Maximizing your ROI with serverless LLMs
JUN. 27, 2024
There is a way to harness the power of large language models without the operational headaches.
In just the last year or two, large language models (LLMs) have gone from a niche, technical term to a household name.
Business leaders recognize their immense potential, but turning that potential into tangible ROI is a whole different ball game.
These complex AI models have a gargantuan appetite for data and computational resources. The costs and complexities associated with managing the underlying infrastructure can quickly snowball, turning LLM adoption into a resource-intensive nightmare.
But there is a way to harness the power of large language models without the operational headaches: serverless LLMs.
What are serverless LLMs, and how do they work?
Serverless LLMs represent a fundamental shift in how we deploy and interact with these powerful AI models.
Many organizations don’t have the desire or bandwidth to pay for and manage servers themselves. With serverless architectures, you can utilize the capabilities of cutting-edge language models without the burden of owning and maintaining the underlying infrastructure.
The traditional LLM deployment model: A costly and complex endeavor
“LLMs demand a ton of energy, plain and simple,” says Lumenalta’s Donovan Crewe. “For most businesses, one or two servers isn’t enough. You need to invest in a whole fleet of them to handle the immense computational needs of these models.”
These servers require specialized graphics processing units (GPUs), and they don’t come cheap. For instance, training OpenAI’s GPT-3 necessitates thousands of GPUs and can involve processing hundreds of petaFLOP/s-days of computation.
Organizations should expect a significant capital outlay and a setup time of several weeks or months. And the costs don’t end there. Maintaining this infrastructure requires ongoing expenses for:
- Hardware: Upgrading servers, replacing faulty components, and ensuring sufficient capacity to meet growing demands
- Software: Licensing operating systems, databases, and other essential software to keep the servers running smoothly
- Personnel: Hiring and training IT staff to monitor and troubleshoot the server environment
- Energy: Paying for the electricity to power and cool the servers, which can be substantial
The serverless LLM model: Agility, efficiency, and cost savings
Unless your company requires significant workloads or ultra-low latency, serverless LLMs are likely your best bet. You get nearly all the functionality of on-premises servers at a fraction of the cost.
Instead of relying on physical servers, your LLM code is executed on demand within a cloud provider’s infrastructure. You only pay for the compute resources your model actually consumes during inference.
If you need more compute in one month, you pay more, and vice versa. It’s like renting a car for a road trip — you’re billed for the miles you drive, not the entire vehicle.
All the setup, maintenance, and management of servers are dealt with by the cloud provider. Your team can instead focus on building more effective large language model applications.
The ROI advantages of serverless LLMs
Being able to provision computing resources on-demand can lead to big savings for organizations in the short and long terms.
Cost reductions
Purchasing servers yourself can be a gamble. To be worth the significant upfront investment, you’d better be sure that you’ll use all that compute and have the resources required to keep the infrastructure up and running.
Factor in the recurring expenses of server maintenance, cooling, and electricity, and your expenses can quickly spiral before your LLM even starts delivering value.
Few businesses know exactly how much compute they’ll need next year, much less 5 or 10 years down the line. For them, a pay-as-you-go model is the optimal choice — instead of wasteful spending on idle servers, you can buy computing power depending on your needs for the month or year.
Scalability and application speed
Physical server networks aren’t exactly known for their adaptability. Imagine trying to expand a restaurant overnight — it’s disruptive, costly, and usually results in overspending or underperformance.
Modern serverless architectures are just the opposite, seamlessly handling thousands of concurrent executions and automatically scaling up or down to meet your evolving needs. This translates directly to faster response times for your LLM applications.
Unexpected traffic surges? No problem. Serverless scaling ensures your applications can handle them effortlessly, guaranteeing a consistent user experience and eliminating the need to overprovision resources during periods of low demand.
Operational efficiency
Businesses that invest in on-premises servers usually have dozens of staff dedicated to keeping them in tip-top shape.
From server provisioning and security patches to operating system updates and network configurations, managing this infrastructure isn’t for the faint of heart. It demands specialized knowledge, constant vigilance, and meticulous maintenance.
Delegating this upkeep to cloud providers frees up your IT team to focus on more strategically aligned tasks: developing innovative large language model applications, fine-tuning models, and delivering exceptional user experiences.
Best practices for maximizing ROI with serverless LLMs
Optimize your models
Size matters in large language model architecture. Smaller, more efficient models that incorporate fewer parameters tend to perform better in a serverless LLM environment.
Explore model optimization techniques like quantization (reducing the precision of model parameters) and pruning (removing less important connections) to trim down your LLM without sacrificing performance. Doing so will lead to faster response times, reduced resource consumption, and a stronger return on your investment.
Choose the right provider
Not all serverless platforms are created equal. Each offers a unique set of features, pricing models, and integrations.
As you peruse different options, consider factors like:
- Workload: How often will your LLM be used, and what level of traffic do you anticipate?
- Model size: Are you working with smaller, specialized models or larger, more complex ones?
- Performance requirements: What level of latency and throughput do you need?
- Budget: How much are you willing to invest in serverless LLM infrastructure?
- Existing tech stack: Will it seamlessly integrate with your current tools and workflows?
Also, look for features like robust monitoring and logging tools. These will give you crucial insights into your LLM’s performance and resource utilization, allowing you to proactively identify bottlenecks and optimize efficiency.
Monitor and analyze
Just as you wouldn’t embark on a road trip without checking your tire pressure, you shouldn't deploy a serverless LLM without the right monitoring tools in place.
Keep a watchful eye on key metrics like response times, error rates, and resource utilization. Most cloud providers offer comprehensive monitoring tools that help keep your LLMs running at peak performance.
Plan for cold starts
Serverless functions are prone to speed bumps known as “cold starts.” Think of it like starting your car on a chilly morning — it takes a moment to warm up and get moving. Similarly, if your LLM hasn’t been used in a while, there might be a slight delay as the cloud provider fires up the necessary resources.
Most of the time, you won’t even notice this minor delay. But if your application demands lightning-fast responses — like a high-frequency trading algorithm or a real-time language translation service — cold starts can be a nuisance.
If you think cold starts might affect your applications’ performance, consider implementing strategies like provisioned concurrency. This allows you to keep a certain number of LLM instances “warm” and ready to respond instantaneously, even during periods of sporadic usage.
Embrace experimentation
Serverless architectures are inherently agile and adaptable. They encourage experimentation and empower you to explore new possibilities with LLMs. Test out different models, configurations, and use cases — you might be surprised at the innovative solutions you discover.
Don’t bog your team down with the complexities of infrastructure management. Access the power of AI minus the headaches with serverless LLMs.
