ManySpace
Türkçe Sign in Sign up

AI & MLRecommendedAIML-SYS-018

KV Caching and Batched LLM Serving

Reusing attention keys and values across decoding steps, and batching requests continuously to use hardware well.

Difficulty
3 / 5
Importance
4 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can compute KV-cache memory for a model and context length and explain how continuous batching raises throughput.

The route to this topic

Route AIML-SYS-018
What Is a Model? Tasks: Regression, Classification, and Beyond Features and Targets Classification Framing Linear Regression I: Prediction and Residuals Embeddings Tokens and Tokenization Scores and Logits Loss and Fitting: Squared Error Similarity in Vector Space Multiclass Scores and Softmax The Artificial Neuron Autoregression Objective Functions and Empirical Risk Query, Key, Value Activation Functions Next-Token Prediction Training vs Serving Similarity Scores The Multilayer Perceptron Weighted Aggregation Latency and Throughput Forward Propagation Self-Attention The Attention Block Context and Conditioning Memory and Model Size The Causal Transformer Context Windows Caching and Reuse KV Caching and Batched LLM Serving