---
id: pr-deepgemm-42
repo: deepseek-ai/DeepGEMM
pr: 42
title: 'Performance: reducing the percentage of FFMA interleaving yields a sight performance gain, roughly 0.5%'
author: sazczmh
date: '2025-03-05'
url: https://github.com/deepseek-ai/DeepGEMM/pull/42
source_category: upstream-code
architectures:
- sm90
tags:
- benchmark_test
- compiler_runtime
- gemm_quant
- recent-kernel-path
techniques:
- fine-grained-quantization
hardware_features: []
kernel_types:
- compiler
- gemm
languages:
- cuda-cpp
captured_at: '2026-05-16'
status: merged
merge_sha: 9d3222a93e9637f6fb1cf2538199738c1eee23aa
inclusion_reason: Mine shape sets, tolerance rules, warmup logic, and profile commands before mutating code.
changed_paths:
- deep_gemm/jit/interleave_ffma.py
artifact_dir: evidence/pull-bundles/deepgemm/gh-42
head_ref_oid: fcd1dcd99d00106c8ca818948c4b1676df6d91bc
---
# Performance: reducing the percentage of FFMA interleaving yields a sight performance gain, roughly 0.5%

- Repository: `deepseek-ai/DeepGEMM`
- PR: [42](https://github.com/deepseek-ai/DeepGEMM/pull/42)
- 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

- `deep_gemm/jit/interleave_ffma.py`

## Evidence Bundle

`evidence/pull-bundles/deepgemm/gh-42`
