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AWS Neuron vs vLLM Serving Benchmark Harness with OpenAI Agents SDK

Deploying models safely across heterogeneous cloud environments requires benchmarking AWS Trainium/Inferentia against vLLM GPU targets. You will build an automated benchmarking agent using the OpenAI Agents SDK to execute parallel workload tests, measure throughput (tok/s), and calculate cost per million tokens. The agent must generate an auditable trade-off matrix with less than 3% execution variance.

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What you are building

Build an automated cross-hardware profiling agent using the OpenAI Agents SDK to evaluate AWS Neuron and vLLM target workloads.

How work is evaluated

Evaluation

Evaluates benchmarking agent's capability to compare AWS Neuron and CUDA endpoints accurately.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

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

math_correctness

Verifies calculated cost savings percentage strictly matches ground truth ratio

Binary check

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

Dimension 2

analysis_accuracy

Accuracy of cost and throughput comparison calculations • target: 0.99 • range: 0-1

Binary check

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

Learning goals

  • Construct OpenAI Agents SDK function calls to manage AWS Neuron and vLLM benchmarking pipelines

  • Execute reproducible synthetic token generation benchmarks across target instance types

  • Collect and normalize latency metrics (TTFT and inter-token latency ITL)

  • Synthesize an auditable deployment recommendation report

Resources

Dataset notes

Latency and throughput measurements recorded across AWS Inf2 and NVIDIA GPU instances.

Challenge setup

Evaluates benchmarking agent's capability to compare AWS Neuron and CUDA endpoints accurately.

Challenge input

JSON containing neuron_bench_data and vllm_bench_data with throughput, TTFT, and hourly_cost

OpenAI Agents SDK

Provides direct tool calling and execution loop orchestration.

Evaluated output

JSON returning optimal_target, cost_savings_pct, ttft_diff_ms, and trade_off_summary

Evaluation checks
  • Verifies calculated cost savings percentage strictly matches ground truth ratio
  • Accuracy of cost and throughput comparison calculations • target: 0.99 • range: 0-1
Configured evaluation evidence
  • Benchmark: Cloud-Inference-Cost-Bench
  • Analysis Accuracy target: 0.99
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

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

Evaluation contract

  • math_correctness · Weight 1
  • analysis_accuracy · Weight 1

Metrics

  • analysis_accuracy · Target 0.99

Benchmarks

  • Cloud-Inference-Cost-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing neuron_bench_data and vllm_bench_data with throughput, TTFT, and hourly_cost

    Output: JSON returning optimal_target, cost_savings_pct, ttft_diff_ms, and trade_off_summary

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about AWS Neuron vs vLLM Serving Benchmark Harness with OpenAI Agents SDK