Skip to main content

FBGEMM_GPU

FBGEMM_GPU-CPU CI FBGEMM_GPU-CUDA CI FBGEMM_GPU-ROCm CI

FBGEMM_GPU (FBGEMM GPU Kernels Library) is a collection of high-performance PyTorch GPU operator libraries for training and inference. The library provides efficient table batched embedding bag, data layout transformation, and quantization supports.

See the full Documentation for more information on building, installing, and developing with FBGEMM_GPU, as well as the most up-to-date support matrix for this library.

Join the FBGEMM_GPU Community

For questions, support, news updates, or feature requests, please feel free to:

For contributions, please see the CONTRIBUTING file for ways to help out.

License

FBGEMM_GPU is BSD licensed, as found in the LICENSE file.

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.

fbgemm_gpu_nightly_cpu-2026.8.8-cp314-cp314-manylinux_2_28_x86_64.whl (6.3 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.8.8-cp314-cp314-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.8-cp313-cp313-manylinux_2_28_x86_64.whl (6.3 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.8.8-cp313-cp313-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.8-cp312-cp312-manylinux_2_28_x86_64.whl (6.3 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.8.8-cp312-cp312-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.8-cp311-cp311-manylinux_2_28_x86_64.whl (6.3 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.8.8-cp311-cp311-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.8-cp310-cp310-manylinux_2_28_x86_64.whl (6.3 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.8.8-cp310-cp310-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ ARM64

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.8-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.8-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 50fe0cecfc6559ee2b4f1ff4ef482281bf5b3d38e37620df8f1d38e3a7655bca
MD5 ff248547bf2ed013217283d6bd05e466
BLAKE2b-256 16401e5d6083a3a39095161008bdc3e9c8c2b7c9ef02ba65c9828178e6a4bd94

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.8-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.8-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 9fb35cc17ccf37df4ca894619122d17e3bba4c7d4e74b21da52eebd0eda5c6ec
MD5 7cf27ed099ee97b675bce9909eacee03
BLAKE2b-256 4ba333e4632e89b7587921171538953dc73fd2acf681be4fb2c534b4e7ec097e

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.8-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.8-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 706afe5c914db99b943173b7fea7d6ce1722add87be0f1e7df578da9ce8738fe
MD5 92df731f54a91a33fa4e31bcf396a89e
BLAKE2b-256 22ebb16bf575f9f19ca4ac0dee52d7457045ffe9f3192a65534881bee7298df7

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.8-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.8-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 d483abf9be400fabc131c61c42b3bc21add117f5facb0b92398fce7b13baa483
MD5 ecd7b16006427c7009fc356a3720f249
BLAKE2b-256 0ec56bb676ce1fac6fad351c95f246b432836dbd1a2eb099a792d45346269535

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.8-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.8-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 ee25fc2c78381518fdad396b3fc5e047a3751fd3e6d7a1223c04d21776e5898b
MD5 5c111725b114dc6502c931486645f9e0
BLAKE2b-256 587fe300747afe0f024b1673dac18ddfebb38f48d48fa0636ef64cfaf84ad591

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.8-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.8-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 f11de10050812b94b9134360b16c9340099c68761e5372ebdb8bbbd59a394d1f
MD5 c7e5d191b281bc89f4cc7da9b427f053
BLAKE2b-256 781c17937e0c0581a6ad16e88d46ef59db787a9d6d29f1294441f0927571b319

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.8-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.8-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 0d6aa0a8e3f20df98e7002df1543050efade2ba5046f4a4fcabbea0bfcf32533
MD5 d0bf8cd0ad6bc9ba2b5af8f2e14fc1ba
BLAKE2b-256 e1057ca1bb01433f2bd4032043d90c626005e206dae8c871f9240858c95bef99

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.8-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.8-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 02999b18e8cdc569232ea54ccc041ecf686b7b1bb12fa1999aaa573b41ab580f
MD5 2a93a385a2a2cb85511e7686b5ac7dc9
BLAKE2b-256 698949e91012891864f47545aff829fa690164974745dda6954e29d458d8aef7

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.8-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.8-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 1ae3abeed771c7946fe141a9a4e134f0872da6e9496944e34f495f647f753125
MD5 afd9f999a381103cf4b525c06abdf7ba
BLAKE2b-256 8fca00109d02bda71ac347088ae76d26666b450ecc5ed5e676ba0d8c60573e58

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.8-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.8-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 afbb77b5d001a9f26612c1342d24f6a6ad486dfbc00571183f42ea2707a956e4
MD5 fe991d95fe7becf7ff3667847821329d
BLAKE2b-256 09422712dad425bfc2257fb3aece9f9d58833083edad0925ada39d5137ad6223

See more details on using hashes here.

Release history Release notifications | RSS feed

Supported by

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