds on top of it.
Every public number on this page is tied to a named comparison or a scoped demonstration. The mathematics is proprietary; we do not ask you to take broad claims on faith. We share the benchmark harness, signed manifests and technical review materials under NDA for teams ready to evaluate their own workload.
log-log fit · R²=0.9995 · to N=262,144 · AMD·Intel·ARM
Reproducibility, witnessed on-site
Deterministic by construction
Eigendecomposition · O(n³) since the early era
Benchmark harness, signed manifests and scope notes
Multiple families; details shared during appropriate diligence
Every number renders with its source. None of them is a headline multiplier; each one is read off a chart or a benchmark it cites.
Tell us the problem the old math kept off your roadmap.