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Yapay ZekâÖnerilenAIML-SYS-018

KV Caching and Batched LLM Serving

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

Bu konunun henüz Türkçe çevirisi yok, bu yüzden İngilizce gösteriliyor.

Zorluk
3 / 5
Önem
4 / 5
Çalışma süresi
1–2 sa
Çalışma türü
Karma

Neden önemli

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

Bu konuya giden rota

Rota 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