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.
What you are building
The core problem, expected build, and operating context for this challenge.
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
Evaluates kernel benchmark reporting accuracy and cost trade-off calculation fidelity.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
speedup_validity
Ensures FP8 shows target throughput improvement over FP16 within reasonable bounds.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
benchmarking_precision_score
Accuracy of benchmark prediction against standard empirical measurements • target: 0.96 • range: 0.9-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
Reference links and supporting material
GEMM execution benchmark data gathered across FP16, FP8, and INT4 kernels.
How this agent runs
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
- 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
- Benchmark: Triton GEMM Benchmark
- Benchmarking Precision Score target: 0.96
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- OpenAI · Required
- OpenAI o3 · Optional
- o3 · Optional
Evaluation contract
- speedup_validity · Weight 1
- benchmarking_precision_score · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.