@akshay_pachaar: You're in an ML Engineer interview at Anthropic. The interviewer asks: "Our model generates 100 tokens in 42 seconds. H…

X AI KOLs Timeline News
kv-caching llm-inference attention-mechanism model-optimization production-scale open-source redundant-computation

Summary

Explains how KV caching speeds up LLM inference by eliminating redundant recomputation of attention keys and values, trading off speed for memory, and introduces production-scale cache management challenges.

You're in an ML Engineer interview at Anthropic. The interviewer asks: "Our model generates 100 tokens in 42 seconds. How do you make it 5x faster?" You: "I'll optimize the model architecture and use a better GPU." Interview over. Here's what you missed: The real bottleneck isn't compute. It's redundant computation. Without KV caching, your model recalculates the same attention keys and values for every single token generation. That's why a 9-second inference becomes 42 seconds. You're wasting 80% of your time on repeated calculations. The fundamental issue: (refer image below as you read ahead) LLM token generation is autoregressive: - Generate token 1 from the prompt - Generate token 2 from prompt + token 1 - Generate token 3 from prompt + token 1 + token 2 At each step, you're reprocessing ALL previous tokens through attention. Token 50? You've computed attention for token 1 fifty times. The reality of attention mechanism: For each token, the transformer computes: - Query (Q) from current token - Key (K) from all previous tokens - Value (V) from all previous tokens Then: Attention(Q, K, V) = softmax(QK^T)V Problem: K and V for previous tokens never change. You're recalculating identical matrices every single step. How KV caching solves this: Instead of recomputing K and V matrices: - Cache them after first computation - Reuse cached values for subsequent tokens - Only compute K and V for the new token Without KV caching (token 50): - Compute Q, K, V for all 50 tokens → O(n²) With KV caching (token 50): - Load cached K, V for tokens 1-49 - Compute Q, K, V only for token 50 → O(n) You've eliminated quadratic redundancy. So what's the tradeoff: While KV caching makes the inference faster, it also takes up a lot of memory, so there is always a tradeoff between speed and memory. Why your first token always takes longer: KV caching speeds up inference by computing the prompt's KV cache before generating tokens. This is exactly why ChatGPT takes longer to generate the first token than the rest. First token: Computing KV cache for entire prompt Remaining tokens: Just loading cached KVs + computing new token ---- Everything above is how KV caching works inside a single request. Running it in production is a different problem. Caches break on document reordering, and a single GPU throws away roughly 15 TB of reusable cache per day. I wrote an article that picks up exactly where this post ends: how a new open-source architecture manages KV cache at production scale, with 14x faster time-to-first-token to show for it. The article is quoted below.
Original Article
View Cached Full Text

Cached at: 07/07/26, 07:36 PM

You’re in an ML Engineer interview at Anthropic.

The interviewer asks:

“Our model generates 100 tokens in 42 seconds. How do you make it 5x faster?”

You: “I’ll optimize the model architecture and use a better GPU.”

Interview over.

Here’s what you missed:

The real bottleneck isn’t compute. It’s redundant computation.

Without KV caching, your model recalculates the same attention keys and values for every single token generation.

That’s why a 9-second inference becomes 42 seconds. You’re wasting 80% of your time on repeated calculations.

The fundamental issue:

(refer image below as you read ahead)

LLM token generation is autoregressive:

  • Generate token 1 from the prompt
  • Generate token 2 from prompt + token 1
  • Generate token 3 from prompt + token 1 + token 2

At each step, you’re reprocessing ALL previous tokens through attention.

Token 50? You’ve computed attention for token 1 fifty times.

The reality of attention mechanism:

For each token, the transformer computes:

  • Query (Q) from current token
  • Key (K) from all previous tokens
  • Value (V) from all previous tokens

Then: Attention(Q, K, V) = softmax(QK^T)V

Problem: K and V for previous tokens never change. You’re recalculating identical matrices every single step.

How KV caching solves this:

Instead of recomputing K and V matrices:

  • Cache them after first computation
  • Reuse cached values for subsequent tokens
  • Only compute K and V for the new token

Without KV caching (token 50):

  • Compute Q, K, V for all 50 tokens → O(n²)

With KV caching (token 50):

  • Load cached K, V for tokens 1-49
  • Compute Q, K, V only for token 50 → O(n)

You’ve eliminated quadratic redundancy.

So what’s the tradeoff:

While KV caching makes the inference faster, it also takes up a lot of memory, so there is always a tradeoff between speed and memory.

Why your first token always takes longer:

KV caching speeds up inference by computing the prompt’s KV cache before generating tokens.

This is exactly why ChatGPT takes longer to generate the first token than the rest.

First token: Computing KV cache for entire prompt

Remaining tokens: Just loading cached KVs + computing new token

Everything above is how KV caching works inside a single request.

Running it in production is a different problem. Caches break on document reordering, and a single GPU throws away roughly 15 TB of reusable cache per day.

I wrote an article that picks up exactly where this post ends: how a new open-source architecture manages KV cache at production scale, with 14x faster time-to-first-token to show for it.

The article is quoted below.

Similar Articles