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-2026.8.5-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-2026.8.5-cp314-cp314-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.5-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-2026.8.5-cp313-cp313-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.5-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-2026.8.5-cp312-cp312-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.5-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-2026.8.5-cp311-cp311-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.5-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-2026.8.5-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-2026.8.5-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.5-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 66111cf6999a0751034aa07f6c50ed30fab74917f8373925a15d7a77c61c686e
MD5 135d50df51fe5452b151d46a93a9084f
BLAKE2b-256 8d8f631a9d0ec0a18d78edaa1921c7840acc7a152f763338ab26951acde1cf27

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.5-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.5-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 c95f35a0281439e77ebea19779c46d82f4f8046d2a841180b95345fcb625aa9c
MD5 9239d0d9acf2d3c13c1242d2a0d9aee5
BLAKE2b-256 2af2cda4db95fc3ff4adb349f92dd6bae3a4b73b81f511681fb381a5d534e156

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.5-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.5-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 59789e44eb9ebddf85a7a4cb56909716f1266d32fa4dd08bc3364b2f09efc2bc
MD5 5205efd2a62b57564b59b7dc49e52469
BLAKE2b-256 0ced15d1d373c07d83fcfa81cd744f2dba26c66567e9b42052379e20c04c615e

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.5-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.5-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 409cc1a1e9c7a2a70d31245bf02cf2ad364a2da3916ed952531c3e181c78fdf0
MD5 bf13c566d8ad8f2b9f42810e8f5589c7
BLAKE2b-256 e00d611d523b45afab26657f304c22a2a4344450bbc3ce9247e885a688e50784

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.5-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.5-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a92efd7acb666d70aef167af5e6b19980ae2a085a4b79c2e08d790d55b098209
MD5 5bba4df4398e8b02aedac37297bd8f9d
BLAKE2b-256 686d240c8e879c3ba1b8463fccba2e79ce5112eea1a1cbdf70895bdf9706b6d6

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.5-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.5-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 a01ff8afafc06be4990b3d5ca70c83596dbd3c6cc43cfaad45734679f77d31e6
MD5 94431d74580fa467c4a561c9dd43abfa
BLAKE2b-256 dd3dc1735e2b5df580cb033475449749883b09afa5186cd52202778456311376

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.5-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.5-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 3f7d29a8409f0cf3aeb21036801a4670b211b399dce6c0c796e39e79b142a039
MD5 2b68cf11fd5091b62d6130a155dda710
BLAKE2b-256 4def3cb3968bc92b0e47a3141a749ac02252f61b6aacc571aca21eb5bda40e11

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.5-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.5-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 dc8c6051d64ece26243644ed4f7b3f0f62272752bd1be6bb76727e53d3381b3f
MD5 5ce4c955fcdeeae8d99d5f57c9cb8407
BLAKE2b-256 334cec0b809240f3c46ede99326159e2be9b5745ec05e98c480b2d9d2c259208

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.5-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.5-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 eb53f2f5230c80c302fc94dce9884d52da6a33b4a43bd38cb3bc1ff3ec904f7c
MD5 b1a36089b122b531fc2aaefa77f5d572
BLAKE2b-256 2b60f707a26e3ad75f718e7e1ec193b50a6c8bf81e491347a6a528b640dbb3c1

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.5-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.5-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 3f3a9df8f37423e62126895d1f83d33322a6961528919788efa12cdddbfbc4c6
MD5 d1c9411329e3676353edcda112a66042
BLAKE2b-256 c3858c5b88b619a9ec2f6cdeb57bebb83a6f9cac143baba5347140cff13a8a67

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