AI Performance Engineering & FinOps

AI economics are unforgiving: GPU capacity is expensive, and most estates run well below their potential because utilization, latency and cost are measured separately or not at all. Novera Intelligence unifies performance and cost engineering into one practice — because tokens per second and cost per inference are two views of the same system.

We profile your workloads at every layer, benchmark against explicit targets, and implement the cost governance that makes AI spending deliberate and defensible.

The Challenges We Solve

• Low GPU utilization

  Expensive accelerators idle at low percentages due to scheduling gaps, IO bottlenecks or misconfigured serving. We profile end to end and fix the real constraint.

• Latency regressions

  Time-to-first-token and inter-token latency degrade as traffic and model versions change. We establish continuous benchmarking so regressions surface immediately.

• Unexplained cost growth

  Bills grow faster than usage. We attribute cost to teams and workloads, identify waste, and implement rightsizing and governance.

• No performance baseline

  Without measured baselines, every capacity decision is a debate. We install benchmarking practice so decisions are grounded in numbers.

What We Deliver

• GPU utilization profiling

  Deep profiling of training and inference workloads to locate the true bottleneck: compute, memory bandwidth, interconnect or storage.

• Inference benchmarking

  Latency and throughput benchmarking against explicit targets — time-to-first-token, inter-token latency and tokens per second under realistic concurrency.

• Training throughput engineering

  Optimization of data pipelines, communication patterns and checkpoint strategy to raise effective training throughput.

• GPU memory & KV-cache optimization

  Memory management, cache sizing and quantization strategies that raise concurrency within the same hardware envelope.

• CPU & storage bottleneck analysis

  End-to-end analysis of the non-GPU layers — tokenization, preprocessing, IO and networking — that silently cap performance.

• Workload placement

  Placing workloads on the right infrastructure tier — cloud, on-premises, spot or dedicated — based on performance and cost profiles.

• Infrastructure rightsizing

  Continuous alignment of capacity to demand, with sizing models updated from measured utilization.

• Cost per inference

  Unit-economics modeling that expresses AI cost per request, per token or per task, comparable across infrastructure options.

• Cloud TCO modeling

  Total-cost comparison of deployment options including commitments, egress and operational overhead.

• AI cost governance

  Budgeting, quota, attribution and approval workflows that make AI spend deliberate across the organization.

Architecture in Layers

Performance and cost in one loop

1. Measure

   Continuous telemetry across GPU, serving, application and cost dimensions.

2. Attribute

   Per-team and per-workload cost and utilization accounting.

3. Analyze

   Profiling and benchmarking that locates bottlenecks and waste.

4. Act

   Rightsizing, placement, tuning and architectural changes with measured before/after.

5. Govern

   Budgets, quotas and approval workflows that keep spend aligned to value.

How We Engage

Delivered through a disciplined lifecycle that carries the initiative from assessment to dependable, optimized operation.

1. 01

   Baseline

   Profiling and benchmarking to establish current performance and cost baselines.

2. 02

   Diagnosis

   Bottleneck and waste analysis across compute, serving, data and placement.

3. 03

   Optimization

   Targeted engineering changes with measured results against the baseline.

4. 04

   Governance

   Cost attribution, budgets and review cadences installed with finance and platform teams.

5. 05

   Continuous

   Ongoing optimization cycles as workloads and models evolve.

Deliverables You Can Expect

• Performance baseline and profiling report
• Inference benchmark suite and results
• Rightsizing and placement recommendations
• Cost-per-inference and TCO models
• Cost governance framework and dashboards

Use Cases

• Inference economics program

  An enterprise reducing cost per request through serving optimization, quantization and placement — with unit economics tracked continuously.

• Cluster utilization uplift

  A GPU estate lifted from low utilization to a scheduled, measured, high-efficiency operation.

• Cloud cost control

  A runaway cloud AI bill brought under governance with attribution, quotas and rightsizing.