CUTLASS 4.x provides a Python native interfaces for writing high-performance CUDA kernels based on core CUTLASS and CuTe concepts without any performance compromises. This allows for a much smoother learning curve, orders of magnitude faster compile times, native integration with DL frameworks without writing glue code, and much more intuitive meta-programming that does not require deep C++ expertise.

Overall we envision CUTLASS DSLs as a family of domain-specific languages (DSLs). With the release of 4.0, we are releasing the first of these in CuTe DSL. This is a low level programming model that is fully consistent with CuTe C++ abstractions — exposing core concepts such as layouts, tensors, hardware atoms, and full control over the hardware thread and data hierarchy.

CuTe DSL demonstrates optimal matrix multiply and other linear algebra operations targeting the programmable, high-throughput Tensor Cores implemented by NVIDIA's Ampere, Hopper, and Blackwell architectures.

We believe it will become an indispensable tool for students, researchers, and performance engineers alike — flattening the learning curve of GPU programming, rapidly prototyping kernel designs, and bringing optimized solutions into production.

CuTe DSL is currently in public beta and will graduate out of beta by summer 2026.

For more details please visit CUTLASS Documentation or CUTLASS GitHub.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (4.7.0)

Owner

PyPI data
Data sourced directly from PyPI's database.

Credits

Author: NVIDIA Corporation

License

Other/Proprietary License

Requires

Python >=3.10

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314t-manylinux_2_28_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.28+ x86-64

nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314t-manylinux_2_28_aarch64.whl (3.3 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.28+ ARM64

nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314-manylinux_2_28_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314-manylinux_2_28_aarch64.whl (3.3 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

nvidia_cutlass_dsl_libs_base-4.6.1-cp313-cp313-manylinux_2_28_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

nvidia_cutlass_dsl_libs_base-4.6.1-cp313-cp313-manylinux_2_28_aarch64.whl (3.3 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

nvidia_cutlass_dsl_libs_base-4.6.1-cp312-cp312-manylinux_2_28_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

nvidia_cutlass_dsl_libs_base-4.6.1-cp312-cp312-manylinux_2_28_aarch64.whl (3.3 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

nvidia_cutlass_dsl_libs_base-4.6.1-cp311-cp311-manylinux_2_28_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

nvidia_cutlass_dsl_libs_base-4.6.1-cp311-cp311-manylinux_2_28_aarch64.whl (3.3 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

nvidia_cutlass_dsl_libs_base-4.6.1-cp310-cp310-manylinux_2_28_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

nvidia_cutlass_dsl_libs_base-4.6.1-cp310-cp310-manylinux_2_28_aarch64.whl (3.3 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ ARM64

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314t-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314t-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 0a1bd67e0a387834389b81c1742a3af8c583fe2499fd01d8da9fc4354d4c322d
MD5 5de17c714909fcde6469beddc75e2650
BLAKE2b-256 f665e31b952a786f811b73e1eb831d198ce20af7ecc1cc5d201717756ad20156

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314t-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314t-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 5fd2c4051a9251009a978ecdb61381271f904dd44704fc7f2a8995777d87dbfa
MD5 e6de678922190a6d2ec6feae45863af1
BLAKE2b-256 5566bb63fca713c809005242a5683bd4c82b04603f004f8a8c98b9c4d043a73b

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 27461ff52338280b6c02475f87851273b5a11c387d9daa383376225a3f767cc2
MD5 c5add6e11ce78290c39e8411afd1b9d0
BLAKE2b-256 c8b56cf58671e7d9a9163da5fab2b171e3d30446df008635a1206ea4aca3d487

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 96815af98b19b8045802fd8b0a53d5e197e0d3bce42c7038e2861af7c8483f5d
MD5 055a1990bc94ed7c205228e2bb016000
BLAKE2b-256 3b0c949f122219839716ac34b5e11fa39fb1f0a40ba0609f59e095c52dccfa10

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 09a6aace841594ea39cf75f7cee68dc31b8cf3cdbadffcbd494f08fc73c6466d
MD5 ad40e99aff40cb70d5441345f277684f
BLAKE2b-256 f71de19ae692f4ed485f5026834801cd044007be6a11bd30f3dad407c1422fe5

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 7ca4e5ca9bd0caa2ec797ebb1e404bb3eb0f76d235823611795cb14d8d341e4c
MD5 6bb75776d46fee85fb69cfe7fdd67086
BLAKE2b-256 c1dd1d7f1f8ec9a0e91dbc42a9652fb5d087cea0470a81fdb5fb5642e8284faa

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 b1ffc932923fc7871be9d9890622c4a827fe64ab291f3660792b53f808f08801
MD5 845410f246b45539819cf6754f09af98
BLAKE2b-256 09dfe606ae9ea51b2dc7e8942177d9487cfb718ef819769cfe641588c976641b

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 e244956cb5097c0f9595a23fb204c7b6b1ef9ecb23db6389e5103605f7d324da
MD5 36dcc00fe21335b1d781954fbe45a539
BLAKE2b-256 2e113dfb742643e328f07348320a63211184f651bf13de04bc6e14c033a4a957

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 dcbbf471839801501030f1097ce13dbf5082ad34f26d6b503459c6fed078e4e9
MD5 aa4fda8be3f3c5e92da2a69ff438009d
BLAKE2b-256 b6cd259e288298c40675dbd03bd1ea7206640326ee314c6bc852186f7669b27c

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 617ff12b9a1fcbc3ec7397dea99380a72855a477c69c2421ce8a773966c127bb
MD5 c25c45725a562803ed116992fe3dfc50
BLAKE2b-256 656123834991d30145aa11021d6027774c475203926b50be0dcb0e3688c34f53

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e68ac160f75f79b690c267be3785cd35631d5bc147fc5c5a4e1902454bdd0d4c
MD5 d5210ef5b7ddac5cccb5b1b1dd33c76c
BLAKE2b-256 66b6455e0a508d6c3128e220f5c96dde45fffe4d9a750a246b64cbd9222d3916

See more details on using hashes here.

File details

Details for the file nvidia_cutlass_dsl_libs_base-4.6.1-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for nvidia_cutlass_dsl_libs_base-4.6.1-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 df002a6d01624bacfef3bb0b0096ef190a324e3951870ad9685ba9a3daf7ec78
MD5 88fef0f82a9cd4716a2d0e772b4a1b49
BLAKE2b-256 10b6d2665415a5a00abf0cfb33917329c23d8e7b5759950504394f0f10db3cf6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page