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The Large Language Model (LLM) has recently begun to find a place in this field, and will expand further. As the company begins to understand the benefits of implementing an LLM, the data team will modify the model to fit the business requirements.
The optimal path for a business is to use a cloud platform to scale any LLM requirements the business needs. However, there are several obstacles that may hinder the performance of LLM in the cloud and increase the cost of use. This is definitely what we want to avoid at work.
That’s why this article will try to outline the strategy you can use to improve LLM performance in the cloud while paying attention to cost. What is the strategy? Let’s get into it.
We must understand our financial situation before implementing any strategy to improve performance and costs. The amount of budget we are willing to invest in LLM will become our maximum. A higher budget can lead to more significant performance results but may not be ideal if it does not support the business.
The budget plan needs to be thoroughly discussed with various stakeholders so that it does not become a waste. Identify the critical focus your business wants to solve and evaluate whether an LLM is worth investing in.
The strategy also applies to any single business or individual. Having a LLM budget that you are willing to spend will help solve your financial problem in the long run.
As research progresses, there are many types of MBA that we can choose to solve our problem. With a smaller parameter model, the optimization will be faster but may not have the best ability to solve your business problems. While a larger model has a better knowledge and creativity base, it costs more to compute.
There are trade-offs between performance and cost with the change in LLM size, which we need to take into account when we decide on the model. Do we need models with larger parameters that have better performance but require higher cost, or vice versa? It’s a question we must ask. So, try to evaluate your needs.
In addition, cloud hardware may affect performance as well. Better GPU memory may have faster response times, allow for more complex models, and reduce latency. However, higher memory means higher cost.
Depending on the cloud platform, there will be several options for inferences. When comparing your application’s workload requirements, the option you want to choose may also be different. However, heuristics may also affect cost utilization due to the different number of resources for each option.
Taking an example from Amazon SageMaker Inferences Options, your inference options are:
- Real-time inference. Heuristics process the response immediately when the input comes. These are usually heuristics used in real time, such as a chatbot, translator, etc. Since it always requires low latency, the application will need high computing resources even in a period of low demand. This means that LLM with real-time inference can lead to higher costs without any benefit if demand is not there.
- Serverless inference. This heuristics is where the cloud platform dynamically scales and allocates resources as required. Performance may be affected as there will be a slight latency each time resources are initiated for each request. But it is the most cost effective because we only pay for what we use.
- Batch conversion. Inference is where we process the order in batches. This means that heuristics are only suitable for offline operations because we do not process the request immediately. It may not be suitable for any application that requires immediate processing because the delay will always be there, but it does not cost much.
- Asynchronous inference. This heuristics is suitable for background tasks because it runs the heuristics task in the background while retrieving the results later. In terms of performance, it is suitable for models that require a long processing time as it can handle different tasks simultaneously in the background. In terms of cost, it can also be effective due to better allocation of resources.
Try to evaluate what your application needs, so you have the most effective heuristics option.
LLM is a model for a specific situation, where the number of tokens affects the cost we have to pay. That’s why we need to effectively create a router that uses the minimum amount of token for either input or output while maintaining output quality.
Try creating a prompt that outlines a certain amount of paragraph output or use a concluding paragraph such as “Summary,” “Summary,” and any other paragraphs. You can also precisely create the input vector to create the output you need. Don’t let the LLM model generate more than you need.
There will be information that will be asked repeatedly and have the same answers every time. To reduce the number of queries, we can cache all the sample information in the database and call it when needed.
Typically, data is stored in a vector database such as Pinecone or Weaviate, but a cloud platform should have a vector database as well. The response we want to cache will be converted into vector forms and stored for future queries.
There are some challenges when we want to cache responses effectively, as we need to manage policies where the cache response is insufficient to answer the input query. Also, some caches are similar to each other, which may lead to an incorrect response. Managing the response well and having an adequate database can help reduce costs.
The LLM we deploy can end up costing us a lot and performing inaccurately if we don’t handle it properly. For this reason, here are some strategies you can use to improve the performance and cost of your MBA in the cloud:
- To have a clear budget plan,
- Determine the model size and appropriate hardware,
- Choose appropriate inference options,
- Build effective claims,
- Caching responses.
Cornelius Yoda Wijaya He is the Assistant Director of Data Science and Data Writer. While working full-time at Allianz Indonesia, he loves sharing Python tips and data via social media and writing media.
Large language models have become increasingly popular in various industries for their ability to process and generate human-like text. However, utilizing these models in the cloud can come with high costs and performance issues. Therefore, it is crucial for organizations to implement effective strategies to improve both the performance and cost efficiency of using large language models in the cloud. By implementing these strategies, organizations can maximize the benefits of large language models while minimizing their drawbacks. In this article, we will explore some key strategies that can help organizations improve their performance and costs when using large language models in the cloud.