Megatron LM - Detailed Review
Research Tools
Megatron LM - Product Overview
Introduction to Megatron LM
Megatron LM, developed by NVIDIA, is a powerful and highly scalable language model framework within the Research Tools AI-driven product category. Here’s a brief overview of its primary function, target audience, and key features.Primary Function
Megatron LM is specifically designed for training large-scale natural language processing (NLP) models. It enables the efficient processing of massive amounts of data and learning from diverse linguistic patterns, resulting in impressive language generation capabilities. The framework supports a wide range of NLP tasks, including language modeling, text generation, question answering, and sentiment analysis.Target Audience
The primary target audience for Megatron LM includes researchers, developers, and organizations involved in advanced NLP projects. This includes those working on large language models, generative AI, and multimodal training. The framework is particularly useful for those who need to train models at large scales and require high performance and efficiency.Key Features
Scalability
Megatron LM is highly scalable, allowing it to handle models with billions of parameters. It achieves this through a combination of data parallelism and model parallelism, where the training data and the model itself are split across multiple GPUs or machines.Performance
The framework is optimized for speed and efficiency. For instance, Megatron can train models up to 7 times faster than other models like T5, and it has achieved high accuracy scores on benchmarks such as the Stanford Question Answering Dataset (SQuAD v1.1).Technical Architecture
Megatron uses a variant of the transformer architecture called the GShard Transformer, which allows for greater parallelism and scalability. It also supports various optimization techniques like stochastic gradient descent (SGD), Adam, and Adafactor.Multimodal Training
With the introduction of Megatron-Core, the framework now supports multimodal training, allowing models to leverage various types of data, including visual inputs, to generate comprehensive and context-aware responses.Advanced Parallelism
Megatron LM incorporates advanced parallelism techniques such as tensor parallelism, sequence parallelism, and pipeline parallelism. The integration with Microsoft’s DeepSpeed library further enhances these capabilities through 3D parallelism, which includes Zero Redundancy Optimizer (ZeRO) sharding and other offloading techniques.Compatibility and Customization
The framework is compatible with all NVIDIA Tensor Core GPUs and supports the FP8 data format introduced with the NVIDIA Hopper architecture, which boosts compute throughput and reduces memory footprint. It also offers customizable building blocks and modular APIs, allowing for easy integration into various NLP architectures.Business Applications
Megatron LM can be applied in various business contexts, such as chatbots, virtual assistants, sentiment analysis, predictive text, and automated news summarization. Its capabilities make it a versatile tool for any organization requiring advanced NLP solutions. Megatron LM - User Interface and Experience
User Interface and Overall User Experience
The user interface and overall user experience of NVIDIA’s Megatron LM are subjects of both praise and criticism, reflecting its powerful capabilities and some of its limitations.
Ease of Use
While Megatron LM is praised for its efficiency and performance, its ease of use is a mixed bag. Some users find the interface relatively easy to use, especially for those familiar with training large language models. For instance, users appreciate the flexibility and simplicity of integrating Megatron LM into existing workflows, particularly for fine-tuning pre-trained language models.
However, many users highlight that Megatron LM has a steep learning curve, especially for those who are not tech-savvy. The framework requires significant technical expertise to set up and customize, which can be challenging for beginners.
User Interface
The user interface itself is not extensively detailed in the available resources, but it is clear that the framework is highly customizable. Users can adjust various parameters such as the number of transformer layers, model size, and hidden size, which suggests a flexible and configurable setup.
Documentation and Support
One of the major drawbacks mentioned by users is the limited documentation and community support. Many users find that the documentation could be improved, and issues raised on GitHub are not resolved in a timely manner. This lack of support can make it harder for users to troubleshoot and fully utilize the framework’s capabilities.
Performance and Efficiency
Despite the challenges, users overwhelmingly praise Megatron LM for its performance and efficiency. The framework’s ability to handle vast datasets, leverage model parallelism, and utilize mixed precision training makes it highly efficient for training large language models. This efficiency translates into faster training times and better resource utilization, which is a significant advantage for users working with massive models.
Overall Experience
The overall user experience with Megatron LM is marked by its exceptional performance and scalability but is also marred by its complexity and the need for substantial computational resources. Users appreciate the framework’s ability to process large datasets and train massive models efficiently, but they often struggle with the initial setup and customization due to the lack of comprehensive documentation and community support.
Conclusion
In summary, while Megatron LM offers unparalleled performance and scalability, its user interface and experience are hampered by a steep learning curve, limited documentation, and significant resource requirements. However, for experienced users and those willing to invest time in learning the framework, it can be a highly powerful tool for training large language models.
Megatron LM - Key Features and Functionality
Overview
Megatron-LM, a powerful language model developed by NVIDIA, boasts several key features and functionalities that make it a standout in the field of natural language processing (NLP).
Model Size and Parameters
Megatron-LM is one of the largest language models, with an impressive 8.3 billion parameters. This vast size allows it to capture complex language patterns and nuances, enabling it to generate high-quality, coherent text.
Distributed Training and Parallelism
The model leverages advanced parallelism techniques, including tensor, sequence, pipeline, and context parallelism, to train efficiently across thousands of GPUs. This is facilitated by NVIDIA’s Megatron-Core library, which provides GPU-optimized training methods and modular APIs. This distributed training capability ensures fast and efficient processing of large-scale datasets.
Training Process
Megatron-LM employs a two-step training process. First, it is pre-trained on a large-scale dataset using the masked language modeling (MLM) objective, where the model predicts missing words in sentences based on context. Second, it is fine-tuned using a smaller dataset with specific task objectives, such as text completion or language translation. This combination of pre-training and fine-tuning enhances its performance in various NLP tasks.
Multimodal Capabilities
Megatron-LM supports multimodal inputs, including images and audio, which revolutionizes how NLP tasks are approached. The recent integration with Megatron-Core v0.7 now supports multimodal training, allowing models to generate comprehensive and context-aware responses using multiple sensory inputs. This is achieved through the Large Language and Vision Assistant (LLaVA) pipeline, enabling the blending of multimodal datasets with determinism and reproducibility.
Efficiency and Scalability
The model is optimized for efficiency and scalability. It uses techniques such as activation recomputation, distributed optimizers, and distributed checkpointing to save memory and ensure training resiliency. This allows for high per-GPU throughput even when training large models across thousands of GPUs.
Versatility in NLP Tasks
Megatron-LM is versatile and supports a wide range of NLP tasks, including language translation, summarization, question-answering, sentiment analysis, and text generation. Its ability to handle vast amounts of data and generate coherent text makes it highly effective in these tasks.
Contrastive Supervised Fine-Tuning
To improve the diversity of responses, Megatron-LM incorporates a technique called contrastive supervised fine-tuning. This involves introducing a contrastive loss term during training, encouraging the model to generate distinct outputs and mitigating repetitive responses.
Integration with Other Models
Megatron-LM can be integrated with other pre-trained language models like BERT or GPT, enhancing its performance in specific tasks. This integration allows for richer contextual representation and comprehensive understanding, making it a powerful tool in modern information retrieval and question-answering systems.
Applications
The model has significant implications for various industries, such as content creation, customer support, and personalized assistant systems. It can generate human-like text, improve customer interactions, and provide intelligent and adaptive language tutoring. Its applications also extend to machine translation, text summarization, and sentiment analysis, making it a valuable tool across multiple domains.
Conclusion
In summary, Megatron-LM’s key features include its massive scale, efficient distributed training, multimodal capabilities, and versatility in handling a wide range of NLP tasks. These features, combined with its integration with advanced training techniques and other models, make it a powerful tool in the field of natural language processing.
Megatron LM - Performance and Accuracy
Performance of Megatron-LM
Megatron-LM, developed by NVIDIA, is a significant advancement in the field of natural language processing (NLP) due to its impressive performance and accuracy.Scalability and Efficiency
Megatron-LM stands out for its ability to train large-scale language models efficiently. It leverages model parallelism and data parallelism to distribute the training process across multiple GPUs, achieving up to 15.1 PetaFLOPs sustained over the entire application when using 512 NVIDIA V100 GPUs. This approach results in high scaling efficiency, with 76% efficiency compared to a single GPU baseline.Accuracy on Benchmark Tasks
The model has demonstrated state-of-the-art results on several benchmark tasks. For instance, it achieved a perplexity of 10.8 on the WikiText103 dataset, an accuracy of 66.5% on the LAMBADA dataset, and an accuracy of 90.9% on the RACE dataset. These results indicate a significant improvement in performance as the model size increases, particularly when careful attention is given to the placement of layer normalization in BERT-like models.Training Data and Model Size
Megatron-LM is trained on an extensive dataset comprising 8.3 billion sentences and 37 billion tokens. The model itself can scale up to 8.3 billion parameters, making it one of the largest language models trained to date. This massive scale enables the model to generate coherent and contextually accurate text, supporting a wide range of NLP tasks such as language translation, summarization, and question-answering.Limitations and Areas for Improvement
Despite its impressive performance, Megatron-LM faces several limitations:Computational Requirements
One of the significant challenges is the massive computational resources required to train and run the model. This includes substantial GPU memory and computational power, making it difficult to deploy in resource-constrained environments. The training process is also time-consuming, with each epoch taking around two days for the 8.3 billion parameter model on 512 GPUs.Memory and Input Capacity
The model’s input capacity is restricted due to memory limitations, which can pose challenges when dealing with long documents or lengthy conversations. This restriction can affect the model’s ability to maintain context and coherence throughout the text.Potential Biases and Ethical Concerns
The extensive pre-training phase of Megatron-LM can result in potential biases and undesirable outputs if the training data is biased or incomplete. This highlights the need for careful management and holistic evaluation of the model’s impact to mitigate potential harms.Training and Inference Speed
The model’s large size also results in slower training and inference speeds compared to smaller language models. This can be a significant limitation for real-time applications where speed is crucial.Conclusion
Megatron-LM represents a significant advancement in NLP, offering exceptional performance and accuracy on various benchmark tasks. However, its deployment is hindered by high computational requirements, memory limitations, and potential biases in the training data. Addressing these limitations through improvements in efficiency, memory footprint, and responsible AI practices will be essential for further enhancing the model’s capabilities and practicality. Megatron LM - Pricing and Plans
The Megatron-LM Framework
The Megatron-LM framework, developed by NVIDIA, is an open-source tool for training large-scale language models, and it does not have a pricing structure or different tiers in the traditional sense. Here are some key points to consider:
Open-Source Nature
Megatron-LM is an open-source project, which means it is freely available for anyone to use, modify, and distribute. You can access the entire codebase and documentation without any cost.
No Subscription Plans
There are no subscription plans or different tiers for using Megatron-LM. The framework is provided as is, and users can utilize it based on their specific needs and resources.
Features and Capabilities
The framework offers several advanced features, including tensor, pipeline, and sequence parallelism, optimized transformer layer implementations, and other performance-enhancing techniques. These features are available to all users without any additional cost.
Hardware Requirements
While the software itself is free, using Megatron-LM effectively often requires significant computational resources, such as high-end GPUs. The cost of these hardware components is not included in the framework itself but is a necessary investment for those who want to train large language models.
Summary
In summary, Megatron-LM is a free, open-source tool with no pricing structure or subscription plans. It is available for anyone to use, with the only costs being associated with the necessary hardware and computational resources.
Megatron LM - Integration and Compatibility
NVIDIA’s Megatron-LM Overview
NVIDIA’s Megatron-LM is a versatile and highly integrated framework for training large transformer models, offering several key features that enhance its compatibility and integration with various tools and platforms.
Integration with Other Frameworks
Megatron-LM is closely integrated with other popular AI frameworks and libraries. For instance, it is compatible with Hugging Face’s Accelerate, enabling large-scale pre-training and fine-tuning of models like BERT, GPT, and T5. This integration allows users to leverage Megatron-LM’s efficient tensor, pipeline, and sequence-based model parallelism within the Accelerate ecosystem.
Additionally, Megatron-LM has inspired and is utilized by other frameworks such as Colossal-AI and NVIDIA NeMo. NeMo, in particular, is an enterprise-grade AI software platform that incorporates Megatron-Core (the latest iteration of Megatron-LM) for large language model capabilities, providing a comprehensive set of tools for multimodal and speech AI.
Compatibility with NVIDIA GPUs
Megatron-LM is optimized for NVIDIA GPUs, including the latest Tensor Core GPUs such as the A100 and V100. It also supports the FP8 data format available on NVIDIA Hopper architectures, enhancing compute throughput and reducing memory footprint. This compatibility ensures that users can fully leverage NVIDIA’s GPU capabilities to train large models efficiently.
Modular and Composable Design
The Megatron-Core library, part of Megatron-LM, features a modular and composable design. This allows developers to easily customize submodules in the PyTorch model definition, making it flexible for various use cases. The library includes GPU-optimized building blocks such as attention mechanisms, transformer blocks, normalization layers, and embedding techniques, all of which can be combined to train custom transformers at scale.
Distributed Training
Megatron-LM supports distributed training across hundreds or thousands of GPUs, enabling the efficient handling of models with billions of parameters. It implements data, model, and pipeline parallelism, which are crucial for scaling large models. This distributed training capability makes it an ideal choice for advanced natural language processing tasks.
Multimodal Training
The latest version of Megatron-Core, v0.7, introduces support for multimodal training through the Large Language and Vision Assistant (LLaVA) pipeline. This allows model developers to blend multimodal datasets with determinism and reproducibility, bringing generative AI models closer to human-like processing of multiple sensory inputs.
Conclusion
In summary, Megatron-LM integrates seamlessly with various AI frameworks, is highly compatible with NVIDIA GPUs, and offers a flexible, modular design that supports advanced distributed and multimodal training scenarios. This makes it a powerful tool for researchers and developers working on large-scale language models.
Megatron LM - Customer Support and Resources
Customer Support Options and Resources
For individuals seeking information about the customer support options and additional resources provided by Megatron-LM, here are some key points to consider:
Documentation and Guides
Megatron-LM provides comprehensive documentation that includes user guides, which are essential for getting started and using the framework effectively. The NVIDIA Docs, for example, offer a detailed user guide that covers initializing Megatron Core, setting up GPT models, and configuring datasets.
Community and Repository
The Megatron-LM project is hosted on GitHub, which allows users to access the source code, report issues, and engage with the community. This platform facilitates collaboration and support from other users and the development team.
Pre-trained Models and Fine-tuning
Megatron-LM offers pre-trained models that can be fine-tuned for various downstream NLP tasks. The documentation includes examples of how to fine-tune these models for tasks like question answering, using scripts and specific configuration parameters.
Advanced Training Features
The framework supports advanced model parallelism techniques such as tensor, sequence, pipeline, context, and MoE expert parallelism. It also includes features like fast distributed checkpointing and hybrid model training, which can be beneficial for large-scale training.
Integration and Compatibility
Megatron-LM is compatible with NVIDIA Tensor Core GPUs and supports advanced precision formats like FP8, introduced with the NVIDIA Hopper architecture. This ensures that users can leverage the latest hardware advancements for efficient training.
Additional Resources
- API Documentation: Detailed information about the components of Megatron-LM is available, which helps in understanding and utilizing the framework’s capabilities.
- Citation and Acknowledgment: For users who need to cite Megatron-LM in their research, the necessary citation details are provided.
While the primary support comes through the documentation and community engagement, there is no explicit mention of dedicated customer support channels like email or phone support. However, the community-driven approach and extensive documentation are designed to help users overcome most challenges.
Megatron LM - Pros and Cons
Advantages of Megatron-LM
Megatron-LM, developed by NVIDIA, offers several significant advantages that make it a powerful tool in the field of natural language processing (NLP):
Scalability and Efficiency
Megatron-LM is highly scalable and efficient, allowing it to train large-scale language models quickly. It leverages distributed training across multiple GPUs, achieving impressive performance and reducing training times significantly.
Large-Scale Training
The model is trained on massive datasets, comprising 8.3 billion sentences and 37 billion tokens, which enables it to generate coherent and contextually accurate text. This extensive training data allows it to capture nuanced language patterns and improve prediction accuracy.
Versatility
Megatron-LM supports a wide range of NLP tasks, including language translation, summarization, question-answering, and text generation. Its versatility makes it a valuable tool for various applications in NLP.
Fine-Tuning Capabilities
The model can be fine-tuned for specific tasks, enhancing its performance in areas like question-answering systems or text completion. It also employs contrastive supervised fine-tuning to improve the diversity of its responses.
High Performance
Megatron-LM achieves state-of-the-art performance on various NLP benchmarks, outperforming other models in terms of accuracy and efficiency. Its ability to handle long-context documents and process vast amounts of text data efficiently is particularly noteworthy.
Disadvantages of Megatron-LM
Despite its impressive capabilities, Megatron-LM also has several limitations and challenges:
Computational Requirements
The model requires substantial computational resources and memory to operate efficiently. This makes it challenging to deploy in resource-constrained environments and increases the computational costs.
Training and Inference Speed
While Megatron-LM is efficient in terms of parallel processing, its massive size can result in slower training and inference times compared to smaller models.
Data Bias and Fairness
The extensive pre-training phase can magnify biases present in the training data, raising concerns about fairness and inclusivity in its generated outputs. Careful management of these biases is necessary during deployment.
Interpretability
Like other deep learning models, Megatron-LM can be difficult to interpret, making it challenging to understand how the model makes predictions or diagnose errors.
Data Requirements
The model is trained on massive amounts of data, which can make it difficult to fine-tune on smaller or more specialized datasets. This limitation can be particularly significant for tasks requiring domain-specific data.
In summary, Megatron-LM is a powerful tool for NLP tasks, offering significant advantages in scalability, efficiency, and performance. However, it also comes with substantial computational requirements, potential biases, and interpretability challenges that need to be addressed.
Megatron LM - Comparison with Competitors
Unique Features of Megatron-LM
- Efficient Distributed Training: Megatron-LM is renowned for its ability to manage distributed training across multiple GPUs, optimizing performance and scalability. This is achieved through extensive parallelization techniques such as data, tensor, pipeline, and sequence parallelism.
- Optimized Performance: It leverages features like flash attention, distributed optimizers, activation recomputation, mixed precision, and rotary positional embeddings to enhance training efficiency.
- Compatibility and Integration: Megatron-LM is compatible with all NVIDIA Tensor Core GPUs and can utilize the FP8 data format supported by the NVIDIA Hopper architecture, further boosting compute throughput and reducing memory footprint.
- Modular Design: The Megatron-Core component has a modular, composable design that seamlessly integrates into multimodal LLM architectures, allowing for easy customization of submodules in the PyTorch model definition.
Alternatives and Comparisons
GPT-NeoX
- Based on Megatron-LM: GPT-NeoX is built upon NVIDIA’s Megatron Language Model but enhanced with techniques from DeepSpeed. It offers a central repository for training large-scale autoregressive models and is optimized for GPU training.
- Similarities: Like Megatron-LM, it focuses on efficient large-scale model training but adds additional improvements from DeepSpeed.
NVIDIA NeMo
- End-to-End Framework: NeMo is an end-to-end framework for training and deploying LLMs, building upon the technologies of NVIDIA research. It provides automated distributed processing and hyperparameter tuning, similar to Megatron-LM but with a more integrated approach for enterprise applications.
- Integration: NeMo allows developers to deploy models on public and private clouds and offers access to various NVIDIA models, including the Megatron 530B model.
ROCm Megatron-LM (AMD)
- Fork for AMD GPUs: This is a specialized fork of Megatron-LM designed to run on AMD GPUs using the ROCm framework. It offers similar features like flash attention and 3D parallelism but is optimized for AMD Instinct™ MI300X accelerators.
- Cross-Platform Compatibility: This alternative allows researchers to leverage AMD hardware, providing a similar but hardware-specific solution.
Colossal-AI and Hugging Face Accelerate
- Built on Megatron-LM: These frameworks are built on top of Megatron-LM, leveraging its efficient training capabilities. Colossal-AI and Hugging Face Accelerate offer additional features and integrations that can be beneficial depending on the specific needs of the project.
Other Large Language Models
- Models like Chinchilla, XLNet, and PanGu-Σ: These models, while not directly comparable in terms of training frameworks, offer alternative architectures and training methods. For example, Chinchilla outperforms several large models in downstream tasks with less compute, and PanGu-Σ uses sparse models and expert computation for efficient training.
- Different Architectures: These models might be more suitable for specific tasks or environments, such as Chinchilla’s efficiency in fine-tuning and inference or PanGu-Σ’s use of sparse models.
In summary, Megatron-LM stands out for its efficient distributed training and optimized performance features, making it a powerful tool for large-scale language model training. However, alternatives like GPT-NeoX, NVIDIA NeMo, and the ROCm Megatron-LM fork offer different strengths and compatibility options that can be chosen based on the specific requirements of the project.
Megatron LM - Frequently Asked Questions
Frequently Asked Questions about Megatron-LM
Q: What is Megatron-LM and what is it used for?
Megatron-LM is a framework developed by NVIDIA for training large transformer-based language models at scale. It enables efficient training using tensor, pipeline, and sequence-based model parallelism, making it suitable for large-scale language model training.
Q: How do I set up the environment for Megatron-LM?
To set up the environment, you can use an NVIDIA PyTorch Container, which includes all the necessary installations. You can run this container using Docker and then clone the Megatron-LM repository inside it. Alternatively, you need to install the latest versions of PyTorch, CUDA, NCCL, and NVIDIA APEX, along with the nltk library.
Q: What are the hardware requirements for running Megatron-LM?
Megatron-LM requires significant computational resources, typically a GPU cluster or access to multiple high-performance GPUs. Running these models on a local workstation with a single GPU is not feasible due to the large scale of the models.
Q: How do I install Megatron-LM?
You can install Megatron-LM by cloning the repository from GitHub, checking out the desired version (e.g., core_r0.5.0), and then installing it using pip with the appropriate flags. Additionally, you may need to install NVIDIA APEX and other dependencies.
Q: What is the process for training a language model using Megatron-LM?
Training a language model involves several steps, including setting up the model configuration (e.g., number of layers, hidden size, attention heads), preparing the dataset (using classes like GPTDataset), and configuring the training parameters such as learning rate schedulers and batch sizes. You can use the provided scripts and configurations to fine-tune pre-trained models or train from scratch.
Q: How do I handle data loading and preprocessing in Megatron-LM?
Data loading and preprocessing can be managed using classes like GPTDataset and BlendedMegatronDatasetBuilder. These tools help in creating and managing datasets for training large language models. You may also need to adjust the data pipeline settings according to your specific needs.
Q: What are some common issues to watch out for when using Megatron-LM?
One common issue is ensuring sufficient shared memory when using multi-threaded data loaders, especially in Docker containers. You may need to increase the shared memory size using the --shm-size argument when running the Docker container.
Q: Can I use Megatron-LM for fine-tuning pre-trained models?
Yes, Megatron-LM supports fine-tuning pre-trained models. For example, you can fine-tune a pre-trained LLaMA model on specific data, such as code data, by adjusting the model configurations and training parameters accordingly.
Q: How do I integrate Megatron-LM with other frameworks or libraries?
Megatron-LM can be integrated with other frameworks like Hugging Face’s Transformers. You can use tools from these libraries to manage training, model configurations, and other aspects of your language model development.
Q: What kind of model configurations can I use with Megatron-LM?
You can configure various aspects of the model, such as the number of layers, hidden size, number of attention heads, and sequence length. These configurations are typically defined in files like transformer_config.py and can be adjusted based on your specific requirements.
Megatron LM - Conclusion and Recommendation
Final Assessment of Megatron LM
NVIDIA Megatron LM is a highly advanced and efficient framework for training large transformer-based language models, making it an invaluable tool in the AI-driven research and development landscape.Key Benefits
- Scalability: Megatron LM supports data, model, and pipeline parallelism, allowing for the efficient training of massive models with billions of parameters. This scalability is crucial for handling large-scale language models that would otherwise be computationally infeasible.
- Performance Optimization: It leverages NVIDIA’s Automatic Mixed Precision (AMP) and the latest GPU architectures (such as A100 or V100) to reduce memory usage and accelerate computations. Additionally, it supports FP8 precision, which further boosts compute throughput and reduces memory footprint.
- Parallelism Techniques: Megatron-Core, the core component of Megatron LM, offers various advanced model parallelism techniques including tensor, sequence, pipeline, context, and MoE expert parallelism. This ensures that the framework can scale both within and across nodes efficiently.
Who Would Benefit Most
- Researchers: Those involved in natural language processing (NLP) and generative AI research can significantly benefit from Megatron LM. It enables the training of large language models that can perform competitively in a wide range of NLP tasks without the need for extensive fine-tuning.
- Developers: Developers working on large language models, especially those looking to scale their models to billions of parameters, will find Megatron LM invaluable. It provides a lightweight, research-oriented framework that is compatible with various NVIDIA Tensor Core GPUs and supports multimodal training.
- Organizations: Companies and research institutions aiming to advance the state of the art in AI for natural language generation will benefit from the scalability and performance optimizations offered by Megatron LM. It has already been used in collaborations like the Megatron-Turing NLG model, which is the largest and most powerful monolithic transformer language model to date.