KV Cache and Paged Attention: Accelerating LLM Inference through VRAM Optimization
Generated: 2026-07-05 · API: Gemini 2.5 Flash · Modes: Summary
KV Cache and Paged Attention: Accelerating LLM Inference through VRAM Optimization
Clip title: How KV Cache Speeds Up LLMs for Faster AI Models on GPUs Author / channel: IBM Technology URL: https://www.youtube.com/watch?v=o0gkdZBtwEg
Summary
This video addresses the critical challenge of efficiently serving Large Language Models (LLMs) at scale, focusing on optimizing GPU memory (VRAM) utilization during inference. As the number of concurrent users interacting with an LLM increases, the “Time-To-First-Token” (TTFT) latency climbs, and overall throughput tanks, leading to wasted computational resources and increased costs. The speaker explains that this bottleneck isn’t primarily due to the LLM model itself, but rather how its context, specifically the attention mechanism’s Key and Value (KV) matrices, are managed in memory during token generation. To combat this, the video introduces two core mechanisms from the open-source vLLM inference engine: KV Cache and Paged Attention.
LLM inference involves two distinct phases: the prefill phase and the decode phase. The prefill phase is highly compute-bound, where the model processes the entire input prompt through all its transformer layers to build a mathematical representation, which can cause an initial lag before any output is generated. Following this is the decode phase, which is memory-bound. In this phase, the model generates output tokens one by one. The crucial issue here is that for each new token generated, the model needs to re-evaluate its “attention” over all previously generated tokens and the original prompt. Without an efficient caching mechanism, this means repeatedly recomputing the Key and Value matrices for all preceding tokens at every single step, leading to significant computational overhead and memory thrashing. The KV Cache directly addresses this by storing these computed Key and Value matrices from previous steps, allowing each new token to only compute its own Query, Key, and Value, and then attend over the cached history, thus reducing redundant computation.
However, even with KV caching, traditional LLM serving systems face severe memory inefficiencies. A significant portion of GPU VRAM (e.g., 65% for a Llama 13B model) is consumed by the model’s weights alone. The remaining memory is then typically allocated for the KV cache. Naive serving pre-allocates a fixed, contiguous block of VRAM for each concurrent request, based on the maximum possible output sequence length. This leads to considerable internal fragmentation, where much of the reserved memory for shorter responses sits empty and unused. Furthermore, requests of varying lengths can cause external fragmentation, leaving insufficient contiguous memory for new large requests, even if total free memory is available. Additionally, redundant duplications occur when common system prompts are stored separately for every active user, further exacerbating memory waste. Research indicates that traditional systems can waste 60-80% of the VRAM allocated for KV cache due to these issues.
Paged Attention, a technique inspired by operating system virtual memory paging, revolutionizes GPU memory management for LLMs. Instead of allocating large, contiguous blocks, Paged Attention breaks the KV cache into small, fixed-size pages (e.g., 16 tokens each). These pages are non-contiguous and are allocated on demand, meaning memory is only consumed as needed. A lightweight block table maps the model’s logical page addresses to the physical page addresses in VRAM, effectively eliminating both internal and external fragmentation. This virtualized approach allows for much higher VRAM utilization and significantly increases the number of concurrent requests an LLM can handle on a single GPU.
The video concludes by providing three key tuning parameters for vLLM deployments to maximize GPU throughput and reduce latency. First, adjust --gpu-memory-utilization (default 0.9) to control the VRAM fraction dedicated to KV cache, pushing it higher (e.g., 0.95) for stable workloads or lower (e.g., 0.8) to prevent OOM errors. Second, enabling --enable-prefix-caching allows multiple requests with shared system prompts to point to the same physical memory for their KV cache, drastically reducing Time-To-First-Token by skipping redundant prefill computation for common prefixes. Third, --enable-chunked-prefill optimizes throughput for heavy workloads by interleaving decode requests with prefill chunks, preventing token stuttering that occurs when long prompts block decode generation. Lastly, a bonus feature, speculative decoding (via --speculative-model and --speculative-model-ngram), is introduced for latency-sensitive applications. This technique uses a smaller, faster “draft” model to propose token sequences, which are then verified in a single pass by the larger, more accurate model, effectively speeding up generation without compromising output quality.
Video Description & Links
Description
Learn more about LLM inference here → https://ibm.biz/~Ewjm0UejN
Why do LLMs crawl when traffic spikes? 🤔 Legare Kerrison explains how KV cache and paged attention reshape GPU memory across prefill and decode to speed up LLM inference. Learn how smarter context handling unlocks faster AI models, lower latency, and better GPU throughput. 🚀
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llm aiinfrastructure gpu machinelearning
Tags
IBM, IBM Cloud