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Large language models, or LLMs, have emerged as a driving catalyst in natural language processing. Their use cases range from chatbots and virtual assistants to content generation and translation services. Yet they have become one of the fastest-growing fields in tech—and we can find them everywhere.

As the need for more powerful language models increases, so does the need for effective optimization techniques.

However, many logical questions also arise:

How can they improve their knowledge?
How can they improve their overall performance?
How can we scale these models?

The insightful talk titled “A Survey of Techniques for Maximizing LLM Performance” by John Allard and Colin Jarvis from OpenAI DevDay attempted to answer these questions. If you missed the event, you can watch the talk on YouTube.
This presentation provided an excellent overview of various techniques and best practices for improving the performance of your LLM applications. This article aims to summarize the best techniques to improve both the performance and scalability of our AI-powered solutions.

Understanding the Basics

LLMs are advanced algorithms designed to understand, analyze, and produce coherent and contextually appropriate text. They achieve this through extensive training on large amounts of linguistic data spanning diverse topics, dialects, and styles, in order to understand how human language works.

However, when integrating these models into complex applications, we must consider a number of important challenges:

Key challenges in optimizing LLMs

• Accuracy of LLMs: ensuring that LLM outcomes contain accurate and reliable information, without hallucinations.
• Resource consumption: LLMs require significant computing power, including GPU power, memory, and large infrastructure.
• Latency: Real-time applications require low latency, which can be a challenge given the size and complexity of LLMs.
• Scalability: As user demand increases, it is critical that the model can handle increased load without degrading performance.

Strategies for Better Performance

The first question is, “How can we improve their knowledge?”

Creating a partially functional LLM demo is relatively straightforward, but refining it for production requires iterative improvements. LLMs may need help with tasks that require deep knowledge of specific data, systems, and processes, or precise behaviors.

Teams use prompt engineering, retrieval augmentation, and fine-tuning to address this. A common mistake is to assume that this process is linear and must be followed in a specific order. Instead, it is more effective to approach it along two axes, depending on the nature of the problems:

1. Context optimization: Are the problems caused by the model not having the right information or knowledge?
2. LLM optimization: Is the model not producing the right output? For example, is it inaccurate or does it not conform to the desired style or formatting?

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To address these challenges, three primary tools can be deployed, each playing a unique role in the optimization process:

Fast technique

Adjust the prompts to guide the model’s responses. For example, refining the prompts of a customer service bot to ensure it consistently provides helpful and polite responses.

Retrieval Augmented Generation (RAG)

Improving the contextual understanding of the model using external data. For example, integrating a medical chatbot with a database of the latest research articles to provide accurate and up-to-date medical advice.

Fine tuning

Adapting the base model to better suit specific tasks. Such as refining a legal document analysis tool using a dataset of legal texts to improve accuracy in summarizing legal documents.

The process is highly iterative, and not every technique will work for your specific problem. However, many techniques are additive. When you find a solution that works, you can combine it with other performance improvements to achieve optimal results.

Strategies for optimized performance

The second question is, “How can we improve their overall performance?”
After having an accurate model, a second concern is inference time. Inference is the process by which a trained language model, such as GPT-3, generates responses to prompts or questions in real-world applications (such as a chatbot).
It is a critical phase where models are put to the test and predictions and responses in practical scenarios are generated. For large LLMs such as GPT-3, the computational demands are enormous, making optimization during inference essential.
Consider a model like GPT-3, which has 175 billion parameters, which is equivalent to 700 GB of float32 data. This size, coupled with the activation requirements, requires significant RAM. Therefore, running GPT-3 without optimization would require an extensive setup.
There are a number of techniques that can be used to reduce the amount of resources required to run such applications:

Model pruning

It involves trimming non-essential parameters, leaving only those critical to performance. This can drastically reduce the size of the model without significantly compromising accuracy.
Which means a significant reduction in computational burden, while maintaining the same accuracy. You can find easy to implement pruning code in the following GitHub.

Quantization

It is a model compression technique that converts the weights of an LLM from high-precision variables to lower-precision variables. This means that we can reduce the 32-bit floating-point numbers to lower-precision formats such as 16-bit or 8-bit, which are more memory-efficient. This can drastically reduce the memory footprint and improve inference speed.

LLMs can be easily loaded in a quantized manner using HuggingFace and bitsandbytes. This allows us to execute and refine LLMs in lower power sources.

from transformers import AutoModelForSequenceClassification, AutoTokenizer 
import bitsandbytes as bnb 

# Quantize the model using bitsandbytes 
quantized_model = bnb.nn.quantization.Quantize( 
model, 
quantization_dtype=bnb.nn.quantization.quantization_dtype.int8 
)

Distillation

It is the process of training a smaller model (student) to mimic the performance of a larger model (also called a teacher). This process involves training the student model to mimic the teacher’s predictions, using a combination of the teacher’s output logits and the real labels. By doing this, we can achieve similar performance with a fraction of the required resources.

The idea is to transfer the knowledge of larger models to smaller models with simpler architecture. One of the best known examples is Distilbert.

This model is the result of imitating Bert’s performance. It is a smaller version of BERT that retains 97% of its language comprehension ability, while being 60% faster and 40% smaller.

Scalability techniques

The third question is “How can we scale these models?”
This step is often crucial. An operational system can behave very differently when used by a handful of users than when it is scaled to accommodate intensive use. Here are some techniques to address this challenge:

Load balancing

This approach efficiently distributes incoming requests, ensuring optimal use of computing power and dynamic response to demand fluctuations. For example, to offer a widely used service like ChatGPT in different countries, it is better to deploy multiple instances of the same model.
Effective load balancing techniques include:
Horizontal scalability: Add more model instances to handle increased load. Use container orchestration platforms like Kubernetes to manage these instances across nodes.
Vertical scalability: Upgrade existing machine resources, such as CPU and memory.

Shards

Model sharding distributes segments of a model across multiple devices or nodes, enabling parallel processing and significantly reducing latency. Fully Sharded Data Parallelism (FSDP) provides the key benefit of using a diverse set of hardware, such as GPUs, TPUs, and other specialized devices across clusters.

This flexibility allows organizations and individuals to optimize their hardware resources based on their specific needs and budget.

Caching

Implementing a caching mechanism reduces the load on your LLM by storing frequently accessed results, which is especially beneficial for applications with repetitive queries. Caching these frequent queries can significantly save computational resources by eliminating the need to repeatedly process the same requests.

In addition, batch processing can optimize resource usage by grouping similar tasks together.

Conclusion

For those building applications that rely on LLMs, the techniques discussed here are critical to maximizing the potential of this transformative technology. Mastering and effectively applying strategies to more accurately output our model, optimize its performance, and enable scaling are essential steps in evolving from a promising prototype to a robust, production-ready model.
To fully understand these techniques, I highly recommend you delve deeper into them and experiment with them in your LLM applications for optimal results.

Josep Ferrer is an analytics engineer from Barcelona. He graduated in physics and currently works in the field of data science, applied to human mobility. He is a part-time content creator focused on data science and technology. Josep writes about everything related to AI, and covers the application of the ongoing explosion in the field.