Neural Nova Joins NVIDIA Inception: Advancing Performance Ownership in AI Systems

We’re excited to announce that Neural Nova has been accepted into the NVIDIA Inception program, NVIDIA’s global initiative supporting startups building advanced technologies in AI and accelerated computing.

For Neural Nova, this milestone is not about branding — it’s about accelerating our ability to deliver performance ownership on real hardware, at a time when AI systems are becoming too complex to tune manually.

From Optimization to Performance Ownership

Modern AI performance is no longer determined by a single kernel, model, or GPU. It emerges from the interaction of:

• Models and operators

• Kernel implementations

• Hardware architecture

• Runtime behavior

• Deployment constraints

Yet performance is still treated as something teams “optimize” periodically — not something they own continuously.

At Neural Nova, we’re changing that.

We define performance ownership as the ability to:

• Measure performance continuously on real hardware

• Detect regressions as systems evolve

• Automatically generate and apply fixes

• Maintain performance guarantees over time

This is the problem space we operate in.

Deployment Units (DUs): Making Performance Ownable

A key concept behind performance ownership is the Deployment Unit (DU).

A DU represents a concrete, production-relevant scope where performance can be measured and enforced consistently. It encapsulates:

• A specific AI workload or pipeline

• Target hardware and system configuration

• Runtime constraints and performance expectations

By scoping performance to a DU, Neural Nova can:

• Generate and select optimized kernels tailored to that deployment

• Evaluate performance directly on the target GPUs

• Track performance behavior over time

• Automatically respond to regressions caused by code changes, model updates, or hardware refreshes

This makes performance repeatable, auditable, and enforceable — not just optimized once and forgotten.

Why NVIDIA Inception Matters for This Approach

Acceptance into NVIDIA Inception directly supports our work on performance ownership and DUs:

• Hardware-aware validation
  Access to NVIDIA’s developer ecosystem and tooling helps us evaluate kernel behavior and performance characteristics accurately on modern GPUs.

• Faster iteration on real hardware
  As GPU architectures evolve, having close alignment with NVIDIA’s software stack accelerates our ability to validate and adapt DUs without breaking performance guarantees.

• Stronger foundations for continuous enforcement
  NVIDIA’s tools and SDKs complement our focus on real-hardware measurement, which is essential for enforcing performance over time rather than relying on static benchmarks.

This alignment enables us to build systems that remain robust as both models and hardware change.

What This Means for Neural Nova Users

For teams using Neural Nova, this milestone reinforces our commitment to:

• Production-grade performance, not synthetic benchmarks

• Autonomous kernel generation and selection, evaluated on target GPUs

• Continuous performance tracking and recovery within each DU

• Reduced operational risk as hardware and software evolve

In short: performance that holds, not just performance that peaks once.

Looking Forward

With support from NVIDIA Inception, we will continue to deepen our focus on:

• Expanding automated kernel coverage across AI workloads

• Strengthening regression detection and recovery mechanisms

• Improving performance reporting and traceability at the DU level

Our long-term belief is simple:
AI performance should be continuously owned, not periodically tuned.

This is the foundation we are building toward.

Thank You

We’re grateful to the NVIDIA Inception team for the opportunity to be part of this program and to collaborate within a global ecosystem advancing accelerated computing.

We’re excited about what lies ahead.