Efficient LLM Serving Strategies

Updated 24 November 2025

Efficient LLM serving is defined by integrated strategies that combine dynamic batching, memory management, and hardware adaptation to overcome inference bottlenecks.
Advanced techniques like bucket-based batching, elastic memory pooling, and KV cache optimizations reduce latency, boost throughput, and lower deployment costs.
Adaptive frameworks and precision reduction methods enhance resource utilization, enabling scalable, multi-tenant LLM deployments across heterogeneous systems.

Efficient Serving of LLMs

LLMs have become foundational in natural language processing, powering a broad spectrum of applications from conversational agents to automated code generation. However, the substantial computational and memory requirements of LLM inference present formidable challenges in system design for efficient, scalable, and cost-effective serving. Efficient LLM serving encompasses architectural strategies, advanced batching and memory management, adaptation to hardware heterogeneity, support for dynamic workloads, and multi-tenant or multi-model scenarios. This article synthesizes methodologies and empirical results from recent research to provide a comprehensive technical account of efficient LLM serving.

1. Bottlenecks and Principles of LLM Serving

LLM serving pipelines must address major bottlenecks including:

Model and KV Cache Memory: LLM inference requires memory for static model weights, dynamic activation tensors, and a rapidly growing key–value (KV) cache for storing intermediate representations during autoregressive decoding. KV cache management is especially challenging due to variable and growing sequence lengths and diverse request patterns (Kwon et al., 2023, Xu et al., 18 Jun 2025).
Batching and Latency Constraints: The throughput/latency trade-off is central. Aggressive batching improves hardware utilization but risks heightened response latency and can violate service-level objectives (SLOs).
Heterogeneity of Requests and Hardware: Serving systems must adapt to variable sequence lengths, unpredictable output lengths, and heterogeneous GPU types to maximize tokens-per-dollar efficiency (Griggs et al., 22 Apr 2024).
Scalability and Cold-Start: Scaling across many GPUs and addressing unpredictable load spikes (including minimizing cold start times) are critical for robust online services (Hu et al., 24 Jan 2025).

Achieving efficient LLM serving requires an integrated approach spanning algorithmic, system, and hardware-oriented optimizations.

2. Dynamic Batching and Scheduling Strategies

Dynamic batching is key to optimizing both throughput and memory utilization. Conventional static or continuous batching approaches incur inefficiencies due to internal padding and inability to adapt to input length diversity or fluctuating workloads. Recent frameworks provide several improvements:

Bucket-Based Dynamic Batching: BucketServe introduces a bucket-based scheme that groups requests by sequence length, dynamically adjusts bucket boundaries (split/merge), and determines safe batch sizes based on real-time GPU memory state. This reduces padding waste and OOM error risk, and incorporates priority-aware scheduling to meet latency SLOs (Zheng et al., 23 Jul 2025).

| System | Throughput Factor | SLO Attainment (A ≥ 80%) | | -------------- | ---------------- | ------------------------ | | BucketServe | Up to 3.58× over UELLM | Maintained up to 32 RPS | | Baseline (UELLM/DistServe) | 1.0× | Degrades earlier |

Generation-Length Prediction: Proxy-model-based output length predictors enable speculative shortest-job-first (SSJF) scheduling and HRRN policies, reducing head-of-line blocking and improving both average completion time and throughput (Qiu et al., 12 Apr 2024, Cheng et al., 7 Jun 2024).
Application-Specific Scheduling: Systems like Magnus leverage input-output length correlation, semantic features, and adaptive batch sizing under memory constraints to maximize utilization and reduce queuing delays (Cheng et al., 7 Jun 2024).

These techniques minimize computational redundancy and maximize GPU utilization under dynamic, heterogeneous workloads.

3. Memory Management and Cache Efficiency

KV cache management is central to scaling LLM serving. Approaches include:

PagedAttention and Fine-Grained Caching: vLLM uses OS-inspired paging, mapping logical KV blocks to pooled physical blocks, substantially reducing both internal and external fragmentation. Copy-on-write mechanisms support efficient branching and sharing (e.g., beam search or prefix tokens) (Kwon et al., 2023).
Elastic Memory Pooling: eLLM unifies tensor and KV cache allocations in a single elastic GPU memory pool, dynamically “ballooning” allocations via a virtual memory abstraction and offloading to CPU DRAM or host as pressure increases. This allows for up to 2.32× throughput and 295× lower time-to-first-token (TTFT) under heavy contexts compared to earlier static-allocation systems (Xu et al., 18 Jun 2025).
Stateful, Multi-Tier Cache Strategies: Pensieve and EFIM introduce stateful multi-turn caching to optimize reuse across conversational turns and infilling contexts; EFIM employs specialized prompt transformations and fragment tokenization to maximize cache hit rate on infilling tasks (Yu et al., 2023, Guo et al., 28 May 2025).

These advancements enable serving systems to sustain large batch sizes, maximize cache locality, and support longer contexts on limited hardware.

4. Adaptive and Morphing Serving Frameworks

Elastic adaptation to workload intensity is increasingly critical for SLO-driven production environments:

MorphServe provides asynchronous, token-level adaptation for constrained resources. It employs a feedback-driven loop to dynamically swap transformer layers between precision variants (FP16, INT8, INT4) based on current utilization and to adapt KV cache block allocation in real-time according to monitored pressure (Su et al., 24 May 2025). This reduces average SLO violations by 92%+ and improves P95 TTFT by up to 3.9× compared to full-precision static serving, with bounded impact on model quality.
KunServe introduces parameter-centric memory management, rapidly freeing HBM by selectively dropping redundant parameter replicas (rather than KV cache). The resulting pipeline-parallelism across GPUs allows immediate admission of new requests, preserving ongoing state, and reducing tail TTFT by up to 72.2× compared to KV cache-centric approaches (Cheng et al., 24 Dec 2024).

Adaptation frameworks are fully compatible with modern batched attention kernels and scheduling, emphasizing minimal migration and state-preserving transitions.

5. Multi-Model, Multi-Tenant, and Sparse Architectures

Efficient LLM serving increasingly entails simultaneous deployment of multiple model variants or very large sparse models. Specialized systems address these demands:

DeltaZip: For many full-model–fine-tuned LLMs, DeltaZip compresses each model’s fine-tune delta (via quantization, pruning, and GDeflate). At serve time, the base model is shared in GPU memory, and per-request deltas are streamed and composed on demand, yielding 2–12× throughput gains and minimal accuracy loss (Yao et al., 2023).
Multi-LoRA Serving: FASTLIBRA unifies LoRA adapter and KV caching into a dependency-aware pool managed by a cost model that minimizes TTFT by prefetching likely-needed objects and optimizing cache replacement. This reduces TTFT by 63.4% and increases cache hit rates to 88% under heavy multitenancy (Zhang et al., 19 Apr 2025).
Sparse/MoE Management: SwapMoE dynamically selects a small set of "virtual experts" per layer, maintaining just-in-time expert swapping and importance-based routing. This enables serving of large sparse models within strict memory budgets, halving latency over static pruning and outperforming on-demand swapping in both throughput and accuracy trade-off (Kong et al., 2023).

These frameworks permit both efficient scaling across large fleets of fine-tuned models and real-time task-specific adaptation.

6. Heterogeneous and Distributed Hardware Optimization

Maximizing cost-effectiveness and utilization often requires exploiting hardware heterogeneity and distributed resources:

Mélange models serving allocation as a cost-aware bin-packing problem, systematically profiling request size, rate, and SLOs against diverse GPU types (e.g., L4, A10G, A100, H100). It uses integer linear programming to assign request slices to GPU types and recommends an optimal hardware mix. Empirical evaluation shows up to 77% reduction in deployment cost under conversational loads, and robust SLO attainment (>99.5–99.95%) across real-world workloads (Griggs et al., 22 Apr 2024).
MoLink enables cost-efficient LLM serving on weakly networked, heterogeneous consumer GPUs using dynamic micro-batch scheduling, prioritized chunked transmission, and layer assignment proportional to compute capacity. This yields up to 458% throughput improvements and 151% higher cost-profit margins over high-end GPU baselines (Jin et al., 7 Jul 2025).

Efficient mapping of workload slices and prioritization of network resources are essential to achieving high utilization and minimal cost across heterogeneously provisioned infrastructures.

7. Precision Reduction and Format Optimization

Reducing arithmetic and memory precision is indispensable for cost-effective, high-throughput LLM serving on modern accelerators:

Block Floating-Point (BFP) and Microscaling Extensions: MX+ extends BFP to specifically increase precision for per-block outliers by reusing the exponent field as additional mantissa bits for the block maximum. MX+ achieves quality close to 6-bit quantization at the storage and bandwidth cost of 4.5 bits per element, with negligible runtime overhead when hardware-accelerated (Lee et al., 16 Oct 2025).
Low-Precision Serving: Real-world deployments employ low-bit quantization of weights, KV cache, and activations, together with hardware and software adaptation for generalized deployment (e.g., TensorRT-LLM, Triton) (Lee et al., 16 Oct 2025, Yao et al., 23 Jul 2024).

Adaptive format selection based on model, hardware, and workload profile is essential for maximizing tokens-per-second-per-dollar efficiency at scale.

Efficient LLM serving advances through an overview of batching/scheduling innovation, memory and cache management, precision reduction, elastic adaptation, and heterogeneity-aware resource allocation. These approaches collectively enable order-of-magnitude improvements in cost, throughput, and latency, facilitating broad and robust deployment of LLM-powered systems across increasingly diverse hardware and workload environments.