# NVIDIA-Merlin/HugeCTR

HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training

Repository: https://github.com/NVIDIA-Merlin/HugeCTR
Canonical: https://ross.abutalabs.com/products/hugectr
Language: C++
License: Apache-2.0
License Family: permissive
Topics: cpp, deep-learning, gpu-acceleration, recommendation-system, recommender-system
Last push: 2026-08-03T01:12:02+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 95, release rhythm 42, longevity 100
- inputs: {"age_days": 2546, "days_push": 31, "days_rel": 174, "gap_med": 362, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1071, forks 207 (observed 2026-08-28T04:03:28.081521+00:00)

## What it is
HugeCTR is a GPU-accelerated deep learning framework from NVIDIA designed for training and inference of large recommender models, especially click-through-rate (CTR) estimation with huge embedding tables. It offers a high-level Python interface, model-parallel and multi-node training, mixed precision, and ONNX export.

## Use cases
- train click-through-rate prediction models on GPUs
- train recommender models with very large embedding tables
- run model-parallel deep learning training across multiple GPUs and nodes
- benchmark recommender system training performance with MLPerf
- convert trained recommender models to ONNX for deployment
- serve large-scale recommendation model inference

## When to choose
- you need to train CTR or recommender models with embeddings too large for a single GPU
- you want NVIDIA-optimized, MLPerf-competitive training performance
- you need multi-node, model-parallel training for recommendation workloads

## When to avoid
- your models are small and fit comfortably in standard frameworks like PyTorch or TensorFlow
- you are not working on recommendation or CTR-style workloads
- you lack NVIDIA GPUs or a multi-GPU cluster

## Facets
- artifact type: framework
- maturity: active
- function: deep-learning, machine-learning, gpu-computing, benchmarking
- domain: deep-learning, machine-learning, gpu-computing
- platform: cpp, python
- tags: ctr, recommender-systems, nvidia, model-parallel-training, embeddings, mlperf, recommendation-systems, linux, gpu

## Member repositories
- NVIDIA-Merlin/HugeCTR (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:28.081521+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 2026-08-30T06:53:47.778745+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA-Merlin/HugeCTR (fetched 2026-08-28T04:03:28.081521+00:00, sha 90c9d3698651)
- Data as of 2026-08-30T08:39:29.467469+00:00.