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AI SDK & OpenAI o3 Quantization Kernel Benchmarking Suite

Comparing FP8, INT4, and AWQ quantization kernels across inference hardware requires precise throughput measurements and accuracy degradation tracking. Build a TypeScript Vercel AI SDK pipeline powered by OpenAI o3 to automatically benchmark quantization kernels and compute performance-cost trade-offs.

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What you are building

Build an automated quantization kernel benchmarking suite using Vercel AI SDK and OpenAI o3 to evaluate FP8 and INT4 precision loss versus hardware speedup.

How work is evaluated

Evaluation

Evaluates kernel benchmark reporting accuracy and cost trade-off calculation fidelity.

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

speedup_validity

Ensures FP8 shows target throughput improvement over FP16 within reasonable bounds.

Binary check

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

Dimension 2

benchmarking_precision_score

Accuracy of benchmark prediction against standard empirical measurements • target: 0.96 • range: 0.9-1

Binary check

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

Learning goals

  • Use Vercel AI SDK streaming and object generation for automated benchmark reporting

  • Leverage OpenAI o3 for deep evaluation of precision loss metrics (perplexity/accuracy)

  • Benchmark Triton and CUDA kernel execution times across FP16, FP8, and INT4

  • Generate auditable cost-efficiency trade-off matrices for production deployment

Resources

Dataset notes

GEMM execution benchmark data gathered across FP16, FP8, and INT4 kernels.

Challenge setup

Evaluates kernel benchmark reporting accuracy and cost trade-off calculation fidelity.

Challenge input

JSON containing matrix_dimensions, precision_formats, batch_sizes

AI SDK

Vercel AI SDK for TypeScript structured generation and workflows

o3

OpenAI o3 model for complex technical benchmark analysis

Evaluated output

JSON containing latency_matrix_ms, memory_footprint_mb, perplexity_delta, recommended_format

Evaluation checks
  • Ensures FP8 shows target throughput improvement over FP16 within reasonable bounds.
  • Accuracy of benchmark prediction against standard empirical measurements • target: 0.96 • range: 0.9-1
Configured evaluation evidence
  • Benchmark: Triton GEMM Benchmark
  • Benchmarking Precision Score target: 0.96
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript 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
  • OpenAI o3 · Optional
Policy Serving
  • o3 · Optional

Evaluation contract

  • speedup_validity · Weight 1
  • benchmarking_precision_score · Weight 1

Metrics

  • benchmarking_precision_score · Target 0.96

Benchmarks

  • Triton GEMM Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing matrix_dimensions, precision_formats, batch_sizes

    Output: JSON containing latency_matrix_ms, memory_footprint_mb, perplexity_delta, recommended_format

    Execution harness: JavaScript

Evaluation tools

  • AI SDK · Agent Framework
  • o3 · Reasoning LLM

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about AI SDK & OpenAI o3 Quantization Kernel Benchmarking Suite