{"data":{"node":{"slug":"nvidia-megatron-lm","name":"Megatron-LM","tagline":"Ongoing research training transformer models at scale","github_url":"https://github.com/NVIDIA/Megatron-LM","owner":"NVIDIA","repo":"Megatron-LM","owner_avatar_url":"https://avatars.githubusercontent.com/u/1728152?v=4","primary_language":"Python","stars":17341,"forks":4333,"topics":["large-language-models","model-para","transformers"],"archived":false,"github_pushed_at":"2026-08-06T23:12:52+00:00","maintenance_label":"Very active","stars_delta_30d":353,"url":"https://www.graphcanon.com/tools/nvidia-megatron-lm","markdown_url":"https://www.graphcanon.com/tools/nvidia-megatron-lm.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nvidia-megatron-lm","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nvidia-megatron-lm"},"categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"large-language-models","name":"large language models"},{"slug":"model-para","name":"model-para"},{"slug":"transformers","name":"transformers"}],"edges":[{"type":"integrates_with","direction":"out","explanation":"Megatron-LM, designed for GPU-optimized training of large-scale transformer models, integrates with the transformers library by leveraging its extensive collection of pre-trained models and utilities to facilitate the development and fine-tuning of these models in Megatron's framework.","successor_context":null,"tool":{"slug":"huggingface-transformers","name":"transformers","tagline":"Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models","github_url":"https://github.com/huggingface/transformers","owner":"huggingface","repo":"transformers","owner_avatar_url":"https://avatars.githubusercontent.com/u/25720743?v=4","primary_language":"Python","stars":164121,"forks":34249,"topics":["audio","deep-learning","deepseek","gemma","glm","hacktoberfest","llm","machine-learning","model-hub","natural-language-processing","nlp","pretrained-models","python","pytorch","pytorch-transformers","qwen","speech-recognition","transformer","vlm"],"archived":false,"github_pushed_at":"2026-08-15T22:28:12+00:00","maintenance_label":"Very active","stars_delta_30d":1457,"url":"https://www.graphcanon.com/tools/huggingface-transformers","markdown_url":"https://www.graphcanon.com/tools/huggingface-transformers.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/huggingface-transformers","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=huggingface-transformers"}},{"type":"integrates_with","direction":"out","explanation":"Megatron-LM and DeepLearningExamples both focus on state-of-the-art deep learning capabilities specifically optimized for NVIDIA GPUs. They integrate well because they share a common hardware ecosystem and aim to enhance machine learning performance.","successor_context":null,"tool":{"slug":"nvidia-deeplearningexamples","name":"DeepLearningExamples","tagline":"State-of-the-Art Deep Learning scripts for various applications","github_url":"https://github.com/NVIDIA/DeepLearningExamples","owner":"NVIDIA","repo":"DeepLearningExamples","owner_avatar_url":"https://avatars.githubusercontent.com/u/1728152?v=4","primary_language":"Jupyter Notebook","stars":14844,"forks":3408,"topics":["computer-vision","deep-learning","drug-discovery","forecasting","large-language-models","mxnet","nlp","paddlepaddle","pytorch","recommender-systems","speech-recognition","speech-synthesis","tensorflow","tensorflow2","translation"],"archived":false,"github_pushed_at":"2024-08-12T14:01:29+00:00","maintenance_label":"Dormant","stars_delta_30d":14,"url":"https://www.graphcanon.com/tools/nvidia-deeplearningexamples","markdown_url":"https://www.graphcanon.com/tools/nvidia-deeplearningexamples.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nvidia-deeplearningexamples","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nvidia-deeplearningexamples"}},{"type":"related","direction":"out","explanation":"Both projects are related in their focus on serving and handling large language models efficiently. While Megatron-LM focuses more on training at scale, vLLM emphasizes fast and affordable LLM inference.","successor_context":null,"tool":{"slug":"vllm-project-vllm","name":"vllm","tagline":"A high-throughput and memory-efficient inference and serving engine for LLMs","github_url":"https://github.com/vllm-project/vllm","owner":"vllm-project","repo":"vllm","owner_avatar_url":"https://avatars.githubusercontent.com/u/136984999?v=4","primary_language":"Python","stars":87847,"forks":20135,"topics":["amd","blackwell","cuda","deepseek","deepseek-v3","gpt","gpt-oss","inference","kimi","llama","llm","llm-serving","model-serving","moe","openai","pytorch","qwen","qwen3","tpu","transformer"],"archived":false,"github_pushed_at":"2026-08-01T11:55:36+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/vllm-project-vllm","markdown_url":"https://www.graphcanon.com/tools/vllm-project-vllm.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/vllm-project-vllm","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=vllm-project-vllm"}},{"type":"related","direction":"out","explanation":"Both Megatron-LM and litgpt deal with large-scale language model training but focus on slightly different aspects. While Megatron-LM emphasizes research and scaling transformer models across multiple GPUs, litgpt provides specific LLM recipes for various stages of model development.","successor_context":null,"tool":{"slug":"lightning-ai-litgpt","name":"litgpt","tagline":"High-performance LLMs with recipes for pretraining, finetuning and deployment","github_url":"https://github.com/Lightning-AI/litgpt","owner":"Lightning-AI","repo":"litgpt","owner_avatar_url":"https://avatars.githubusercontent.com/u/58386951?v=4","primary_language":"Python","stars":13605,"forks":1483,"topics":["ai","artificial-intelligence","deep-learning","large-language-models","llm","llm-inference","llms"],"archived":false,"github_pushed_at":"2026-07-20T10:24:12+00:00","maintenance_label":"Active","stars_delta_30d":137,"url":"https://www.graphcanon.com/tools/lightning-ai-litgpt","markdown_url":"https://www.graphcanon.com/tools/lightning-ai-litgpt.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/lightning-ai-litgpt","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=lightning-ai-litgpt"}},{"type":"related","direction":"out","explanation":null,"successor_context":null,"tool":{"slug":"microsoft-autogen","name":"autogen","tagline":"A programming framework for agentic AI","github_url":"https://github.com/microsoft/autogen","owner":"microsoft","repo":"autogen","owner_avatar_url":"https://avatars.githubusercontent.com/u/6154722?v=4","primary_language":"Python","stars":60139,"forks":9059,"topics":["agentic","agentic-agi","agents","ai","autogen","autogen-ecosystem","chatgpt","framework","llm-agent","llm-framework"],"archived":false,"github_pushed_at":"2026-04-15T11:59:09+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/microsoft-autogen","markdown_url":"https://www.graphcanon.com/tools/microsoft-autogen.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/microsoft-autogen","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=microsoft-autogen"}},{"type":"related","direction":"out","explanation":"Megatron-LM is a tool that can be part of the toolkit curated by llm-engineer-toolkit, which lists various LLM libraries. While not directly integrated, Megatron-LM could be referenced as one of the tools in this comprehensive list.","successor_context":null,"tool":{"slug":"kalyanks-nlp-llm-engineer-toolkit","name":"llm-engineer-toolkit","tagline":"A curated list of over 120 LLM libraries categorized.","github_url":"https://github.com/KalyanKS-NLP/llm-engineer-toolkit","owner":"KalyanKS-NLP","repo":"llm-engineer-toolkit","owner_avatar_url":"https://avatars.githubusercontent.com/u/202506543?v=4","primary_language":null,"stars":10767,"forks":1682,"topics":["ai-engineer","generative-ai","large-language-models","llm-engineer","llms"],"archived":false,"github_pushed_at":"2026-08-16T13:05:43+00:00","maintenance_label":"Very active","stars_delta_30d":106,"url":"https://www.graphcanon.com/tools/kalyanks-nlp-llm-engineer-toolkit","markdown_url":"https://www.graphcanon.com/tools/kalyanks-nlp-llm-engineer-toolkit.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/kalyanks-nlp-llm-engineer-toolkit","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=kalyanks-nlp-llm-engineer-toolkit"}},{"type":"related","direction":"out","explanation":"Megatron-LM and train-llm-from-scratch both serve to aid developers in training large language models. Although they approach the problem differently, they share a common goal of making LLM development accessible.","successor_context":null,"tool":{"slug":"fareedkhan-dev-train-llm-from-scratch","name":"train-llm-from-scratch","tagline":"A straightforward method for training your LLM from raw text to aligned model generation","github_url":"https://github.com/FareedKhan-dev/train-llm-from-scratch","owner":"FareedKhan-dev","repo":"train-llm-from-scratch","owner_avatar_url":"https://avatars.githubusercontent.com/u/63067900?v=4","primary_language":"Python","stars":9141,"forks":1264,"topics":["gemini","large-language-models","llm","openai","training","transformers"],"archived":false,"github_pushed_at":"2026-08-17T05:07:26+00:00","maintenance_label":"Very active","stars_delta_30d":765,"url":"https://www.graphcanon.com/tools/fareedkhan-dev-train-llm-from-scratch","markdown_url":"https://www.graphcanon.com/tools/fareedkhan-dev-train-llm-from-scratch.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/fareedkhan-dev-train-llm-from-scratch","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=fareedkhan-dev-train-llm-from-scratch"}},{"type":"related","direction":"in","explanation":null,"successor_context":null,"tool":{"slug":"p-e-w-heretic","name":"heretic","tagline":"Fully automatic censorship removal for language models","github_url":"https://github.com/p-e-w/heretic","owner":"p-e-w","repo":"heretic","owner_avatar_url":"https://avatars.githubusercontent.com/u/2702526?v=4","primary_language":"Python","stars":27709,"forks":2997,"topics":["abliteration","llm","transformer"],"archived":false,"github_pushed_at":"2026-08-14T11:09:07+00:00","maintenance_label":"Very active","stars_delta_30d":1309,"url":"https://www.graphcanon.com/tools/p-e-w-heretic","markdown_url":"https://www.graphcanon.com/tools/p-e-w-heretic.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/p-e-w-heretic","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=p-e-w-heretic"}},{"type":"integrates_with","direction":"in","explanation":"NVIDIA DeepLearningExamples provides training scripts that could leverage Megatron-LM for large-scale transformer-based models, thereby integrating both resources efficiently for high-performance model training.","successor_context":null,"tool":{"slug":"nvidia-deeplearningexamples","name":"DeepLearningExamples","tagline":"State-of-the-Art Deep Learning scripts for various applications","github_url":"https://github.com/NVIDIA/DeepLearningExamples","owner":"NVIDIA","repo":"DeepLearningExamples","owner_avatar_url":"https://avatars.githubusercontent.com/u/1728152?v=4","primary_language":"Jupyter Notebook","stars":14844,"forks":3408,"topics":["computer-vision","deep-learning","drug-discovery","forecasting","large-language-models","mxnet","nlp","paddlepaddle","pytorch","recommender-systems","speech-recognition","speech-synthesis","tensorflow","tensorflow2","translation"],"archived":false,"github_pushed_at":"2024-08-12T14:01:29+00:00","maintenance_label":"Dormant","stars_delta_30d":14,"url":"https://www.graphcanon.com/tools/nvidia-deeplearningexamples","markdown_url":"https://www.graphcanon.com/tools/nvidia-deeplearningexamples.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nvidia-deeplearningexamples","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nvidia-deeplearningexamples"}},{"type":"alternative","direction":"in","explanation":"Both are focused on training large transformer models but `train-llm-from-scratch` is more of a standalone tutorial, whereas NVIDIA’s Megatron-LM scales up the process for massive models.","successor_context":null,"tool":{"slug":"fareedkhan-dev-train-llm-from-scratch","name":"train-llm-from-scratch","tagline":"A straightforward method for training your LLM from raw text to aligned model generation","github_url":"https://github.com/FareedKhan-dev/train-llm-from-scratch","owner":"FareedKhan-dev","repo":"train-llm-from-scratch","owner_avatar_url":"https://avatars.githubusercontent.com/u/63067900?v=4","primary_language":"Python","stars":9141,"forks":1264,"topics":["gemini","large-language-models","llm","openai","training","transformers"],"archived":false,"github_pushed_at":"2026-08-17T05:07:26+00:00","maintenance_label":"Very active","stars_delta_30d":765,"url":"https://www.graphcanon.com/tools/fareedkhan-dev-train-llm-from-scratch","markdown_url":"https://www.graphcanon.com/tools/fareedkhan-dev-train-llm-from-scratch.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/fareedkhan-dev-train-llm-from-scratch","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=fareedkhan-dev-train-llm-from-scratch"}},{"type":"depends_on","direction":"in","explanation":"flash-linear-attention likely depends on Megatron-LM for GPU optimization during the training of large-scale transformer models.","successor_context":null,"tool":{"slug":"fla-org-flash-linear-attention","name":"flash-linear-attention","tagline":"🚀 Efficient implementations for emerging model architectures","github_url":"https://github.com/fla-org/flash-linear-attention","owner":"fla-org","repo":"flash-linear-attention","owner_avatar_url":"https://avatars.githubusercontent.com/u/40835596?v=4","primary_language":"Python","stars":5568,"forks":661,"topics":["large-language-models","machine-learning-systems","natural-language-processing","sequence-modeling"],"archived":false,"github_pushed_at":"2026-08-17T10:13:08+00:00","maintenance_label":"Very active","stars_delta_30d":208,"url":"https://www.graphcanon.com/tools/fla-org-flash-linear-attention","markdown_url":"https://www.graphcanon.com/tools/fla-org-flash-linear-attention.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/fla-org-flash-linear-attention","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=fla-org-flash-linear-attention"}}],"neighbours":[{"slug":"lightning-ai-pytorch-lightning","name":"pytorch-lightning","tagline":"Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.","github_url":"https://github.com/Lightning-AI/pytorch-lightning","owner":"Lightning-AI","repo":"pytorch-lightning","owner_avatar_url":"https://avatars.githubusercontent.com/u/58386951?v=4","primary_language":"Python","stars":31267,"forks":3768,"topics":["ai","artificial-intelligence","data-science","deep-learning","machine-learning","python","pytorch"],"archived":false,"github_pushed_at":"2026-08-03T01:13:09+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/lightning-ai-pytorch-lightning","markdown_url":"https://www.graphcanon.com/tools/lightning-ai-pytorch-lightning.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/lightning-ai-pytorch-lightning","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=lightning-ai-pytorch-lightning","shared_categories":["model-training"]},{"slug":"lyogavin-airllm","name":"airllm","tagline":"AirLLM 70B inference with single 4GB GPU","github_url":"https://github.com/lyogavin/airllm","owner":"lyogavin","repo":"airllm","owner_avatar_url":"https://avatars.githubusercontent.com/u/1113905?v=4","primary_language":"Jupyter Notebook","stars":24183,"forks":2722,"topics":["chinese-llm","chinese-nlp","finetune","generative-ai","instruct-gpt","instruction-set","llama","llm","lora","open-models","open-source","open-source-models","qlora"],"archived":false,"github_pushed_at":"2026-07-23T08:29:43+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/lyogavin-airllm","markdown_url":"https://www.graphcanon.com/tools/lyogavin-airllm.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/lyogavin-airllm","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=lyogavin-airllm","shared_categories":[]},{"slug":"nvidia-deeplearningexamples","name":"DeepLearningExamples","tagline":"State-of-the-Art Deep Learning scripts for various applications","github_url":"https://github.com/NVIDIA/DeepLearningExamples","owner":"NVIDIA","repo":"DeepLearningExamples","owner_avatar_url":"https://avatars.githubusercontent.com/u/1728152?v=4","primary_language":"Jupyter Notebook","stars":14844,"forks":3408,"topics":["computer-vision","deep-learning","drug-discovery","forecasting","large-language-models","mxnet","nlp","paddlepaddle","pytorch","recommender-systems","speech-recognition","speech-synthesis","tensorflow","tensorflow2","translation"],"archived":false,"github_pushed_at":"2024-08-12T14:01:29+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/nvidia-deeplearningexamples","markdown_url":"https://www.graphcanon.com/tools/nvidia-deeplearningexamples.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nvidia-deeplearningexamples","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nvidia-deeplearningexamples","shared_categories":["model-training"]},{"slug":"lightning-ai-litgpt","name":"litgpt","tagline":"High-performance LLMs with recipes for pretraining, finetuning and deployment","github_url":"https://github.com/Lightning-AI/litgpt","owner":"Lightning-AI","repo":"litgpt","owner_avatar_url":"https://avatars.githubusercontent.com/u/58386951?v=4","primary_language":"Python","stars":13605,"forks":1483,"topics":["ai","artificial-intelligence","deep-learning","large-language-models","llm","llm-inference","llms"],"archived":false,"github_pushed_at":"2026-07-20T10:24:12+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/lightning-ai-litgpt","markdown_url":"https://www.graphcanon.com/tools/lightning-ai-litgpt.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/lightning-ai-litgpt","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=lightning-ai-litgpt","shared_categories":["model-training"]},{"slug":"huggingface-accelerate","name":"accelerate","tagline":"A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support.","github_url":"https://github.com/huggingface/accelerate","owner":"huggingface","repo":"accelerate","owner_avatar_url":"https://avatars.githubusercontent.com/u/25720743?v=4","primary_language":"Python","stars":9803,"forks":1425,"topics":[],"archived":false,"github_pushed_at":"2026-07-30T11:20:54+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/huggingface-accelerate","markdown_url":"https://www.graphcanon.com/tools/huggingface-accelerate.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/huggingface-accelerate","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=huggingface-accelerate","shared_categories":["model-training"]},{"slug":"fminference-flexllmgen","name":"FlexLLMGen","tagline":"Running large language models on a single GPU for throughput-oriented scenarios.","github_url":"https://github.com/FMInference/FlexLLMGen","owner":"FMInference","repo":"FlexLLMGen","owner_avatar_url":"https://avatars.githubusercontent.com/u/125944572?v=4","primary_language":"Python","stars":9361,"forks":590,"topics":["deep-learning","gpt-3","high-throughput","large-language-models","machine-learning","offloading","opt"],"archived":true,"github_pushed_at":"2024-10-28T03:05:41+00:00","maintenance_label":"Archived","url":"https://www.graphcanon.com/tools/fminference-flexllmgen","markdown_url":"https://www.graphcanon.com/tools/fminference-flexllmgen.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/fminference-flexllmgen","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=fminference-flexllmgen","shared_categories":[]},{"slug":"fareedkhan-dev-train-llm-from-scratch","name":"train-llm-from-scratch","tagline":"A straightforward method for training your LLM from raw text to aligned model generation","github_url":"https://github.com/FareedKhan-dev/train-llm-from-scratch","owner":"FareedKhan-dev","repo":"train-llm-from-scratch","owner_avatar_url":"https://avatars.githubusercontent.com/u/63067900?v=4","primary_language":"Python","stars":9141,"forks":1264,"topics":["gemini","large-language-models","llm","openai","training","transformers"],"archived":false,"github_pushed_at":"2026-08-17T05:07:26+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/fareedkhan-dev-train-llm-from-scratch","markdown_url":"https://www.graphcanon.com/tools/fareedkhan-dev-train-llm-from-scratch.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/fareedkhan-dev-train-llm-from-scratch","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=fareedkhan-dev-train-llm-from-scratch","shared_categories":["model-training"]},{"slug":"eleutherai-gpt-neox","name":"gpt-neox","tagline":"Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries","github_url":"https://github.com/EleutherAI/gpt-neox","owner":"EleutherAI","repo":"gpt-neox","owner_avatar_url":"https://avatars.githubusercontent.com/u/68924597?v=4","primary_language":"Python","stars":7452,"forks":1119,"topics":["deepspeed-library","gpt-3","language-model","transformers"],"archived":false,"github_pushed_at":"2026-06-11T19:25:44+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/eleutherai-gpt-neox","markdown_url":"https://www.graphcanon.com/tools/eleutherai-gpt-neox.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/eleutherai-gpt-neox","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=eleutherai-gpt-neox","shared_categories":["model-training"]},{"slug":"linkedin-liger-kernel","name":"Liger-Kernel","tagline":"Efficient Triton Kernels for LLM Training","github_url":"https://github.com/linkedin/Liger-Kernel","owner":"linkedin","repo":"Liger-Kernel","owner_avatar_url":"https://avatars.githubusercontent.com/u/357098?v=4","primary_language":"Python","stars":6555,"forks":573,"topics":["finetuning","gemma2","hacktoberfest","llama","llama3","llm-training","llms","mistral","phi3","triton","triton-kernels"],"archived":false,"github_pushed_at":"2026-08-07T08:48:09+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/linkedin-liger-kernel","markdown_url":"https://www.graphcanon.com/tools/linkedin-liger-kernel.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/linkedin-liger-kernel","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=linkedin-liger-kernel","shared_categories":["model-training"]},{"slug":"nvidia-fastertransformer","name":"FasterTransformer","tagline":"Transformer related optimization including BERT and GPT","github_url":"https://github.com/NVIDIA/FasterTransformer","owner":"NVIDIA","repo":"FasterTransformer","owner_avatar_url":"https://avatars.githubusercontent.com/u/1728152?v=4","primary_language":"C++","stars":6446,"forks":935,"topics":["bert","gpt","pytorch","transformer"],"archived":false,"github_pushed_at":"2024-03-27T11:25:30+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/nvidia-fastertransformer","markdown_url":"https://www.graphcanon.com/tools/nvidia-fastertransformer.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nvidia-fastertransformer","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nvidia-fastertransformer","shared_categories":[]},{"slug":"nvidia-transformerengine","name":"TransformerEngine","tagline":"A library for accelerating Transformer models on NVIDIA GPUs using low precision formats like FP8 and FP4.","github_url":"https://github.com/NVIDIA/TransformerEngine","owner":"NVIDIA","repo":"TransformerEngine","owner_avatar_url":"https://avatars.githubusercontent.com/u/1728152?v=4","primary_language":"Python","stars":3479,"forks":795,"topics":["cuda","deep-learning","fp4","fp8","gpu","jax","machine-learning","python","pytorch"],"archived":false,"github_pushed_at":"2026-08-07T05:44:50+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/nvidia-transformerengine","markdown_url":"https://www.graphcanon.com/tools/nvidia-transformerengine.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nvidia-transformerengine","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nvidia-transformerengine","shared_categories":["model-training"]},{"slug":"huggingface-nanotron","name":"nanotron","tagline":"Minimalistic large language model 3D-parallelism training","github_url":"https://github.com/huggingface/nanotron","owner":"huggingface","repo":"nanotron","owner_avatar_url":"https://avatars.githubusercontent.com/u/25720743?v=4","primary_language":"Python","stars":2775,"forks":329,"topics":[],"archived":false,"github_pushed_at":"2026-05-26T10:32:37+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/huggingface-nanotron","markdown_url":"https://www.graphcanon.com/tools/huggingface-nanotron.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/huggingface-nanotron","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=huggingface-nanotron","shared_categories":["model-training"]}]}}