LLMs run out of GPU memory when model weights, runtime allocations and the live key-value (KV) cache together exceed available VRAM—or when memory is reserved or fragmented so that capacity cannot be used efficiently. PagedAttention reduces cache-allocation waste by storing a sequence’s KV data in blocks that need not sit next to each other. It can make room for more concurrent requests, but it does not eliminate the memory required by the model or its active tokens.
Why inference needs a growing KV cache
Autoregressive language models generate output one token at a time. To produce the next token, the model uses attention over earlier tokens. It retains the attention keys and values calculated for those tokens—the KV cache—so it can reuse them rather than recompute the entire prefix at every step.
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That reuse saves computation, but costs memory. As a prompt is processed and the response grows, the cache grows too. Serving several requests at once means keeping several live caches, each with its own prompt and generation length. The PagedAttention paper describes this state as both large and dynamically changing. Kwon et al., “Efficient Memory Management for Large Language Model Serving with PagedAttention” (2023)
Why an LLM can run out of VRAM
There are two different problems that are easy to conflate: actual capacity pressure and allocation waste. If the model’s weights, active KV tensors and other runtime allocations genuinely use all available VRAM, an allocation can fail even with an efficient cache manager. Fragmentation or over-reservation is different: some memory may be stranded or held aside, so the serving system cannot use it effectively for another request.
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Capacity pressure from live data
Weights occupy memory while the model is loaded. The runtime also needs memory for execution, and every active sequence adds KV-cache data that increases with its token count. Longer prompts, longer generations and more simultaneous requests all increase demand. Changing how memory is laid out cannot make these live tensors disappear.
Waste from variable-length requests
Requests do not arrive, finish or grow in lockstep. One sequence may finish while another is still generating; their prompts and outputs may also differ in length. A system that reserves large contiguous areas or holds space for possible future growth can leave capacity unusable between allocations. The PagedAttention paper identifies fragmentation and redundant duplication as sources of waste that can limit batch size. Kwon et al. (2023)
In a 2023 project explanation, vLLM characterized fragmentation and over-reservation as wasting 60%–80% of memory in the systems it examined. That figure describes the project’s assessment of those systems, not a universal waste rate for every LLM server. vLLM project blog, 20 June 2023
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How PagedAttention allocates the cache
PagedAttention divides a sequence’s KV cache into fixed-token blocks. A block table maps the sequence’s logical blocks—their place in token order—to physical memory blocks. Those physical blocks do not have to be adjacent in GPU memory. The serving system allocates blocks as generation proceeds, instead of requiring one large, contiguous region for the full sequence.
The idea is analogous to paging in virtual memory: logical positions are mapped to physical storage. It is an analogy, not a claim that GPU cache management is identical to an operating system’s general-purpose virtual-memory subsystem. The vLLM blog describes the method as leaving unused capacity only in a sequence’s final, partially filled block. In its described scheme, the blog says waste in that final-block allocation is under 4%; this is not a guarantee for all configurations or cache managers. vLLM project blog, 20 June 2023
Block-based placement also supports sharing KV data in some cases, including within and across requests, as described by the PagedAttention paper. Avoiding redundant copies and reducing allocation waste can leave more usable memory for active sequences. Kwon et al. (2023)
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What PagedAttention improves—and what it does not
- It reduces a source of allocation waste. Cache blocks can be placed independently rather than requiring each growing sequence to occupy one contiguous region.
- It can improve serving capacity. More usable cache memory may allow a server to run a larger batch, which can improve throughput depending on the workload and system.
- It does not reduce the underlying need for memory. Model weights, runtime allocations and live KV tensors still consume VRAM; a sufficiently large workload can still exceed capacity.
- It does not eliminate all slack. A partially filled final block can contain unused space, and fixed-block allocation may not suit every cache-management strategy.
The 2023 PagedAttention paper reports that vLLM improved throughput by 2–4× at the same latency compared with FasterTransformer and Orca in its evaluated workloads. That is a result from the authors’ tests, not a speedup guarantee for a different model, GPU, sequence mix or software release. Kwon et al. (2023)
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PagedAttention uses non-contiguous physical cache blocks and a block table. vAttention takes another route: it keeps the cache contiguous in virtual memory while managing physical allocation separately, aiming to mitigate physical fragmentation without the same explicit block-based layout. The vAttention authors report up to 1.23× throughput over the specific PagedAttention-based kernels they evaluated. This limited result does not establish a universal winner; performance and implementation trade-offs depend on the system and workload. Prabhu et al., “vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention” (2024)
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why block granularity still matters
Fixed-size blocks make allocation manageable, but they also define the granularity at which cache space is placed and reclaimed. A 2026 preprint on vToken describes a mismatch when token-level cache eviction is paired with fixed-block allocation. Its authors report 27.2%–72.3% fewer retained KV blocks in their comparisons; those results are specific to the preprint’s workloads and baselines and should be treated as emerging research rather than established production guidance. Gao et al., “vToken: Token-Level Virtualization for Reclaimable KV Caches” (2026)
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Implementations also evolve beyond the simplified picture of identical blocks for every sequence. The current vLLM design documentation describes KV blocks and allocation that can vary by layer attention type. For release-specific behavior or tuning, consult documentation for the exact vLLM version in use rather than assuming a mutable main-branch design applies unchanged. vLLM hybrid KV cache manager design
How to interpret a VRAM limit in practice
An out-of-memory error alone does not show whether the main constraint was live cache demand, model weights, other runtime allocations or allocator waste. The distinction matters: an allocation strategy can recover usable capacity stranded by fragmentation, but it cannot solve a workload whose actual memory requirements exceed the GPU’s capacity. PagedAttention is best understood as a way to use cache memory more efficiently while serving variable-length requests, not as a way to remove the GPU-memory cost of inference.
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