There is no escaping the excitement and potential that generative AI has been commanding recently, particularly with regard to large language models (LLMs). The release of GPT-4 in March 2023 has produced a strong gravitational pull, resulting in enterprises making clear and intentional moves toward the adoption of the latest LLM technology.
As a result, other technology companies have increased their investments and efforts to capitalize on large language models’ potential, resulting in the release of LLMs from Microsoft, Google, Hugging Face, NVIDIA, and Meta, to name a few.
The rush by enterprises to adopt and deploy large language models to production should be tempered with the same due diligence that is applied to other technology implementations. We have seen some unfortunate and very public issues with LLM adoption exposing sensitive internal intellectual property, as well as governmental actions putting the brakes on adoption.
In this article, we will be looking into how enterprises can overcome the challenges associated with large language models’ deployment and produce desired business outcomes. We’ll look at some common myths surrounding LLM deployment and address misconceptions, such as, “The bigger the model, the better,” and, “One model will do everything.” We’ll also explore best practices in LLM deployment, focusing on key areas such as model deployment, optimization, and inferencing.
Challenges to be considered with large language models enterprise deployments fall into some of the following categories:
- Complex engineering overhead to deploy custom models within your own secure environment
- Infrastructure availability can be a blocker (e.g., GPUs)
- High inferencing costs as you scale
- Long time to value/ROI
It’s one thing to experiment with large language models that are trained on public data, versus training and operationalizing LLMs on your enterprise data within the constraints of your environment and market. When it comes to LLMs, your model must infer across large amounts of data in a complex pipeline, and you must plan for this in the development stage. Will you need to add compute nodes? Can you build your model to optimize hardware utilization by automatically adjusting the resources allocated to each pipeline based on load relative to other pipelines, making scaling more efficient? For example, GPT-4 reportedly has 1.75 billion parameters, which requires significant compute processing power (typically GPUs).
Deploying large language models in enterprise companies may entail processing hundreds of gigabytes of enterprise data per day, which can pose challenges in terms of performance, efficiency, and cost. Deploying LLMs requires a significant amount of infrastructure resources, such as computing power, storage, bandwidth, and energy as well as optimizing the architecture and infrastructure to meet the demands and constraints of the specific use case and domain. These complexities of deploying LLMs to production include aspects such as the size and resource requirements of LLMs, which can exceed hundreds of gigabytes and can require specialized hardware, such as GPUs or TPUs, to run efficiently.
These resources involved in deployment have both financial and environmental costs, which may not be affordable, justifiable, or sustainable for some organizations or applications. As such, before deploying a large language model, it is important to evaluate whether the expected performance and business impact of the model are worth the investment and trade-offs involved. Some factors to consider are the accuracy, reliability, scalability, and ethical implications of the model, as well as the availability of alternative solutions that may achieve similar or better results with less resource consumption.
The potential outcome of these challenges is a long time to value (ROI), which in turn could put the whole project at risk of being shelved. These challenges must be considered in the early planning and development stages to help set up the business for a successful rollout of large learning models.
One method to overcome these challenges and accelerate training of large language models is to use an open-source model that can leverage the existing knowledge and data of the model. This can save time and resources compared to building your own model from scratch. Also, you can customize and fine-tune the open-source model to suit your specific needs and goals, which can improve the performance and accuracy of your model for your domain and use cases.
Using open-source models to train smaller use case-specific models is a practice that can help enterprises avoid falling into the “one model to rule them all” trap, in turn improving deployment times and managing inference costs. Depending on the applications and use cases, smaller models can be distributed across infrastructure according to the best methods for optimization. For example, a summarization model could be deployed across a joint CPU and GPU configuration. GPUs are expensive and, at the time of writing, at premium availability. Being able to spread the model across a flexible infrastructure can help with desired inference times and lower inference costs without sacrificing deployment time and incremental value to the enterprise.