Stop Wasting GPU Memory: A Deep Dive Into vLLM’s PagedAttention

VLLM's PagedAttention technique reduces GPU memory waste in LLM serving from 60-80% to less than 4% by partitioning the KV cache into non-contiguous blocks, according to a technical analysis. The method enables memory sharing during parallel sampling and beam search, cutting memory usage by 30% and 37-55% respectively, addressing the high cost of serving LLMs, which can be up to 10 times more expensive than traditional search queries.

Every time you type a prompt into an LLM, a silent battle for GPU memory takes place under the hood. If you’ve ever hosted an LLM or looked at serving costs, you know that the bottleneck isn’t just raw computing power FLOPS — it is GPU memory bandwidth and capacity . In fact, serving a single request can be up to 10 times more expensive than a traditional search query. The chief culprit? The Key-Value KV Cache . Today, we are diving deep into how vLLM revolutionized LLM serving by taking a page literally out of classical operating system design: PagedAttention . We will use three visual scenarios to show exactly how it works. The Problem: Contiguous Memory is a Trap To generate text autoregressively one word at a time , an LLM must look at all previous words to calculate where to focus its attention next. To avoid recalculating the Key K and Value V projection vectors for every past token over and over, these vectors are saved in GPU memory. This is the KV Cache . In traditional serving frameworks like FasterTransformer or Orca , deep learning engines require tensors to be stored in contiguous memory . This means when a request starts, the system must pre-allocate a continuous block of GPU DRAM equal to the maximum possible sequence length e.g., 2048 tokens This causes three catastrophic types of memory waste: As a result, traditional serving systems waste up to 60% to 80% of their available KV cache memory Traditional Contiguous Allocation e.g., Max Length = 16 Token 1 Token 2 Token 3 Reserved Reserved Reserved ... Wasted DRAM The breakthrough of PagedAttention is treating GPU DRAM like traditional computer memory. Instead of forcing a sequence’s KV cache to reside in one massive, continuous block of memory, PagedAttention partitions the cache into KV Blocks . Each block contains keys and values for a fixed, small number of tokens denoted as block size B, with 16 being the optimal default . These physical blocks are scattered non-contiguously throughout the GPU memory. vLLM coordinates this using a Block Table — a translation registry that maps contiguous Logical Blocks the model’s view to scattered Physical Blocks the hardware’s view . Let’s trace how this executes in the three core generation scenarios: Scenario 1: Single Sequence Generation The Autoregressive Loop php Logical Block L0 --- Block Table L0 - P7 --- Physical Block P7 in DRAM Logical Block L1 --- Block Table L1 - P3 --- Physical Block P3 in DRAM When you send a prompt, vLLM executes the Prefill Phase 7. If your prompt is “Four score and seven years” 5 tokens and block size B=4: By only allocating what is needed on-demand, memory waste is capped strictly to the unfilled slots of the very last block . This slashes memory waste from ~70% down to less than 4% Scenario 2: Parallel Sampling & Copy-on-Write CoW When generating multiple diverse outputs from a single prompt e.g., generating three code completions for a programming assistant , the prompt is exactly the same. Traditionally, you would have to duplicate the prompt’s KV cache across multiple contiguous memory blocks. PagedAttention handles this elegantly: This allows LLM servers to share up to 30% of their KV memory effortlessly during parallel generation Scenario 3: Beam Search Pruning the Tree Beam Search is a highly dynamic decoding method where multiple candidate sentences ”beams” compete. At each step, only the most probable paths are kept, while low-probability paths are killed off. In standard systems, this requires frequent, heavy memory copies to synchronize the KV cache of newly selected beams. With PagedAttention, Beam Search functions like a dynamic process tree: This block-sharing optimization translates to a massive 37% to 55% memory saving during beam search workloads. By squeezing out every ounce of wasted memory fragmentation, vLLM can increase the server’s effective batch size the number of requests processed simultaneously by up to 4x on the exact same hardware, leading to a dramatic 2–4× improvement in overall serving throughput. To help you see this in action, we’ve built a fully interactive dashboard simulator where you can play with Single Generation, Parallel Sampling, and Beam Search. Try stepping through the simulation to watch block tables update and Copy-on-Write execute in real-time Refer : https://github.com/rahulprasanth487/PagedAttention https://github.com/rahulprasanth487/PagedAttention Stop Wasting GPU Memory: A Deep Dive Into vLLM’s PagedAttention https://pub.towardsai.net/stop-wasting-gpu-memory-a-deep-dive-into-vllms-pagedattention-a7155f1aa0ec was originally published in Towards AI https://pub.towardsai.net on Medium, where people are continuing the conversation by highlighting and responding to this story.