The Pros and Cons of Using LLMs in the Cloud Versus Running LLMs Locally

In recent months, we have witnessed remarkable advancements in the realm of Large Language Models (LLMs), such as ChatGPT, Bard, and LLaMA, which have revolutionized the entire industry. Gain insight into the evolution of LLMs by reading What is GPT-4 and Why Does it Matter?

The emergence of Open Assistant, Dolly 2.0, StableLM, and other open-source projects have introduced commercially licensed LLMs that rival the capabilities of ChatGPT. It means that individuals with technical expertise now have the opportunity to fine-tune and deploy LLMs on either cloud-based platforms or local servers. Such accessibility has democratized the usage of LLMs, empowering a wider range of users to harness their potential.

As access to cutting-edge models becomes more open, the time has come to devise an optimal deployment strategy for LLMs. To achieve this, it is crucial to weigh the advantages and disadvantages of running LLMs on either cloud-based or local servers.

Pros of Using LLMs in the Cloud

Let’s explore some of the benefits of using large language models in the cloud:

Scalability

The training and deployment of LLMs require extensive computing resources and data storage. At times, training processes demand multiple instances of high-end GPUs, which can only be met through cloud-based services that offer scalable resources on demand.

Cost efficiency

If you lack high-end hardware to run large language models, opting for the cloud can prove to be a more cost-effective solution. With cloud services, you only pay for the resources you utilize, and often, GPUs and CPUs are available at more affordable rates.

Ease of use

The cloud platform offers a range of APIs, tools, and language frameworks that greatly simplify the process of building, training, and deploying machine learning models.

Managed services

Cloud providers are responsible for handling the setup, maintenance, security, and optimization of the infrastructure, thereby significantly reducing the operational overhead for users.

Pre-trained models

Cloud platforms now offer access to the latest pre-trained large language models that can be fine-tuned on custom datasets and effortlessly deployed on the cloud. It can be quite useful for creating an end-to-end machine-learning pipeline.

Read 12 GPT-4 Open-Source Alternatives to learn about other popular open-source development in language technologies.

Check out the list of cloud platforms that provide tools and pre-trained models:

• NVIDIA: NeMo Large Language Models (LLM) Cloud Service
• Hugging Face: Inference Endpoints
• AWS: Amazon Titan
• MosaicML: Inference
• Paperspace: The GPU cloud built for Machine Learning

Cons of Using LLMs in the Cloud

Of course, as with any technology, there are some downsides to using large language models in the cloud:

Loss of control

When using cloud-managed ML service, you have less control and visibility over infrastructure and implementation.

Vendor lock-in

If you have trained LLMs on one cloud platform, it will be difficult to port to a different platform. Furthermore, depending solely on a single cloud provider entails inherent risks, particularly concerning policy and price fluctuations.

Data privacy and security

Your data resides on the cloud provider's servers across various regions worldwide, so you have to trust them to keep your data secure.

High costs

Training and running LLMs at scale can still be quite expensive. The costs for computing and storage resources can add up over time.

Network latency

There are some delays when communicating with models running in the cloud, making it less ideal for real-time applications.

New to cloud computing? Read Cloud Computing and Architecture for Data Scientists and learn how to deploy data science solutions to production.

Pros of Running LLMs Locally

Now we’ve explored the benefits and drawbacks of running large language models in the cloud, let's look at the same points when it comes to running them locally. The pros include:

More control

You have more control over the hardware, trained model, data, and software you use to run the service. You can set up to comply with specific regulations, optimize the training and inference process, and improve the performance of the LLMs.

Lower costs

If you already have the necessary hardware, then running it locally can be cheaper than paying cloud costs.

Reduced latency

Running a large language model locally can offer notable advantages in terms of latency, resulting in reduced response time between making a request and receiving a model's response. This aspect holds significant importance, particularly in applications like chatbots or live translation services that heavily rely on real-time responses.

Greater privacy

By training and running LLMs locally, you gain enhanced control over your data and models, enabling you to establish robust safeguards to protect sensitive information.

Cons of Running LLMs Locally

Here are some of the downsides of running large language models locally:

Higher upfront costs

Setting up local servers for running large language models can be costly if you lack high-end hardware and software.

Complexity

Running LLMs locally can be challenging, time-consuming, and comes with operational overhead. There are many moving parts, and you must set up and maintain both the software and the infrastructure.

Limited scalability

You cannot upscale or downscale on demand. Running multiple LLMs may require more computational power than what is feasible on a single machine.

Availability

Local servers are less resilient. In the event of system failures, access to your LLMs is jeopardized. On the other hand, cloud platforms offer multiple layers of redundancy and exhibit lower downtime.

Accessing pre-trained models

Access to the latest state-of-the-art large language models for fine-tuning and deployment may not be readily available to you.

Read about ChatGPT and The Future of AI Regulations to learn about new AI regulations and tackle the potential dangers of next-generation AI.

Factors to Consider When Choosing a Deployment Strategy for LLMs

Scalability needs

How many users do you currently have, and how many models do you need to run in order to meet the requirements? Additionally, are you planning to utilize the data for model improvement? This information will determine whether a cloud-based solution is necessary.

Data privacy and security requirements

Do you operate in a domain where user privacy and data protection are paramount? Are there strict data privacy laws or corporate policies in place? If the answer is yes, it is necessary to develop an on-premises solution.

Cost constraints

If you are working with a limited budget and have access to hardware that can handle the task, running the models locally may prove to be more cost-effective.

Ease of use

If you possess lower technical skills or have a limited team, deploying and managing models can be challenging. In such cases, cloud platforms often offer plug-and-play tools that simplify the process, making it more accessible and manageable.

Need for latest models

Do you have access to Large Language Models? Cloud platforms usually provide access to the latest state-of-the-art models, ensuring you can leverage the most advanced capabilities available.

Predictability

You can manage the cost of on-premise infrastructure. This allows you to predict the budget, as opposed to the variable costs associated with utilizing cloud services.

Vendor lock-in Issues

On-premises infrastructure mitigates this risk of vendor lock-in but requires more self-maintenance.

Network latency tolerance

If your application necessitates real-time responses and lower latency, then choosing a local setup is the optimal choice for achieving the desired performance.

Team expertise

If your team is already familiar with cloud tools and services, choosing the cloud option is the ideal choice. Implementing a new solution and learning new tools can incur costs in terms of time, money, and human resources.

Conclusion

In this post, we have discussed both pros and cons of running LLMs in the cloud versus locally. The optimal deployment strategy for LLMs depends on the size and complexity of the LLM, the specific needs of the application, the budget, and the security and privacy requirements.

In short,

• Businesses with budget constraints or a preference for greater control can choose to run LLMs locally.
• Businesses seeking streamlined LLM deployment solutions and ease of use can opt for Cloud.

Ultimately, the decision rests with you. It is crucial to carefully evaluate and weigh the advantages and disadvantages of each approach before arriving at a well-informed decision.

If you'd like to expand your knowledge on this subject, check the Private Cloud vs. Public Cloud: Which One to Choose? article.