ELDR reduces median TPOT by 5.9-13.9% in PD-disaggregated MoE serving via expert signatures from prefill, K-means partitioning, and locality-band routing with KV-co-indexed signature cache.
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Efficient Memory Management for Large Language Model Serving with PagedAttention
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abstract
High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2-4$\times$ with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM's source code is publicly available at https://github.com/vllm-project/vllm
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- abstract High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system
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Alignment defenses adapted from DPO and GRPO mitigate property inference attacks on LLMs while preserving utility.
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citing papers explorer
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Alignment Defends LLMs from Property Inference Attacks
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Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
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Alpha-RTL: Test-Time Training for RTL Hardware Optimization
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ARBITER: Reasoning Trajectory Basins and Majority Vote Failures in Test-Time Sampling
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Asymmetric Virtual Memory Paging for Hybrid Mamba-Transformer Inference
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Non-Monotonic Latency in Apple MPS Decoding: KV Cache Interactions and Execution Regimes
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DUET: Optimize Token-Budget Allocation for Reinforcement Learning with Verifiable Rewards
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KL for a KL: On-Policy Distillation with Control Variate Baseline
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PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training
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Neural Garbage Collection: Learning to Forget while Learning to Reason
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SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving
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Protection Is (Nearly) All You Need: Structural Protection Dominates Scoring in Globally Capped KV Eviction
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SLM Finetuning for Natural Language to Domain Specific Code Generation in Production
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