vLLM System: Efficient LLM Serving

• vLLM is a high-performance LLM serving system that uses the PagedAttention algorithm to manage KV caches with near-zero memory waste.
• It employs dynamic continuous batching and fine-grained page sharing, achieving 2–4× throughput improvements over traditional systems.
• Empirical benchmarks highlight enhanced GPU utilization, reduced memory fragmentation, and superior performance in high-concurrency scenarios.

vLLM is a LLM serving system designed to maximize throughput, GPU memory efficiency, and usability across a broad spectrum of production and research workloads. At its core, vLLM introduces the PagedAttention algorithm—an OS-inspired virtual memory mechanism for managing the exponentially growing key-value (KV) caches required for autoregressive decoding. vLLM achieves near-zero internal and external KV-cache waste, fine-grained sharing across requests, and demonstrates consistent 2–4× throughput improvements over conventional LLM-serving systems, particularly in high-concurrency and long-context scenarios (Kwon et al., 2023, Kolluru, 17 Nov 2025).

1. Architectural Principles and Memory Management

The foundational challenge for LLM serving is the efficient handling of rapidly expanding per-request KV caches. Autoregressive transformers compute token probabilities as

$$P(x_1,\dots,x_n) = P(x_1) \prod_{i=2}^n P(x_i | x_1, \dots, x_{i-1}),$$

which necessitates retaining all prior $k_j, v_j$ for every new token decoded. The naive per-request preallocation of contiguous KV caches (e.g., in FasterTransformer, Orca) causes severe internal fragmentation, external fragmentation, and prevents sharing across requests with common prefixes or during beam search. On leading GPUs (A100, 40GB), total KV-cache memory can consume ∼30GB, drastically restricting batch size and system throughput (Kwon et al., 2023).

PagedAttention reframes the KV-cache as a set of fixed-size "pages" (e.g., $B=16$ tokens per block). Rather than maintaining a single contiguous tensor for each sequence, a lightweight page table records logical-to-physical block mappings. Each decode iteration selectively loads only the necessary physical blocks ($K_j, V_j$) via coalesced block reads, and these blocks are re-used across requests and beams by copy-on-write (COW) semantics. This design allows KV-cache utilization in excess of 90% (empirically measured), vastly outperforming Orca and FasterTransformer which achieve 20–40% utilization (Kwon et al., 2023).

2. System Components and Scheduling

vLLM's architecture comprises:

• A centralized scheduler issuing iteration-level batches and orchestrating memory decisions.
• GPU workers executing inference, with support for Megatron-style tensor-parallel fragments, all maintaining a shared global paging view.
• A block manager, responsible for logical-to-physical page management, reference counting for sharing and COW, and all-or-nothing eviction when memory is depleted.
• Separate allocators for GPU and CPU memory to implement paging and swapping as needed.

Page management routines follow the OS-style paradigm: new requests allocate pages for prompt tokens, decode iterations allocate on-demand, requests are compacted upon completion, and when GPU memory is exhausted, entire requests (not just blocks) are swapped out (Kwon et al., 2023).

Continuous batching is implemented: new and waiting requests are dynamically injected into decode iterations, maximizing GPU occupancy with minimal scheduling latency. This approach allows for stable throughput even under non-uniform arrival and completion rates (Kolluru, 17 Nov 2025, Wang et al., 2024).

3. PagedAttention Algorithm and Mathematical Formalism

PagedAttention partitions the KV-cache for a sequence of length $L$ into $N = \lceil L/B \rceil$ pages, each holding up to $B$ tokens' key/value vectors. The attention computation at step $i$ is rewritten to operate block-wise as: $$A_{i,j} = \frac{\exp(q_i^\top K_j / \sqrt{d})}{\sum_{t=1}^{\lceil i/B\rceil} \exp(q_i^\top K_t \mathbf{1} / \sqrt{d})}, \quad o_i = \sum_{j=1}^{\lceil i/B\rceil} V_j A_{i,j}^\top$$ with a page table $\pi(j)$ mapping logical pages to physical GPU blocks (Kwon et al., 2023). This mechanism enables both non-contiguity and fine-grained sharing: blocks belonging to shared prefixes or parallel sampled sequences are mapped to the same GPU memory until a write creates divergence (COW).

The memory waste per request is $W_{in} = (nB-L)/nB$ for $n=\lceil L/B\rceil$; choosing $B\ll L$ ensures $W_{in}\to 0$. External fragmentation is eliminated, as all blocks have fixed size (Kwon et al., 2023).

4. Comparative Performance and Empirical Findings

Extensive benchmarking demonstrates vLLM's performance edge under high concurrency and long context settings:

• On OPT-13B with ShareGPT prompts, vLLM achieves ∼1100 requests/s at 10 ms per token versus Orca(Oracle) at ∼450 req/s and FasterTransformer at ∼50 req/s (Kwon et al., 2023).
• For beam search (beam width=6), vLLM is 2.3× faster than Orca(Oracle) and achieves up to 55–66% KV-cache sharing across beams.
• On batch-oriented, production workloads (e.g., 200 users, LLaMA-2-7B), vLLM yields up to 24× throughput advantage over HuggingFace TGI due to its paged KV-cache and continuous batching; TGI is favored only for ultra-low-latency, single-user interactive scenarios (Kolluru, 17 Nov 2025).
• GPU utilization is increased by 19–27%; peak memory usage is reduced consistently, enabling larger batch sizes at the same footprint (Kolluru, 17 Nov 2025).
• In multi-modal, vision-language pipelines (e.g., FlexEdit), vLLM serves as the backbone for integrated LLM+diffusion architectures, supporting image-conditioned tokenization and joint training objectives (Wang et al., 2024).

5. Extensions: Semantic Routing, Mixed Reasoning, and Specialized Attention

vLLM includes native support for flexible reasoning modes and query routing. A ModernBERT classifier (fine-tuned for reasoning detection) is integrated to route inputs to either neutral reasoning (NR) or explicit chain-of-thought (XC) generation modes. This "semantic router" increases MMLU-Pro accuracy by +10.2 percentage points while reducing latency and token usage by 47% and 48%, respectively—a direct result of only selectively invoking expensive reasoning pipelines (Wang et al., 9 Oct 2025).

RelayAttention, integrated into vLLM for long system prompts, exploits per-batch reuse of static KV-caches. By reading static system prompt KV blocks from memory only once for each batch (rather than B times for B requests), RelayAttention approximately doubles end-to-end throughput and raises sustainable request rates 2.2× at $S=2048$ system-prompt tokens (Zhu et al., 2024). This method is mathematically exact and does not affect generation; it is particularly effective when $S\gg C$, with $B>1$ and long shared prompts.

OmniInfer further extends vLLM's system-level optimizations with modules for load-aware Mixture-of-Experts (MoE) scheduling (OmniPlacement), sparse attention acceleration (OmniAttn), and global RPC-level scheduling for disaggregated deployments (OmniProxy). These augmentations yield up to 52% increased throughput (Queries Per Minute), TPOT reduction by 33%, and negligible impact on benchmark accuracy (Wang et al., 27 Nov 2025).

6. Deployment, Tuning, and Practical Recommendations

Efficiency gains in vLLM are contingent upon appropriate tuning of batch size and tensor parallelism. Throughput landscapes are highly irregular; optimal batch sizes and GPU configurations depend on model size, hardware (e.g., A100/V100), and prompt length (Martinez, 2024). It is essential to perform lightweight grid or population-based hyperparameter searches for each deployment. Key guidelines:

• Use the largest batch size (within memory constraints) for high-throughput, batch workloads.
• Apply continuous batching and leverage paged KV-caching for dynamic, real-time requests.
• For real-time latency-sensitive services, keep $max\_new\_tokens$ small and monitor for excessive page faults.

When paired with semantic routing and memory-aware scheduling, vLLM offers an operational envelope amenable to both high-throughput APIs and efficiency-constrained environments (Wang et al., 9 Oct 2025, Wang et al., 27 Nov 2025).

7. Research Integrations and Applications

The vLLM runtime is now a foundation for multi-turn self-supervised reinforcement (e.g., CAGSR-vLLM-MTC), where kernel instrumentation captures per-layer attention distributions for reward computation in chain-of-thought and dialog RL fine-tuning (Kiruluta et al., 8 Jun 2025). In vision–language systems (FlexEdit, Wonderful Team), vLLM is used for end-to-end planning and editing, leveraging its tokenization and streaming generation capabilities for controlling downstream diffusion models or robotic pipelines (Wang et al., 2024, Wang et al., 2024).

Across the ecosystem, vLLM's paged memory, block sharing, and deterministic scheduling abstractions continue to underpin state-of-the-art results in large-scale LLM serving, multi-modal generation, hybrid MoE/sparse systems, and self-supervised fine-tuning regimes. This modular, high-throughput architecture is widely adopted for both academic benchmarking and production LLM APIs (Kwon et al., 2023, Kolluru, 17 Nov 2025, Wang et al., 27 Nov 2025, Martinez, 2024, Zhu et al., 2024, Wang et al., 9 Oct 2025, Kiruluta et al., 8 Jun 2025).