Waveform
ANE made the product possible. Waveform makes it deployable.
Waveform is C++ middleware that instantiates Vectris’s Adaptive Neural Efficiency framework across live AI inference infrastructure. It converts observed runtime state into ANE-governed execution decisions and measures the result as ComputeYield™.
Product definition
A software control plane between the serving stack and GPU execution.
Waveform deploys alongside the existing inference environment. It does not require model retraining, model-weight changes, GPU-kernel modification, application rewrite or proprietary hardware.
The operator retains the same model, weights, serving environment and quality standard. Waveform governs execution and measures customer-specific net outcomes against the optimized baseline.
Observe → Evaluate → Govern → Measure → Produce evidence
Serving frameworks
Waveform — powered by ANE
GPU, cluster and fabric execution
ComputeYield and evidence receipts
Control layer
How Waveform works.
01
Observe
Read live workload, execution, memory and infrastructure state through the serving environment.
02
Evaluate
Apply the ANE framework to determine whether structural recovery exists and which action is admissible.
03
Govern
Execute the bounded ANE-authorized runtime decision across the relevant GPU, multi-GPU, cluster or fabric surface.
04
Measure
Compare accepted throughput, quality, latency, energy, counters, repeatability and controller overhead with baseline.
05
Produce evidence
Record the measured outcome and report ComputeYield through an auditable evidence receipt.
One product surface
Runtime control through evidence reporting, in one product.
Execution
Runtime execution control
Runtime execution control with memory and working-state governance.
Scale
Multi-GPU and cluster coordination
Multi-GPU and cluster coordination plus network and infrastructure-fabric behavior.
Observability
Counters and safeguards
Observability and counter collection with quality safeguards and fairness.
Evidence
Receipts and reporting
Controller-overhead accounting with evidence receipts and ComputeYield reporting.
Customer outcomes
Four ways the measured result becomes business value.
Save CapEx
More accepted fleet output
Increase accepted fleet output and potentially defer part of the next procurement cycle.
Save OpEx
Less energy and time
Reduce energy and wall-clock time per completed, quality-equivalent workload.
Create revenue capacity
Sell recovered capacity
Convert recovered GPU-seconds into additional sellable inference output.
Expand margin
More output per GPU-hour
Increase accepted output per GPU-hour while preserving infrastructure headroom.
Evidence boundary
Published results identify their environment.
Published results identify the relevant hardware, model, workload, baseline and configuration. Vectris-run results have not yet been independently reproduced in customer production. Outcomes vary, and every customer deployment begins with customer-specific measurement.