# flagos-ai/FlagGems

FlagGems is an operator library for large language models implemented in the Triton Language.

Repository: https://github.com/flagos-ai/FlagGems
Canonical: https://ross.abutalabs.com/products/flaggems
Language: Python
License: Apache-2.0
License Family: permissive
Topics: pytorch, triton, triton-kernels
Last push: 2026-09-03T01:31:53+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 90, longevity 64
- inputs: {"age_days": 896, "days_push": 0, "days_rel": 70, "gap_med": 28, "n_releases_24m": 6}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1089, forks 509 (observed 2026-09-03T02:15:11.968029+00:00)

## What it is
FlagGems is a high-performance operator library for large language models written in the Triton language, providing backend-neutral GPU kernels. It registers with PyTorch's ATen backend so developers can accelerate training and inference across diverse hardware platforms without changing their PyTorch APIs.

## Use cases
- accelerate LLM training and inference on non-NVIDIA accelerators
- replace CUDA kernels with portable Triton kernels in PyTorch models
- run PyTorch workloads across multiple AI chip backends without code changes
- generate pointwise operators automatically for arbitrary input types and layouts
- write and contribute GPU kernels in Triton instead of CUDA

## When to choose
- you need hardware-portable PyTorch operator acceleration across diverse AI accelerators
- you want Triton-based kernels with PyTorch API compatibility via ATen registration
- you are building LLM training or inference pipelines that must run on multiple chip vendors

## When to avoid
- you need highly specialized hand-tuned CUDA kernels for a single NVIDIA GPU
- your project does not use PyTorch or Triton-compatible hardware
- you need operators not yet covered by the library's kernel collection

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, llm-inference, llm-training, gpu-computing, compiler
- domain: machine-learning, deep-learning, large-language-models, gpu-computing, developer-tools
- platform: python, cross-platform
- tags: triton, pytorch, kernels, operator-library, hardware-acceleration, aten-backend, gpu, linux

## Member repositories
- flagos-ai/FlagGems (main) score 89

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:11.968029+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 2026-08-30T06:50:01.905234+00:00, confidence not recorded.
  - readme: https://github.com/flagos-ai/FlagGems (fetched 2026-09-03T02:15:11.968029+00:00, sha c917ddfdf2b2)
- Data as of 2026-08-30T08:39:29.467469+00:00.