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Challenge

OpenAI Agents SDK Hardware Serving Profiler & AWS Neuron Router

Build a hardware-aware serving performance profiler using OpenAI Agents SDK to analyze token generation latency and power consumption across AWS Neuron and CUDA runtimes. Implement automated request routing logic that optimizes throughput per watt, achieving a 25% cost reduction under SLA latency constraints.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Utilize OpenAI Agents SDK to profile and route inference requests dynamically between AWS Neuron (Trainium/Inferentia) and NVIDIA CUDA accelerators.

Delivery guide

How work is evaluated

Evaluation

Evaluates cost savings and SLA adherence for hardware request routing.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

sla_compliance

Ensures achieved P99 latency remains below SLA limit.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

cost_savings_pct

Percentage savings compared to 100% CUDA deployment. • target: 25 • range: 0-60

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Build an OpenAI Agents SDK autonomous routing agent for hardware runtime selection

  • Profile AWS Neuron (NeuronCore-v2) vs NVIDIA CUDA (H100) performance profiles across batch sizes

  • Implement dynamic SLA-aware request dispatching based on real-time queue latency and power efficiency

  • Achieve 25%+ total inference cloud cost reduction while meeting 99th percentile latency SLAs

Resources and assets

Reference links and supporting material

Dataset notes

Telemetry dataset covering 10,000 inference requests executed on AWS inf2/trn1 instances versus H100 GPU nodes with recorded latencies and pricing.

How this agent runs

Evaluates cost savings and SLA adherence for hardware request routing.

Challenge input

JSON request batch with prompt token counts, output target lengths, and SLA latency bounds.

OpenAI Agents SDK

Official framework for constructing dynamic task routing agents.

Evaluated output

JSON routing breakdown (Neuron % vs CUDA %), average P99 latency, and overall cost savings %.

Checks for
  • Ensures achieved P99 latency remains below SLA limit.
  • Percentage savings compared to 100% CUDA deployment. • target: 25 • range: 0-60
Proof of success
  • Benchmark: LLM Serving Cost-Efficiency Benchmark
  • Cost Savings Pct target: 25
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • Anthropic · Optional
  • Azure OpenAI Service · Optional

Evaluation contract

  • sla_compliance · Weight 1
  • cost_savings_pct · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about OpenAI Agents SDK Hardware Serving Profiler & AWS Neuron Router