Agent-assisted model porting and inference optimization

What the source reports

Deepgram and AWS FDE engineers report porting the Nova-3 speech model to Trainium2. They used NAD profiling skills to investigate bottlenecks and kernel-writing skills to help produce replacement kernels. Engineers checked layer outputs against a CPU reference and end-to-end word error rate against Deepgram’s targets. The Rust serving integration reproduced reference token sequences at every tested batch size.

Reported technology: AWS Trainium2, Neuron and Neuron Agentic Development (NAD). Scope and outcomes are source-reported.

What the enterprise environment would need

The following is a proposed project design for an enterprise doing similar work. Published details are marked; the remaining systems, data and scenarios would be agreed for your project.

Model and serving stack

Proposed: use an approved model, pinned compiler and runtime versions, the enterprise’s serving interfaces and a permitted accelerator configuration. Agree hardware access explicitly; a substitute cannot establish performance on the target hardware.

Data and reference results

Proposed: use permitted audio and transcripts, fixed evaluation splits, expected outputs and representative batch sizes. Keep a separate test set that the coding agent cannot change.

Engineering responsibilities

Proposed: the agent investigates profiles and suggests kernel changes. Engineers review the code, numerical differences and integration failures. The model and service owners set acceptance criteria and decide whether to roll out a change.

A proposed simulation

An illustrative practice design derived from this work, not an executed assessment.

Port a permitted speech model to an agreed target runtime, use an agent to investigate one performance bottleneck, and submit the change with reproducible correctness and performance results.

Review gate: A reviewer other than the coding agent reruns the checks from the pinned inputs. The accountable owner reviews failed cases before accepting the change.

Challenge the result: Introduce a faster kernel that changes recognition results, then a batch size that exceeds a resource limit. The checks should expose both failures.

What needs checking

• Compare outputs and word error rate with the agreed reference. Reject faster kernels that breach correctness tolerances.
• Confirm operations run on the intended accelerator. Test compilation, memory limits and the serving interface across agreed batch sizes.
• Measure throughput and resource use on the same workload and hardware baseline. Retain failures, versioned results and the reviewer’s decision.

Review environment fidelity, human capability and agent performance separately. The enterprise’s accountable owners decide acceptance of the shared workflow and any operating use.

Shape this around your enterprise

Bring a similar workflow and the people responsible for it. Together, define the records and systems to use, the result your team will build or configure, and how its owners will check it. Nuvepro scopes the simulation, environment and preparation. Agree any existing-agent evaluation, implementation assistance and production responsibilities separately.

Sources and limitations

Curation review: 20 October 2026. This is the date this source example was reviewed, separate from source publication dates.

• This is a joint operator and vendor account, not an independent audit. Results concern the reported offline batch configuration.
• Agent assistance in engineering does not establish autonomous operation, clinical suitability, model fine-tuning or general production readiness.
• The environment and checks proposed here have not been built or executed by Nuvepro. Hardware, model rights, data access and implementation scope would need agreement.

1. AWS: How Deepgram optimized voice AI at scale with AWS TrainiumSource publication: 2026-10-19