Getting Started with Large Language Models for Enterprise Solutions
As enterprises race to keep pace with AI advancements, identifying the best approach for adopting LLMs is essential. As you explore options to implement large language models (LLMs) for your business solutions, several key factors should guide your decisions. Foundation models provide a useful starting point to accelerate development. Leveraging optimised tools and environments to efficiently process and store data and customise models can significantly boost productivity and advance your business goals. With the right strategy and resources in place, LLMs offer a powerful way to gain a competitive edge.
Introduction to Large Language Models for Enterprises
Large language models (LLMs) enable enterprises to generate new content for a variety of use cases. However, developing and deploying LLMs requires significant compute resources and technical expertise.
NVIDIA’s solutions provide the infrastructure and frameworks needed to implement LLMs. The NVIDIA DGX platform, including DGX Cloud and DGX SuperPOD, offers powerful systems for training and running inference with LLMs. The NVIDIA NeMo framework, part of the NVIDIA AI Enterprise software suite included with DGX systems, streamlines building and customising LLMs.
NeMo provides end-to-end capabilities for developing generative AI models, including:
- Tools for data curation and distributed training of text-to-text, text-to-image, and image-to-image models
- Customization techniques like prompt learning and reinforcement learning with human feedback for adapting models to enterprise data and use cases
- Optimizations enabling fast, scalable inference across multiple GPUs and DGX nodes
With NeMo and DGX, enterprises can:
- Train models on their own data using the NeMo framework and DGX systems.
- Customise pretrained or self-trained models for their domain and tasks using techniques like prompt learning or reinforcement learning with human feedback.
- Deploy models at scale for low-latency, high-throughput inference using DGX infrastructure on-premises or in the cloud.
The NVIDIA Base Command platform provides a control plane to manage the infrastructure and workloads supporting LLM development across environments. Enterprises also gain access to NVIDIA’s AI experts, who can provide guidance on everything from getting started with LLMs to optimising production systems.
With powerful infrastructure, optimised software, and AI expertise, NVIDIA enables businesses to adopt LLMs and achieve their AI goals. The combination of DGX platforms, NeMo, and NVIDIA’s support accelerates building, deploying, and improving custom LLMs tailored to enterprise missions.
Benefits of Using Pre-Trained Models Like Megatron Turing NLG
As enterprises adopt large language models (LLMs) like Megatron Turing Natural Language Generation
(NLG) for generative AI solutions, using pretrained models can significantly accelerate productivity and advance business goals.
Benefits of Using Pre-Trained Models
Pretrained models contain broad general knowledge which acts as a foundation to build upon. By fine-tuning these models with enterprise data, you can create customised solutions tailored to your needs without starting from scratch. This reduces the time, cost and resources required to develop in-house models.
Megatron Turing NLG is a pretrained LLM model developed by NVIDIA to generate coherent, fluent long-form text. As it has been trained on massive datasets, it can produce high-quality samples for a variety of use cases out-of-the-box. Enterprises can harness its capabilities to enhance existing systems or develop new solutions, such as intelligent assistants, content creation tools or document summarisation software.
Using tools like prompt learning or reinforcement learning with human feedback, you can specialise Megatron Turing NLG for your industry and use cases. By providing examples and feedback, the model learns to generate text that suits your needs. Its knowledge and skills improve over time through continuous fine-tuning.
The NeMo framework streamlines the process of customising and deploying Megatron Turing NLG at scale. With NeMo, you can build on the model’s broad capabilities, add custom skills, define a narrower focus and put guardrails in place to align it with your values. This framework, coupled with the DGX infrastructure, allows you to develop and deploy LLMs efficiently while maintaining control over the models and the data used to train them.
In summary, pretrained models like Megatron Turing NLG provide a strong foundation for generative AI. When customised for your enterprise using tools like NeMo, they become even more powerful and capable of generating content that achieves your business goals. The time saved by using a pretrained model as a starting point allows you to get solutions up and running faster, so you can realise the benefits sooner.
Customising Models With Tools Like NVIDIA NeMo Framework
NVIDIA NeMo offers an end-to-end framework for building customised generative AI models at scale. The NeMo framework streamlines the development process for large language models (LLMs) and other generative AI systems, providing computational efficiency and scalability for cost-effective training on NVIDIA DGX systems.
The NeMo framework provides tools to customise LLMs and other generative models with enterprise-specific data and knowledge. Techniques like prompt learning, adapter modules, and reinforcement learning with human feedback enable continuous improvement by incorporating feedback into the training loop. These methods allow enterprises to adapt foundation models to their unique use cases and data without retraining from scratch.
Scaling and Deploying Models
After customising a model, the NeMo framework provides optimised and validated techniques for scaling and deploying the model on NVIDIA DGX infrastructure, whether on-premises, in the cloud, or both. The autoconfiguration tool can find optimal configurations for training and inference to maximise throughput and minimise latency. Multi-node, multi-GPU inference can achieve high throughput and low latency for production workloads.
Because AI technologies and data are constantly evolving, continuous improvement of models is essential. The NeMo framework provides tools and guidance for retraining models on new data and evaluating the impact of updates. Retraining only parts of a model, like the final layers, can quickly adapt to new data without starting from scratch. Monitoring model performance and user feedback helps determine when retraining and updates will have the biggest impact.
The NVIDIA NeMo framework, powered by NVIDIA DGX infrastructure, provides an end-to-end enterprise solution for building, deploying and continuously improving generative AI. By simplifying the development of complex models, the NeMo framework helps enterprises achieve their AI goals faster and at lower cost. Paired with world-class support from NVIDIA, the NeMo framework on DGX is the fastest path to delivering custom generative AI.
Leveraging NVIDIA DGX Infrastructure for Efficient Training
Leveraging NVIDIA DGX Infrastructure for Efficient Training
To build and deploy enterprise-ready generative AI solutions, businesses require infrastructure that can handle training complex models on massive datasets. The NVIDIA DGX platform, comprising DGX systems, DGX PODs, and DGX Cloud, provides the compute performance and scalability for enterprises to develop advanced generative AI models.
DGX systems offer a modular and scalable AI infrastructure for enterprises. The DGX A100 is NVIDIA’s universal system for AI, providing state-of-the-art performance for training, inference, and data analytics in a single solution. Multiple DGX A100 systems can be clustered together into DGX PODs, delivering supercomputing performance to accelerate AI development. For enterprises seeking maximum performance and scale, the DGX SuperPOD delivers over 700 petaFLOPS of AI performance, enabling training of models with over a trillion parameters.
DGX Cloud provides enterprises with a cloud-based AI infrastructure, enabling prototyping and scaling of AI workloads without upfront capital investment. Resources can be provisioned on demand, allowing enterprises to start small and scale up as needed. Workloads can be seamlessly migrated between DGX Cloud and on-premises DGX systems, providing flexibility and optimising infrastructure utilisation.
The NVIDIA NeMo framework, an enterprise-ready AI software platform included with DGX systems and DGX Cloud, simplifies and accelerates the development of advanced generative AI models. NeMo provides computational efficiency and scalability for training models with billions of parameters across multiple DGX nodes. State-of-the-art distributed training techniques such as sequence parallelism and selective activation recomputation can deliver up to 30% faster training times for large language models.
With the combined power of DGX infrastructure and NeMo, enterprises have a complete solution to build, deploy and scale custom generative AI models to drive business transformation. By leveraging NVIDIA’s ecosystem of partners and AI services, enterprises can further accelerate their AI journey and achieve a competitive advantage with generative AI.
Next Steps: Partnering With AsiaPac to Implement Solutions
As enterprises seek to implement generative AI solutions, partnering with experienced providers can help ensure successful outcomes. NVIDIA’s solutions, including the NVIDIA DGX platform and NVIDIA NeMo framework, provide the infrastructure and tools to build custom generative AI models. However, enterprises may require additional support to fully leverage these technologies.
A Wealth of Experience
AsiaPac has over a decade of experience helping enterprises implement AI solutions. Our consultants have in-depth knowledge of NVIDIA’s products and services, as well as expertise in data management, distributed training techniques, and reinforcement learning. By partnering with AsiaPac, enterprises can tap our experience to accelerate their AI development and gain valuable guidance on infrastructure deployment, model customization, and scaling for production.
Specifically, AsiaPac offers the following services:
- Infrastructure design and management. We help determine optimal infrastructure for enterprises’ needs and handle setup and maintenance of NVIDIA DGX systems, including on-premises and cloud-based deployments.
- Data curation and model training. Our data scientists have experience training a variety of generative AI models, including text-to-text, text-to-image, and image-to-image models using massive datasets. We leverage techniques like sequence parallelism and selective activation recomputation to achieve faster training times.
- Model customization. We apply techniques such as prompt learning, adapter modules, and reinforcement learning with human feedback to customise generative AI models with enterprise data and objectives. This helps ensure models generate relevant and high-quality content.
- Production scalability. We have experience running inference for large-scale generative AI models on multiple GPUs and nodes to achieve low-latency, high-throughput performance. This allows enterprises to serve models at scale while minimising resource consumption.
By partnering with AsiaPac, enterprises can tap our expertise and experience to overcome obstacles, avoid pitfalls, and achieve AI success. We provide guidance and support for enterprises seeking to implement custom generative AI solutions tailored to their unique needs.
In summary, large language models provide significant opportunities for enterprises to leverage AI and gain a competitive advantage. The key is finding the right approach and tools to implement these advanced models efficiently and effectively. With a solid foundation in place built on GPU-accelerated infrastructure and environments tailored to your data and use cases, you’ll be well on your way to unlocking the potential of LLMs and shaping the future of your business. The AI revolution is here – are you ready to join it?
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