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How KV Cache Works Why It Eats Memory - Medium KV cache is the secret to fast token generation — and a major reason GPUs run out of memory Here’s a deep dive into its mechanics, memory footprint, and lifecycle during inference
LLM Inference Series: 4. KV caching, a deeper look - Medium KV caching is a compromise: we trade memory against compute In this post, we will see how big the KV cache can grow, what challenges it creates and what are the most common strategies used
KV Caching Explained: Optimizing Transformer Inference Efficiency Key-Value caching is a technique that helps speed up this process by remembering important information from previous steps Instead of recomputing everything from scratch, the model reuses what it has already calculated, making text generation much faster and more efficient
KV Cache from scratch in nanoVLM - Hugging Face We have implemented KV Caching from scratch in our nanoVLM repository (a small codebase to train your own Vision Language Model with pure PyTorch) This gave us a 38% speedup in generation In this blog post we cover KV Caching and all our experiences while implementing it
What is the KV cache? | Matt Log - GitHub Pages That is why the key and value vectors of existing tokens are often cached for generating future tokens This approach leads to what is called the KV cache
Advancing KV Cache Optimization - by Rubab Atwal AI models have significantly improved their ability to handle long sequences, largely due to the attention mechanismin transformers This mechanism stores input tokens as key-value (KV) pairs and assigns a relevance score to each token when generating output (query)
What is KV Cache in LLMs and How Does It Help? TL;DR: KV cache is a memory optimization central to efficient LLM inference It enables faster, longer, and more cost-effective generation by caching previously computed attention keys and values—unlocking the practical deployment of models like GPT-4o, Llama 3, etc