Strategies to optimize performance and costs when using large language models in the cloud

Large Language Model (LLM) has recently started to find its place in the business and will expand further. As the company began to understand the benefits of implementing the LLM, the data team would adjust the model to business requirements.

The optimal path for the company is to use a cloud platform to scale any LLM requirements the company needs. However, many obstacles could hinder the performance of cloud LLM and increase the cost of use. Without a doubt, this is what we want to avoid in business.

That is why this article will try to outline a strategy that you could use to optimize cloud LLM performance while taking care of the cost. What is the strategy? Let's get into it.

We must understand our financial situation before implementing any strategy to optimize performance and costs. The amount of budget we are willing to invest in the LLM will become our limit. A higher budget could lead to more significant performance results, but may not be optimal if it does not support the business.

The budget plan needs thorough discussion with various stakeholders so that it does not become a waste. Identify the critical focus your company wants to solve and evaluate whether LLM is worth investing in.

The strategy also applies to any solo company or individual. Having an LLM budget that you are willing to spend would help with your financial problem in the long run.

With the advancement of research, there are many types of LLM that we can choose to solve our problem. With a smaller parameter model, it would be faster to optimize, but may not have the best ability to solve your business problems. While a larger model has a more excellent knowledge base and creativity, it costs more to calculate.

There are trade-offs between performance and cost with the change in LLM size, which we must take into account when deciding on the model. Do we need to have larger parameter models that have better performance but require higher cost, or vice versa? It is a question we must ask ourselves. So try to evaluate your needs.

Additionally, cloud hardware could also impact performance. Better GPU memory could have faster response time, enable more complex models, and reduce latency. However, larger memory means higher cost.

Depending on the cloud platform, there would be many options for inferences. When comparing the workload requirements of your application, the option you want to choose may also be different. However, the inference could also affect the use of costs since the amount of resources is different for each option.

If we take an example of Amazon SageMaker Inference OptionsYour inference options are:

1. Real-time inference. Inference processes the answer instantly when the information arrives. These are usually the inferences used in real time, such as chatbot, translator, etc. Because it always requires low latency, the application would need high computing resources even in the low demand period. This would mean that an LLM with real-time inference could incur higher costs without any benefit if there is no demand.

1. Serverless inference. This inference is where the cloud platform scales and allocates resources dynamically as needed. Performance could be affected as there would be slight latency each time resources are started for each request. But it is the most profitable since we only pay for what we use.

1. Batch transformation. Inference is where we process the request in batches. This means that inference is only suitable for offline processes, since we do not process the request immediately. It may not be suitable for any application that requires instantaneous processing, as the delay will always be present, but it doesn't cost much.

1. Asynchronous inference. This inference is suitable for background tasks because it runs the inference task in the background while the results are retrieved later. Performance-wise, it is suitable for models that require long processing time as it can handle multiple tasks simultaneously in the background. Cost-wise, it could also be effective due to better resource allocation.

Try to evaluate what your application needs to have the most effective inference option.

LLM is a model with a particular case, since the number of tokens affects the cost we would have to pay. That is why we need to effectively create a message that uses the minimum token for either input or output and at the same time maintains the quality of the output.

Try creating a message that specifies a certain number of paragraph outcomes or use a concluding paragraph such as “summarize,” “concise,” and any other. Additionally, precisely craft the input message to generate the output you need. Don't let the LLM model generate more than you need.

There would be information that would be asked repeatedly and would have the same answers each time. To reduce the number of queries, we can cache all the typical information in the database and call it when necessary.

Typically, the data is stored in a vector database such as Pinecone or Weaviate, but the cloud platform should also have its vector database. The response we want to cache would be converted into vector shapes and stored for future queries.

There are some challenges when we want to cache responses effectively, as we need to manage policies where the cached response is inadequate to answer the input query. Also, some caches are similar to each other, which could cause an incorrect answer. Manage the response well and have an adequate database that can help reduce costs.

The LLMs we implement could end up costing us too much and performing inaccurately if we do not treat them correctly. That's why here are some strategies you could employ to optimize the performance and cost of your cloud LLM:

1. Have a clear budget plan,
2. Decide the correct model size and hardware,
3. Choose the appropriate inference options,
4. Build effective instructions,
5. Response caching.

Cornellius Yudha Wijaya He is an assistant data science manager and data writer. While working full-time at Allianz Indonesia, she loves sharing Python tips and data through social media and print media.