How to Make LLMs Faster: Combining First Principles Thinking and Technical Approaches



Aug 14, 20234 min read


How to Make LLMs Faster: Combining First Principles Thinking and Technical Approaches


Language Models (LLMs) have become integral in various fields, from natural language processing to machine translation. However, their size and computational requirements can often hinder their speed and efficiency. In this article, we will explore both technical approaches and the application of Elon Musk's "3-Step" First Principles Thinking to make LLMs faster and more effective.

First Principles Thinking:

Elon Musk, known for his innovative thinking, emphasizes the power of first principles reasoning. He suggests breaking down problems into fundamental principles and creating new solutions from scratch. By applying this approach to LLMs, we can challenge existing assumptions and find unique ways to accelerate their performance.

Step 1: Identify and Define Assumptions:

To make LLMs faster, it is crucial to question the assumptions underlying their design and functionality. By identifying and defining these assumptions, we can uncover potential areas for improvement. For example, the assumption that LLMs need a large number of parameters for effective performance can be challenged.

Step 2: Breakdown into Fundamental Principles:

Once we have identified and defined our assumptions, it is important to break down the problem of LLM speed into its fundamental principles. This involves understanding the core components and mechanisms that contribute to the model's performance. By focusing on these fundamental principles, we can identify areas where changes can be made to enhance speed.

Technical Approaches:

In addition to First Principles Thinking, there are several technical approaches that can be employed to make LLMs faster. Let's explore some of these approaches:

1. Reduce Model Size through Parameter Elimination:

One way to enhance the speed of LLMs is by reducing the size of the model itself. This can be achieved by eliminating unnecessary parameters. By carefully analyzing the model architecture and removing redundant parameters, we can streamline the computations and make the model more efficient.

2. Quantization for Precision Reduction:

Reducing the precision of numerical values used within the LLM can significantly speed up computations. By switching from higher precision formats like float32 to lower precision formats like float16 or even further down to int8, we can reduce memory requirements and improve processing speed without sacrificing performance.

3. Model Distillation for Compactness:

Model distillation involves training a smaller model to imitate the behavior of a larger model. By transferring the knowledge and insights learned by the larger model to a smaller, more compact model, we can achieve comparable performance with reduced computational requirements. This approach is especially useful when dealing with resource-constrained environments.

Connecting First Principles Thinking with Technical Approaches:

When we combine First Principles Thinking with these technical approaches, we can develop innovative solutions to make LLMs faster. For example, by applying first principles reasoning, we can question the necessity of certain parameters and then utilize model pruning techniques to eliminate them, reducing the model size. Similarly, by breaking down the problem into fundamental principles, we can identify the potential benefits of quantization and model distillation.


Making LLMs faster requires a combination of First Principles Thinking and technical approaches. By challenging assumptions, breaking down problems into fundamental principles, and applying techniques like parameter elimination, precision reduction, and model distillation, we can enhance the speed and efficiency of LLMs. As technology advances and researchers continue to explore new avenues, it is important to embrace innovative thinking and constantly seek ways to optimize LLMs for better performance.

Actionable Advice:

  • 1. Regularly question assumptions: Continuously challenge the assumptions underlying LLM design and functionality to identify potential areas for improvement.
  • 2. Experiment with model pruning and quantization: Explore techniques like parameter elimination and precision reduction to reduce the size and computational requirements of LLMs.
  • 3. Consider model distillation: Train smaller models to imitate the behavior of larger models, allowing for faster computations without sacrificing performance.

By incorporating both first principles reasoning and technical approaches, we can unlock the full potential of LLMs, making them faster and more effective in various applications. Remember, good ideas may initially seem crazy until they prove their worth, so don't be afraid to challenge existing norms and think outside the box.


  1. "How to make LLMs faster", (Glasp)
  2. "Elon Musks’ “3-Step” First Principles Thinking: How to Think and Solve Difficult Problems Like a…", (Glasp)

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