# pytorch/FBGEMM

FB (Facebook) + GEMM (General Matrix-Matrix Multiplication) - https://code.fb.com/ml-applications/fbgemm/

Repository: https://github.com/pytorch/FBGEMM
Canonical: https://ross.abutalabs.com/products/fbgemm
Language: C++
License: NOASSERTION
License Family: other
Last push: 2026-08-26T17:43:50+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 82, longevity 100
- inputs: {"age_days": 2900, "days_push": 7, "days_rel": 42, "gap_med": 75.5, "n_releases_24m": 9}
- 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 1584, forks 777 (observed 2026-08-28T04:05:07.382557+00:00)

## What it is
FBGEMM is a collection of highly optimized low-precision matrix multiplication and convolution kernels for server-side deep learning inference, published as FBGEMM (CPU), FBGEMM_GPU, and FBGEMM-GenAI packages. It serves as the backend for PyTorch's quantized x86 CPU operators and provides GPU operators for recommendation systems and generative AI workloads.

## Use cases
- run low-precision quantized inference on x86 CPUs
- speed up matrix multiplication for small batch sizes
- quantize models with row-wise or outlier-aware quantization
- train and serve recommendation system models on GPUs
- use FP8 quantization for generative AI inference
- accelerate PyTorch quantized operators

## When to choose
- you need high-performance low-precision GEMM on x86 server CPUs
- you are deploying PyTorch quantized models
- you build recommendation systems with embedding operations on GPU
- you need FP8 kernels for GenAI inference

## When to avoid
- you need a high-level training framework rather than kernels
- you target ARM or other non-x86 CPUs for the CPU library
- you want plug-and-play quantization without writing C++/CUDA integration

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-inference, gpu-computing, benchmarking
- domain: deep-learning, machine-learning, large-language-models, gpu-computing, performance
- platform: cpp, python
- tags: quantization, matrix-multiplication, inference-kernels, recommendation-systems, low-precision, pytorch, fp8, simd, gpu, linux, cuda

## Member repositories
- pytorch/FBGEMM (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:07.382557+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 2026-08-30T03:55:58.489512+00:00, confidence not recorded.
  - readme: https://github.com/pytorch/FBGEMM (fetched 2026-08-28T04:05:07.382557+00:00, sha b5bb54ca359b)
- Data as of 2026-08-30T08:39:29.467469+00:00.