Reducing Latency in LLM: How Prompt Caching Can Optimize Performance

In the evolving landscape of conversational AI, latency reduction is a critical factor for ensuring seamless and responsive user experiences. One of the most effective strategies for achieving this is prompt caching—particularly in scenarios where a significant portion of the prompt remains static, and only a smaller portion is dynamic. This blog explores how prompt caching works, delving into the technical details, such as caching token embeddings and intermediate states during inference. We’ll also walk through an example involving a support chatbot for a tech company, analyzing the latency impact and how different caching techniques contribute to performance improvements.

Understanding Prompt Caching

Prompt caching involves storing frequently used portions of prompts or responses so they can be quickly retrieved and reused in future interactions. This reduces the need to reprocess the same data multiple times, thereby decreasing latency and improving the overall efficiency of the AI system.

In conversational AI, prompts are often divided into two parts:

• Static Part: This is the unchanging portion of the prompt, such as general instructions, background information, or contextual details that apply across multiple interactions.
• Dynamic Part: This is the variable portion that changes based on the specific input from the user, such as a unique query or user-specific information.

Technical Components of Prompt Caching

To understand how caching is implemented, we need to break down the technical steps involved in generating a response in a conversational AI system. Key components that can be cached include:

1. Token Embeddings
2. Intermediate States During Inference
3. Pre-processed Static Context

1. Token Embeddings

Token embeddings are vector representations of the words or subwords in a prompt. These embeddings are crucial because they transform textual data into numerical data that the AI model can process.

Caching Token Embeddings:

• Static Part: For the static portion of a prompt (e.g., standard instructions or context), the token embeddings remain the same across different queries. By caching these embeddings, the system can skip the computationally expensive step of generating these embeddings every time the prompt is used.
• Dynamic Part: Only the token embeddings for the dynamic portion of the prompt (e.g., the specific user query) need to be generated on-the-fly.

Example:
In our support chatbot, if the static part of the prompt is "Refer to the latest version of the technical documentation on network configuration," the token embeddings for this sentence can be cached. When a user asks, "How do I configure VLANs on this device?" only the embeddings for "How do I configure VLANs on this device?" need to be generated.

2. Intermediate States During Inference

Inference in deep learning models (like transformers) involves several layers of processing. Each layer computes intermediate states, which are progressively refined representations of the input data.

Caching Intermediate States:

• Static Part: The intermediate states for the static part of the prompt can be cached after the first inference pass. When the static prompt is reused, the system can skip directly to processing the dynamic part.
• Dynamic Part: Only the intermediate states related to the dynamic portion of the prompt need to be computed afresh, reducing the overall computational load.

Example:
In the support chatbot, after processing the static prompt, intermediate states like attention matrices in a transformer model can be cached. For subsequent queries, these states are reused, allowing the model to focus on processing only the dynamic content, such as the specific details of a user's query.

3. Pre-processed Static Context

In many AI models, particularly large language models, the initial context or system prompt that frames the conversation is critical. This context might include instructions, guidelines, or other pre-configured information.

Caching Pre-processed Context:

• Static Part: This context can be pre-processed and cached, allowing it to be quickly loaded into the model for each new conversation.
• Dynamic Part: The specific user input is then appended to this cached context and processed by the model.

Example:
For the support chatbot, the technical documentation summary can be pre-processed, stored, and quickly retrieved for each user query. The system only appends and processes the unique question asked by the user.

Example: A Support Chatbot for a Tech Company

Consider a support chatbot for a tech company that assists users with configuring network devices. Users frequently ask about specific configuration tasks, such as setting up VLANs or DHCP.

Scenario: Technical Documentation Retrieval

• Static Part: "Refer to the latest version of the technical documentation on network configuration."
• Dynamic Part: The user’s specific query, such as "How do I configure VLANs on this device?"

The static part includes a reference to the technical documentation and relevant context, which doesn’t change across different user interactions. By caching this static part, including token embeddings, intermediate states, and pre-processed context, the chatbot can quickly generate responses to user queries by focusing only on the dynamic part.

Caching in Action: Detailed Process Flow

Here’s a step-by-step breakdown of how caching improves the efficiency of this chatbot:

1. Initial Request:
   • The user asks a question, triggering the chatbot to load the static part of the prompt.
   • Token embeddings for the static part are generated and cached.
   • The static context is processed through the model, generating and caching intermediate states.
2. Caching Process:
   • The static prompt's token embeddings, intermediate states, and pre-processed context are stored in a cache.
   • Future requests that use the same static prompt can skip these steps, retrieving the cached data instead.
3. Subsequent Requests:
   • When a different user asks a related question, the system retrieves the cached static prompt data.
   • The dynamic part of the new query is processed, and the cached data is reused to quickly generate the final response.

Latency Impact of Caching Static Prompts

The latency reduction from caching the static part of the prompt can be quantified as follows:

• ( T_s ): Time to process the static part of the prompt (e.g., generating token embeddings, intermediate states).
• ( T_d ): Time to process the dynamic part of the prompt (e.g., user-specific query processing).

Without caching, the total time to generate a response is:

[
T_{\text{total}} = T_s + T_d
]

With caching, the time to generate a response is reduced to:

[
T_{\text{cache}} = T_d
]

Using the latency reduction formula:

[
\text{Latency Reduction (LR)} = \left(\frac{T_s}{T_s + T_d}\right) \times 100%
]

Example Calculation:
Assume that:

• ( T_s = 500 ) milliseconds (ms) for generating and processing the static part.
• ( T_d = 200 ) milliseconds (ms) for processing the dynamic part.

Without caching:

[
T_{\text{total}} = 500 \text{ ms} + 200 \text{ ms} = 700 \text{ ms}
]

With caching:

[
T_{\text{cache}} = 200 \text{ ms}
]

Latency reduction:

[
LR = \left(\frac{500}{700}\right) \times 100% \approx 71.4%
]

Thus, caching the static part of the prompt can reduce latency by over 70%, greatly improving response time.

Advantages of Caching

1. Significant Latency Reduction: Caching can reduce the time required to generate a response by over 70% in scenarios with large static prompts, leading to a smoother user experience.
2. Efficient Resource Utilization: By reducing the need to repeatedly process static data, the system can allocate more resources to dynamic processing and handling more simultaneous interactions.
3. Improved Scalability: Systems can handle higher loads with less computational overhead, making them more scalable and robust in high-traffic environments.
4. Consistent and Reliable Responses: Caching ensures that similar queries receive consistent responses, enhancing the reliability and trustworthiness of the chatbot.

Challenges in Caching

While prompt caching offers substantial benefits, there are challenges that must be managed:

1. Cache Invalidation: Ensuring that outdated or irrelevant cached data is refreshed or invalidated promptly to maintain accuracy.
2. Context Sensitivity: The system must ensure that cached prompts are contextually appropriate for each new query.
3. Security and Privacy: Sensitive information should not be cached to avoid potential security risks.

Conclusion

Prompt caching, particularly for the static portions of a prompt, is a powerful tool for reducing latency in conversational AI systems. By caching token embeddings, intermediate states, and pre-processed contexts, systems can dramatically cut down on the time required to generate responses. This makes them more efficient, scalable, and capable of delivering faster, more reliable interactions.

In the case of our support chatbot example, caching the static parts of the prompt allows the system to focus on processing the dynamic parts of user queries, resulting in a significant reduction in response time. As conversational AI continues to evolve, prompt caching will remain a crucial technique for optimizing performance and enhancing user experiences.