KV Cache tutorial

Overview

What is KV Cache?

KV Cache is a inference optimization technique. Reuses earlier key and value vectors instead of recomputing the whole prompt every step. Its central idea is summarized by K₁:ₜ ← Concat(K₁:ₜ₋₁,Kₜ); V₁:ₜ ← Concat(V₁:ₜ₋₁,Vₜ).

This guide connects theory to practice. You will trace the input, intermediate process, and output; decode the notation; run a dependency-light implementation; then repeat the workflow with Hugging Face Transformers and evaluate the result.

Preparation

Prerequisites and tools

You do not need an advanced software stack. Start with a recent Python environment and the fundamentals below, then install only the packages used by the practical example.

• Comfort with Python, tensor shapes, matrix multiplication, and probability distributions.
• A working understanding of token sequences, embeddings, batches, and attention masks.
• Enough memory to run the small tensor example; pretrained-model tutorials may also download model weights.

Process

How KV Cache works

1. 1

   Input

   Cached key-value tensors and the newest token representation.

2. 2

   Prepare and configure

   Check shapes, value ranges, ordering assumptions, missing values, and the parameters that control the algorithm’s behavior.

3. 3

   Algorithm process

   Project only the new token, append its keys and values, then attend over the cache.

4. 4

   Output

   The next-token context with much less repeated computation.

5. 5

   Validate

   Compare cached and uncached next-token logits for numerical agreement.

Core concept

Formula and intuition

The notation captures the main operation or complexity statement behind KV Cache. Read it together with the symbol key above and the step-by-step process in this guide.

Applications

When to use KV Cache

Accelerating autoregressive LLM chat and long-sequence generation.

Implementation

KV Cache from scratch in Python

This dependency-light example emphasizes the algorithm’s mechanics so each important step remains visible.

import numpy as np

key_cache = np.empty((0, head_dim))
value_cache = np.empty((0, head_dim))

for token in generated_tokens:
    query = token @ W_query
    new_key = token @ W_key
    new_value = token @ W_value
    key_cache = np.vstack([key_cache, new_key])
    value_cache = np.vstack([value_cache, new_value])

    scores = query @ key_cache.T / np.sqrt(head_dim)
    weights = np.exp(scores - scores.max())
    weights /= weights.sum()
    context = weights @ value_cache

Practical tutorial

Build KV Cache with Hugging Face Transformers

Transformers exposes past_key_values so autoregressive decoding can reuse attention keys and values from earlier tokens.

1. Prepare the data.Cached key-value tensors and the newest token representation. Validate its shape, type, range, and ordering before training or execution.
2. Configure the algorithm.Begin with explicit, conservative parameters and a fixed random seed whenever the library supports one.
3. Fit or execute.Project only the new token, append its keys and values, then attend over the cache.
4. Inspect the result.The next-token context with much less repeated computation. Then apply the evaluation checks in the next section.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "distilbert/distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).eval()
inputs = tokenizer("Algorithms become clear when", return_tensors="pt")

with torch.no_grad():
    first = model(**inputs, use_cache=True)
    next_id = first.logits[:, -1].argmax(dim=-1, keepdim=True)
    second = model(
        input_ids=next_id,
        past_key_values=first.past_key_values,
        use_cache=True,
    )
print("cached layers:", len(first.past_key_values))
print("next-step logits:", second.logits.shape)

API details and version-specific options: official Hugging Face Transformers reference →

Evaluation

How to evaluate the result

A successful run is not enough. Evaluate the output against the intended use, compare it with a simple baseline, and preserve a genuinely unseen test case whenever the task involves learned parameters.

• Compare cached and uncached next-token logits for numerical agreement.
• Measure per-token latency and cache memory as sequence length grows.

Common mistakes

Pitfalls and how to avoid them

These failure modes are common in tutorials and production systems. Treat them as review questions, not just after-the-fact debugging advice.

• The cache is for inference; reusing detached history is generally inappropriate for full-sequence training.
• Attention masks and position IDs must account for cached length.
• Cache memory grows linearly with generated sequence length and layer count.

Project checklist

Before using KV Cache in a project

• ✓Write down every tensor shape and mask convention before composing layers.
• ✓Test a tiny deterministic case against a simple reference implementation.
• ✓Separate training behavior from autoregressive inference and disable dropout for evaluation.
• ✓Measure quality, latency, peak memory, and sequence-length scaling together.
• ✓Pin model, tokenizer, framework, and generation-configuration versions.

Further reading

Official documentation and next steps

This guide is an educational introduction, not a substitute for domain validation. For consequential applications, review the source documentation, test against representative data, and involve a subject-matter expert.

Continue learning

Related llm algorithms guides

• Byte Pair EncodingBuilds a compact vocabulary by repeatedly merging frequent neighboring symbols.
• Token EmbeddingsMaps discrete token IDs to dense vectors whose geometry can encode meaning.
• Positional EncodingAdds a position-dependent signal so identical tokens at different places remain distinguishable.