Extracting more value from AI infrastructures

Your fleet is already paying for capacity it never uses.

Waveform is a control plane designed to capture ComputeYield™ and turn it into additional productive AI output — no retraining, no weight changes, no kernel modification, no new hardware.

Request a ComputeYield™ Audit → Explore the Technology

Real siliconH100 | H200 | B100 | B200

WorkloadsLlama 8B | Mistral 7B

DeploymentNo retraining | No weight changes | No kernel modification

The operating problem

Busy is not the same as productive.

A GPU-hour is billed in full whether or not it produces quality-equivalent accepted output. Placement, state movement, memory pressure and scheduling behaviour consume the difference.

Vectris measures that gap and applies governed runtime control to recover productive work.

Operator baseline1.00 GPU-hour billed

Governed by Waveform1.00 GPU-hour billed

Accepted output Trapped capacity

Illustrative structure. Segment proportions are not measured values — every customer Audit produces the real decomposition for the selected workload.

The governing foundation

ANE made Waveform possible.

Adaptive Neural Efficiency is Vectris’s invariant-driven governing framework for productive AI inference. ANE defines how runtime state is interpreted, which control actions are admissible and what evidence must exist before a gain can be accepted.

Waveform is the product that instantiates ANE across deployed AI infrastructure.

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Application and modelunchanged

Serving frameworkunchanged

Waveform — powered by ANEinserted here

GPU, cluster and fabric executionunchanged

ComputeYield™ and evidence receiptsnew output

The product

Waveform governs live execution between the serving stack and GPU execution.

Waveform observes live workload and infrastructure state, applies ANE-governed runtime decisions and measures accepted throughput, latency, energy and controller overhead against the operator’s optimized baseline.

Waveform

An additive control layer, not a replacement.

Waveform deploys alongside the existing environment rather than replacing the model, application, serving framework or hardware.

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Real-silicon evidence

Up to 73% more throughput on selected Mistral tests.

Vectris measured Waveform using the ANE framework on commercially available NVIDIA GPUs in third-party RunPod cloud infrastructure. Results are workload-specific and Vectris-measured.

H100 · Mistral 7B

Throughput+73%

Energy consumed−55.5%

Wall-clock−42.3%

H200 · Mistral 7B

Throughput+30%

Energy consumed−51.6%

Wall-clock−23.3%

B200 · Mistral 7B

Throughput+34%

Energy consumed−50.7%

Wall-clock−21.5%

Bar length shows the magnitude of change against the operator’s optimized baseline, not the direction. Energy and wall-clock reductions are improvements.

B100 is included in the validation set; a separate public point estimate is not disclosed.

Measured results to date are on NVIDIA hardware. Support for AMD, Intel and Samsung accelerators is coming soon.

Evidence boundary: Vectris-run results have not yet been independently reproduced in customer production. Results vary by model, workload, hardware and configuration. Every customer Audit measures customer-specific net outcomes.

The deliverable

Every gain ships with a receipt.

The Audit returns a workload-specific record: the baseline it was measured against, the change observed, the controller overhead already netted out, and the configuration needed to reproduce it.

ComputeYield receiptSample layout — not customer data

workload: selected inference workload · batch · context length
accelerator: measured silicon · environment
serving stack: operator-optimized baseline configuration
window: measurement start and end, UTC

throughput: baseline → governed
energy per accepted token: baseline → governed
wall-clock: baseline → governed
controller overhead: netted out of every figure above
quality gate: accepted-output parity held

Verdict recorded · reproducible from the stated configuration

Layout shown for illustration. Values are populated from the customer’s own measured Audit.

Commercial entry

Measure first. Deploy only against demonstrated value.

The engagement begins with a paid ComputeYield Audit. Vectris measures a selected workload against an optimized baseline, quantifies the economic capacity resident in the fleet and defines the controlled path to Waveform Runtime deployment.

01AuditAdvances on a measured gap

02Economic caseAdvances on your own numbers

03Governed deploymentAdvances on parity held

04Measured expansionAdvances on a verified gain

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Next step

See Waveform against your own baseline.

Share your infrastructure environment, selected workload and operating objective. Vectris will define the evidence required for a customer-specific ComputeYield case.

Request an Audit →