---
id: pr-flashinfer-3203
repo: flashinfer-ai/flashinfer
pr: 3203
title: Include TinyGEMM into BF16 autotuner
author: askliar
date: '2026-05-05'
url: https://github.com/flashinfer-ai/flashinfer/pull/3203
source_category: upstream-code
architectures:
- sm100
tags:
- attention_kv
- benchmark
- benchmark_test
- cutlass
- gemm_quant
- scheduler_autotune
- sm100
techniques:
- autotuning
- fine-grained-quantization
- tile-scheduling
hardware_features:
- blackwell
- pdl
kernel_types:
- attention
- gemm
- scheduler
languages:
- cute-dsl
captured_at: '2026-05-16'
status: merged
merge_sha: e250b471f84842822fec06f6a825f29bbfc119b5
inclusion_reason: Mine shape sets, tolerance rules, warmup logic, and profile commands before mutating code.
changed_paths:
- benchmarks/routines/gemm.py
- flashinfer/gemm/gemm_base.py
- tests/gemm/test_mm_bf16.py
artifact_dir: evidence/pull-bundles/flashinfer/gh-3203
head_ref_oid: d0b4f9ffbab42e4b5cce5a1396852ff5ebf6c416
---
# Include TinyGEMM into BF16 autotuner

- Repository: `flashinfer-ai/flashinfer`
- PR: [3203](https://github.com/flashinfer-ai/flashinfer/pull/3203)
- Decision: `Mine shape sets, tolerance rules, warmup logic, and profile commands before mutating code.`
- Reason: Mine shape sets, tolerance rules, warmup logic, and profile commands before mutating code.

## Changed Paths

- `benchmarks/routines/gemm.py`
- `flashinfer/gemm/gemm_base.py`
- `tests/gemm/test_mm_bf16.py`

## Evidence Bundle

`evidence/pull-bundles/flashinfer/gh-3203`
