What is Prompt Caching? Optimize LLM Latency with AI Transformers

IBM Technology

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Video Summary

Prompt caching is a technique designed to enhance the efficiency of large language models (LLMs) by storing and reusing precomputed "key-value" (KV) pairs generated during the initial processing of input prompts. This is distinct from output caching, which stores the final response. Prompt caching focuses on the input, specifically caching these KV pairs to avoid redundant computation for subsequent identical or similar prompts. This is particularly beneficial for lengthy prompts, such as large documents or detailed system instructions, where the initial computation of KV pairs across transformer layers involves millions of operations. By caching these pairs, the LLM can rapidly process new queries that share a common prefix with a previously cached prompt, leading to significant reductions in latency and cost. An interesting fact is that typically, at least 1024 tokens are needed to initiate caching for it to provide a tangible benefit, and caches are usually cleared within 5 to 10 minutes to maintain data freshness.

Short Highlights

Prompt caching stores precomputed key-value (KV) pairs generated by LLMs, not the final output.
Caching KV pairs avoids redundant computation across transformer layers for identical input prompts.
This technique significantly reduces latency and costs, especially for long prompts like 50-page documents.
Common cached items include system prompts, long documents, few-shot examples, and conversation history.
Effective prompt caching requires at least 1024 tokens to initiate and caches are typically cleared within 5 to 10 minutes.

Key Details

What is Prompt Caching? [00:01]

Prompt caching is a method to improve the speed and cost-effectiveness of large language models (LLMs).
It is not regular output-focused caching, which stores the final response to a query.
Instead, prompt caching focuses on caching only the input prompt's processed data.

"Prompt caching is about caching only the input prompt only caching this part here so that the LLM doesn't need to process it a second time."

How LLMs Process Prompts [00:12]

When a prompt is sent to an LLM, the model computes "key-value" (KV) pairs for every token in the input across each transformer layer.
These KV pairs represent the model's internal understanding of the prompt's context, word relationships, and important information.
This computation, known as the "prefill phase," occurs before the LLM generates its first output token and can be computationally expensive.

"And we can think of these KV pairs as the model's internal understanding of your prompt."

The Mechanics of Prompt Caching [03:13]

Prompt caching involves storing these precomputed KV pairs.
For simple prompts with few tokens, caching offers minimal savings.
However, for complex prompts containing large documents (e.g., 50 pages) or extensive instructions, the KV computation is substantial.
By caching these KV pairs for a lengthy document, subsequent queries referencing the same document can reuse the cached data, processing only the new, distinct question at the end.

"So with prompt caching, that processing work is getting saved."

What Can Be Cached? [04:56]

Documents of significant length, such as product manuals, research papers, or legal contracts, can be cached.
System prompts, which define an LLM's personality, rules, and behavior (e.g., "You're a helpful customer service agent"), are a common and highly effective item to cache.
Few-shot examples, used to guide the model's output format, and tool/function definitions are also candidates for caching.
Conversation history can also be stored and reused.

"And we can also put into the cache few examples. So when you want the model to format responses a certain way, you show it examples."

Prefix Matching for Caching [06:12]

The LLM determines what to cache using a technique called "prefix matching."
The system compares incoming prompts token by token from the beginning with cached data.
Caching continues until the first differing token is encountered; thereafter, normal processing resumes.
This makes prompt structure crucial for effective automatic caching.

"So, the cache system matches your prompt from the very beginning token by token and when it encounters the first token that differs from what's cached, then caching stops and normal processing takes over."

Optimal Prompt Structure for Caching [06:37]

To maximize caching benefits, static content should be placed at the beginning of the prompt.
A recommended structure includes system instructions, followed by documents, then few-shot examples, and finally the user's question.
Placing the question first and then static content would lead to immediate cache failure when the question changes, requiring full reprocessing.

"Well, this structure puts all of the static content first. So when the next request comes in with just a different question... The cache matches through all of this static content here."

Caching Parameters and Lifespan [08:00]

Typically, at least 1024 tokens are needed to initiate caching for it to provide a tangible benefit, as the overhead of cache management below this threshold exceeds savings.
Caches are usually cleared after 5 to 10 minutes to ensure data freshness, though some may persist for up to 24 hours.
Some providers offer automatic prompt caching, while others require explicit API calls to designate parts of the prompt for caching.