# NVIDIA/cutlass

CUDA Templates and Python DSLs for High-Performance Linear Algebra

Repository: https://github.com/NVIDIA/cutlass
Canonical: https://ross.abutalabs.com/products/cutlass
Homepage: https://docs.nvidia.com/cutlass/index.html
Language: C++
License: NOASSERTION
License Family: other
Topics: cuda, deep-learning, deep-learning-library, cpp, nvidia, gpu, python
Last push: 2026-08-26T10:48:43+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 99, longevity 100
- inputs: {"age_days": 3199, "days_push": 7, "days_rel": 7, "gap_med": 12, "n_releases_24m": 32}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10317, forks 2046 (observed 2026-08-28T04:10:41.569770+00:00)

## What it is
CUTLASS is NVIDIA's collection of CUDA C++ template abstractions and Python DSLs for implementing high-performance GEMM and related linear algebra computations on NVIDIA GPUs. It provides reusable, tunable kernel building blocks supporting many data types across Volta through Blackwell architectures.

## Use cases
- write custom high-performance GEMM kernels for NVIDIA GPUs
- prototype CUDA kernels in Python without deep C++ expertise
- implement mixed-precision matrix multiplication with FP16, BF16, FP8, or FP4 types
- integrate optimized tensor core kernels into deep learning frameworks
- tune tiling sizes and data movement strategies for GPU performance

## When to choose
- you need maximum GEMM or linear algebra performance on NVIDIA tensor cores
- you are developing custom CUDA kernels for deep learning or HPC
- you want a Python DSL for GPU kernel programming with C++-level performance
- you need support for the latest NVIDIA architectures and low-precision data types

## When to avoid
- you just need a drop-in BLAS library rather than kernel building blocks (use cuBLAS)
- you target non-NVIDIA GPUs or CPUs
- you want a simple high-level API without performance tuning

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, gpu-computing, math, compiler
- domain: deep-learning, gpu-computing, machine-learning, performance
- platform: cpp, python, windows, cross-platform
- tags: cuda, gemm, linear-algebra, tensor-cores, kernels, cute-dsl, nvidia, template-library, algorithms, gpu, linux

## Member repositories
- NVIDIA/cutlass (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:41.569770+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:19:10.942594+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA/cutlass (fetched 2026-08-28T04:10:41.569770+00:00, sha 8828c718bc13)
  - homepage: https://docs.nvidia.com/cutlass/index.html (fetched 2026-08-29T08:18:29.260212+00:00, sha b74e75cd5c5c)
  - site_page: https://docs.nvidia.com/cutlass/latest (fetched 2026-08-29T08:18:29.269377+00:00, sha cc6bf3a690df)
- Data as of 2026-08-30T08:39:29.467469+00:00.