KV Cache: From Inference Optimization to Runtime Infrastructure

Over the past two years, the hottest topics in AI have almost all revolved around knowledge.

Prompt.

RAG.

Context.

Memory.

Long context.

Knowledge bases.

Everyone has been studying the same question:

How to make AI know more.

But after reading some recent papers on Agent and KV Cache, I’ve started to realize:

A different kind of problem is emerging in the AI field.

It no longer cares about what AI knows.

Instead, it starts to care about:

Whether AI is repeatedly thinking about things it has already thought about.

And KV Cache might be the first systematic solution in the LLM world to address this problem.

1. What is KV Cache

Many articles start by explaining Transformer’s Key, Value, and Attention.

But from an engineering perspective, KV Cache can be summed up in one sentence:

Cache the results of computations that have already been performed.

Note.

What is cached here is not knowledge.

Not documents.

Not memory.

Instead, it’s:

The Attention results that have already been computed

Many people confuse Context with KV Cache.

In reality, they solve completely different problems.

Context solves:

What AI knows.

KV Cache solves:

What AI has already computed.

These are two different dimensions.

2. Why KV Cache Exists

Suppose the model has already seen:

Today Weather Is very good

And is about to generate the next Token.

The Transformer will perform a large amount of computation.

These computations produce corresponding Keys and Values.

Here’s the problem.

The next second, the model continues generating content.

The historical part before that has not changed.

So:

Why recalculate everything?

Thus KV Cache was born.

It saves the results of completed computations.

And directly reuses them later.

From a software engineering perspective.

KV Cache is more like:

Computed Cache

A cache of computation results.

Rather than:

Data Cache

A cache of data.

3. In the Chat Era, It Was Just an Optimization

In the past, discussions about KV Cache.

Mostly revolved around inference performance.

Because the operation mode of chat models is simple:

answer = model(prompt)

Ask once.

Answer once.

Done.

In this mode.

The value of KV Cache is mainly reflected in:

• Faster inference speed
• Higher throughput
• Lower service cost

Without it.

The system could still run.

Just slower.

So in the past, KV Cache was more like a performance optimization technique.

4. The Agent Era Begins to Show Changes

But after Agents emerged.

Things started to get interesting.

In the past:

answer = model(prompt)

End.

Now:

while not done: think() act() observe() repair()

Agents start running continuously.

Continuously thinking.

Continuously executing.

Continuously correcting.

At this point, a new problem arises:

Does AI have to re-think all past content in every loop?

If the answer is “yes.”

Then the cost will quickly spiral out of control.

Because an Agent’s running time could be:

5 minutes

30 minutes

2 hours

Or even days

Each re-inference.

Consumes new computing resources.

So the Runtime must solve a classic problem:

What has already been computed?

And KV Cache’s answer is:

Don’t re-think what has already been thought.

From this moment on.

The role of KV Cache begins to change.

It no longer just makes the system faster.

It starts to help the system run continuously.

5. Why This Matters

In recent years.

The AI field has been focused on solving knowledge problems.

Everyone studies:

Prompt

Context

RAG

Memory

These all answer:

What AI should know.

But with the emergence of Agents.

Another question is emerging:

How should AI reuse computations that have already been performed?

This is a completely different problem.

Knowledge and computation are not the same thing.

For example:

Product documentation is knowledge.

Database structure is knowledge.

Business rules are knowledge.

But:

Completed reasoning

Plans that have already been executed

Results that have already been verified

These belong to the computation process.

In the past, AI was more like a query system.

In the future, Agents will be more like continuously running systems.

And all continuously running systems encounter the same problem:

Repetitive work is too expensive.

6. A Signal from Papers

Some recent research has started to become interesting.

Some people study:

Persistent KV Cache

Making caches persist across sessions.

Some study:

Workflow-aware KV Cache

Managing caches based on Agent execution paths.

Some study:

KV Cache Memory

Attempting to use KV Cache as working memory.

These solutions may not be the final answer.

But they reveal a common signal:

Researchers are starting to view KV Cache as a Runtime resource.

Not as an internal detail of the Transformer.

This means the focus of discussion is shifting.

In the past, the discussion was about:

How to compute

Now the discussion starts to focus on:

How to reuse computation

7. The Future

In the future, Agent Runtime may become increasingly dependent on various forms of caches.

Today we see:

KV Cache

In the future, we may also see:

Tool Cache

Planning Cache

Inference Cache

Search Cache

Because all long-running systems eventually face the same problem:

How to avoid repetitive work.

CPUs do this.

Databases do this.

Browsers do this.

Agent Runtime will likely do the same.

KV Cache is just the first piece of the puzzle.

8. Conclusion

Prompt solves the input problem.

Context solves the knowledge problem.

And KV Cache solves something else:

How to prevent AI from repeatedly thinking about the same thing.

In the Chat era.

KV Cache was more like an inference optimization technique.

In the Agent era.

It begins to take on new responsibilities.

Helping the system reuse completed computations.

Helping Agents run continuously at lower cost.

Perhaps in the future, what really matters is not KV Cache itself.

But the trend it represents:

AI not only needs to know something.

But also needs to remember what it has already thought.

And this is precisely an evolution that all mature Runtimes have gone through.