LLMs run out of GPU memory when their model weights, runtime allocations, and the key-value (KV) cache for active requests exceed available VRAM—or when memory allocation leaves too little usable capacity for another request. The KV cache grows as prompts and generated sequences grow. PagedAttention reduces waste by placing that cache in fixed-size blocks that need not sit next to one another in physical memory; it improves how VRAM is used, but does not eliminate the memory an LLM actually needs.
Why inference uses VRAM beyond the model weights
During autoregressive generation, a model produces tokens one at a time. To generate the next token, it can reuse attention keys and values calculated for earlier tokens rather than recomputing the entire prefix. Serving systems retain those tensors in the KV cache. That saves computation, but the cache consumes GPU memory and grows as sequences get longer and more requests run concurrently.
Memory demand therefore changes over time. Requests arrive and finish at different times, and their prompts and generated outputs have different lengths. A server must make room for changing cache sizes while also holding model weights and other runtime allocations. The foundational PagedAttention paper describes the cache as large and dynamic, and notes that inefficient management can waste memory through fragmentation and redundant duplication, limiting batch size. Kwon et al., “Efficient Memory Management for Large Language Model Serving with PagedAttention” (2023).
Capacity pressure versus fragmentation
These are related but distinct reasons an inference server can run out of VRAM:
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- Capacity pressure: model weights, runtime allocations, and live KV tensors genuinely occupy the available memory. A more efficient allocator cannot make those bytes disappear.
- Fragmentation or reservation waste: memory may be unused in total but unavailable in the right place or held aside because of how the system allocates space. Reserving a large contiguous region for a sequence that may grow, for example, can strand capacity that another request could otherwise use.
Fragmentation is not the only cause of out-of-memory errors. The number and length of active sequences, model size, and other allocations matter too. The vLLM project characterized fragmentation and over-reservation in the systems it examined as wasting 60%–80% of memory in a 2023 blog post; that figure describes those systems, not a universal rate for every inference engine. vLLM project blog (June 20, 2023).
How PagedAttention manages the KV cache
PagedAttention divides a sequence’s KV cache into fixed-token blocks. A block table maps the sequence’s logical token positions to physical blocks, which do not have to be adjacent in GPU memory. The serving system allocates blocks as generation proceeds instead of requiring one large contiguous region sized for a sequence’s possible maximum length. This resembles paging in virtual memory, but it is an analogy: a GPU inference engine’s implementation is not simply an operating system’s general-purpose virtual-memory subsystem.
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In its description of PagedAttention, the vLLM project says, “In PagedAttention, memory waste only happens in the last block of a sequence.” The final block can be partially filled, leaving some slack; the approach does not imply that every byte of VRAM is usable. The project blog reported under 4% waste for the final-block scheme it described. Treat that as a characterization of that approach, not a guarantee for all configurations or workloads. vLLM project blog (June 20, 2023).
The foundational paper also discusses sharing KV cache within and across requests, which can reduce redundant storage in supported cases. With less allocation waste, a server may fit a larger batch or serve more work at a given latency. The size of that benefit depends on the workload and system.
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PagedAttention and vAttention compared
vAttention is an alternative design, not another name for PagedAttention. Its authors describe a contiguous virtual-memory layout while managing physical allocation separately, with the aim of mitigating physical fragmentation without requiring the same non-contiguous block layout. The approaches make different trade-offs in allocation, kernel compatibility, and implementation.
| Approach | Cache layout and allocation | Implementation trade-off | Evidence and limits |
|---|---|---|---|
| PagedAttention | Splits each sequence’s KV cache into blocks mapped through a block table to physical blocks that need not be adjacent; allocates blocks as tokens are generated. | Works with PagedAttention-based serving and kernels; the block-based layout is central to the design. | The 2023 vLLM paper reports its own workload-specific results against FasterTransformer and Orca; the result is not a general guarantee. |
| vAttention | Uses a contiguous virtual layout while managing physical allocation separately, aiming to mitigate physical fragmentation. | Seeks to retain a contiguous layout, but makes different implementation and kernel-compatibility trade-offs. | The 2024 paper reports up to 1.23× throughput over the specific PagedAttention-based kernels it evaluated; this does not establish a universal ranking. Prabhu et al., “vAttention” (2024). |
These results are not an apples-to-apples verdict on every engine, GPU, model, or sequence mix. Choosing an approach requires considering the serving stack and its supported kernels as well as memory layout.
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What performance claims do—and do not—show
In their 2023 paper, Woosuk Kwon and coauthors report that vLLM improved throughput by 2–4× at the same level of latency compared with FasterTransformer and Orca in the workloads they evaluated. This is a result from that paper’s tested settings, not a promise for a particular user’s hardware, model, request pattern, or current software release. Kwon et al. (2023).
Throughput describes how much work a system serves over time; it is not the same as the amount of VRAM a model needs or a guarantee that a particular request will fit. Allocation efficiency can let a server use its existing memory more effectively, but live cache contents and weights still have to fit.
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Why block allocation does not solve every memory problem
Fixed-size blocks can leave unused space in a sequence’s partially filled final block. Newer work also points to a different granularity mismatch: token-level cache eviction may not align neatly with allocation in fixed-size blocks. A 2026 vToken preprint reports 27.2%–72.3% fewer retained KV blocks in its workload- and baseline-specific comparisons. That is an emerging research result, not settled production guidance. Gao et al., “vToken: Token-Level Virtualization for Reclaimable KV Caches” (2026).
Implementation details can also vary across model architectures. The living vLLM design document describes KV blocks and allocation that can differ by layer attention type, so the simplified fixed-block explanation is not a complete description of every current cache manager. Consult documentation for the specific release when investigating implementation or tuning behavior. vLLM hybrid KV cache manager design.
What to take away from an out-of-memory error
PagedAttention addresses a particular systems problem: wasted or stranded memory caused by inefficient KV-cache allocation. It can make more of a GPU’s memory available to active requests and can support larger batches, but it cannot provide a universal VRAM threshold or prevent every out-of-memory condition. If actual weights and live KV tensors exceed available capacity, a less fragmented allocator alone will not make the workload fit.
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