Scaling LLM Inference with Dynamic Batch Sizing: Balancing Throughput and Latency in Real-Time AI Systems
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Introduction to Scaling LLM Inference
Large Language Models (LLMs) power many modern AI applications, but their computational demands make real-time inference challenging. Scaling LLM inference means increasing the number of requests processed per second (throughput) while keeping the delay before results are returned (latency) within acceptable limits. Achieving this balance is critical for meeting service-level agreements (SLAs) in production systems.
Traditional approaches use static batching, where incoming queries are grouped into fixed-size batches before being processed on GPUs. While simple, static batching can lead to inefficiencies: if the batch is too small, GPU resources are underused; if too large, latency increases, potentially violating SLAs. Recent research has proposed more dynamic and adaptive methods to address these trade-offs without redesigning the core inference infrastructure.
One notable advance is dynamic batching that adapts batch sizes based on real-time GPU memory usage and SLA constraints. By continuously adjusting how many queries are batched together, this approach improves throughput by around 8% to 28% and boosts system capacity by more than 20%. It balances computational efficiency and latency effectively through memory-awareness and SLA constraints, allowing batch sizes to expand or shrink in response to current load and hardware availability (source).
In distributed LLM serving, imbalanced workloads can create pipeline bubbles, reducing efficiency. A global balanced pipeline parallelism system addresses this by dynamically regulating tokens during prefill and decode phases, smoothing the workload distribution. This token throttling mechanism leads to throughput gains between 11% and nearly 400%, with reduced latency compared to earlier methods (source).
Beyond batch size and workload distribution, scheduling based on predicted response lengths can further optimize inference. By grouping queries that are expected to produce similar output lengths, redundant computations are minimized, and micro-batch formation is improved. This length-aware scheduling has demonstrated up to an 86% increase in throughput while maintaining output quality (source).
Together, these innovations demonstrate that intelligently adapting batch sizes, balancing workloads globally, and scheduling based on response characteristics are key strategies for scaling LLM inference. They provide viable paths to enhance throughput and reduce latency in real-time AI systems, enabling efficient and reliable deployment of large-scale language models.
Challenges in Real-Time LLM Inference: Throughput vs Latency
Real-time large language model (LLM) inference presents a fundamental tension: maximizing throughput while minimizing latency. Throughput measures how many queries a system can handle per unit time, whereas latency refers to the delay before a response is returned. Balancing these metrics is critical for applications requiring swift, scalable AI services, but achieving this balance remains challenging.
The Throughput-Latency Trade-Off
High throughput often relies on batching multiple queries together for efficient GPU utilization. Larger batch sizes increase computational efficiency by amortizing overhead and maximizing parallelism. However, batching can also increase latency since queries must wait for enough peers to arrive before processing begins. This delay conflicts with real-time demands, where users expect near-instantaneous responses. Conversely, prioritizing low latency by processing smaller batches or individual queries underuses computational resources, reducing throughput and system capacity.
Dynamic Batching to Resolve the Conflict
Recent research proposes dynamic batching to navigate this trade-off intelligently. One effective approach continuously adjusts batch sizes in response to available GPU memory and service-level agreement (SLA) latency targets. By monitoring real-time constraints, the system grows batch sizes when possible to boost throughput but shrinks them to meet latency deadlines. This dynamic control improves throughput by 8% to 28% and increases overall processing capacity by 22% compared to static batching, all without requiring changes to existing inference infrastructure (arXiv:2503.05248).
Workload Balancing Across Distributed Systems
For distributed LLM serving, the challenge expands to balancing workloads across pipeline parallelism stages. Imbalance leads to pipeline bubbles—idle waiting times that degrade throughput and increase latency. By implementing token throttling mechanisms, systems can regulate token flow during both prefill and decode phases, creating a more balanced pipeline. This method not only reduces latency but also achieves throughput improvements ranging from 11% up to nearly 400%, surpassing previous state-of-the-art systems (arXiv:2504.14775).
Length-Aware Scheduling for Efficiency
Another dimension of real-time inference optimization is accounting for variable response lengths. Queries generating longer responses take more computational resources and time, potentially causing others to wait. Leveraging a length perception module to predict output size allows the grouping of queries with similar expected lengths into micro-batches. This strategy minimizes redundant operations and improves overall throughput by up to 86%, while maintaining output quality and respecting latency constraints (arxiv.org/pdf/2305.13144.pdf).
Summary
The challenges of real-time LLM inference revolve around the inherent trade-off between throughput and latency. Static batching strategies fall short in adapting to fluctuating workloads and SLAs. Dynamic batching, balanced pipeline parallelism with token throttling, and length-aware scheduling represent promising solutions. By intelligently adapting batch size and workload distribution according to real-time conditions, these methods deliver higher throughput without sacrificing latency, enabling scalable and responsive AI systems.
Dynamic Batching Techniques for LLM Inference
Scaling inference for large language models (LLMs) demands techniques that balance throughput and latency under real-time constraints. Dynamic batching has emerged as a key approach, where batch sizes are adjusted on the fly rather than fixed beforehand. This section explores recent advances in dynamic batching strategies that optimize resource usage while respecting service-level agreements (SLAs).
Memory-Aware and SLA-Constrained Dynamic Batching
One effective method involves continuously adapting batch sizes based on GPU memory availability and SLA targets. The technique monitors memory usage in real time and adjusts the number of requests batched together accordingly. When memory headroom increases, larger batches are formed to maximize throughput. Conversely, it trims batch sizes proactively when approaching memory limits to avoid latency spikes or out-of-memory errors. This approach improved throughput by 8% to 28% and raised overall system capacity by 22% compared to static batching without any changes to the underlying inference infrastructure (source). Such real-time adaptivity ensures efficient utilization of hardware resources while maintaining low-latency responses.
Balanced Pipeline Parallelism with Token Throttling
Another complementary technique targets distributed LLM serving pipelines, where imbalances in batch computations can cause pipeline stalls or "bubbles." A global balanced pipeline parallelism system introduces a token throttling mechanism that regulates the flow of prefill and decode tokens across pipeline stages. By controlling token generation rates globally, it prevents bottlenecks and keeps all pipeline segments well utilized. This method achieved throughput improvements ranging from 11% to 398% alongside latency reductions over state-of-the-art distributed serving methods (source). Integrating token throttling with dynamic batching helps maintain smooth execution across distributed inference hardware.
Length-Aware Micro-Batching
A third dynamic batching strategy focuses on response length prediction and scheduling. By using an auxiliary length perception module to estimate the expected output size of each query, the system groups queries with similar response lengths into micro-batches. This reduces padding and redundant computation caused by variable-length outputs while preserving output quality. Implementing length-aware micro-batching led to an 86% increase in inference throughput in experimental systems (source). This approach complements memory- and pipeline-based techniques by optimizing the microstructure of batches rather than just their size.
Summary
Recent research demonstrates that dynamic batching for LLM inference benefits from integrating multiple strategies. Adjusting batch size based on GPU memory and SLA constraints maximizes capacity without sacrificing latency. Coordinating batch flow across distributed pipelines via token throttling prevents stalls and keeps throughput high. Grouping queries by predicted response length further reduces wasted computation. Together, these dynamic batching methods form a flexible toolkit for scaling LLM inference in real-time AI systems, delicately balancing throughput and latency to meet operational demands.
Memory-Aware and SLA-Constrained Dynamic Batching
Scaling large language model (LLM) inference effectively requires intelligent batch management that adapts to both hardware limitations and real-time service demands. A recent approach that stands out is the memory-aware and SLA-constrained dynamic batching technique, which adjusts batch sizes on the fly based on current GPU memory availability and strict latency requirements dictated by service-level agreements (SLAs) (arXiv:2503.05248).
This method continuously monitors GPU memory usage during inference and dynamically resizes batches to maximize throughput without exceeding memory capacity. By integrating SLA constraints, the system ensures inference latency stays within acceptable bounds, crucial for real-time applications. This dual consideration allows the system to strike a balance where batch sizes are as large as possible to utilize hardware efficiently, but never so large that latency SLAs are violated or memory limits are breached. The result is a significant improvement: throughput increases from 8% to 28%, and capacity—the number of requests served concurrently—rises by about 22% compared to static batching methods that use fixed batch sizes regardless of demand and resource usage.
Balancing Efficiency and Latency Without Infrastructure Changes
One of the practical advantages of this dynamic batching approach is that it does not require redesigning existing inference infrastructure. Instead, it builds on traditional model serving setups by adding a real-time controller that modulates batch composition based on live metrics. This minimizes integration complexity while leveraging memory and latency information to make more intelligent scheduling decisions.
This memory-aware strategy contrasts with static heuristics, which might either under-utilize GPUs by choosing too small batches or cause latency spikes and memory overflows if batches are too large. The dynamic method ensures a smoother, more responsive system that adapts fluidly to workload fluctuations and varying input lengths without sacrificing the quality of service.
Complementary Strategies: Pipeline Parallelism and Length-Aware Scheduling
While memory-aware dynamic batching focuses on per-GPU resource and SLA constraints, other strategies address different performance bottlenecks. For example, global balanced pipeline parallelism with token throttling tackles uneven workload distribution across model pipeline stages, reducing stalls that arise from imbalanced batch sizes in distributed LLM serving (arXiv:2504.14775). This technique regulates the number of tokens processed in each phase, preventing pipeline bubbles and boosting throughput by up to 398% with lower latency.
Similarly, grouping requests by predicted response length, as enabled by a length perception module, creates micro-batches of queries with similar output sizes. This reduces redundant computation and improves throughput by 86%, without impacting output quality (arxiv.org/pdf/2305.13144.pdf).
Collectively, these memory-aware, SLA-constrained dynamic batching approaches combined with pipeline balancing and length-aware scheduling represent a holistic toolkit. They enable real-time AI systems to scale LLM inference efficiently while meeting strict latency guarantees, ultimately delivering a smoother and more performant user experience.
Continuous Batch Size Adjustment Based on GPU Memory Usage
One of the key challenges in scaling large language model (LLM) inference is making the best use of available GPU resources without exceeding memory limits or violating latency requirements. A recent approach called memory-aware dynamic batching tackles this by continuously adjusting batch sizes during runtime, based primarily on real-time GPU memory usage and service-level agreement (SLA) constraints (arXiv:2503.05248).
How Dynamic Batching Works
Unlike static batching, where the batch size is fixed ahead of time, dynamic batching monitors the GPU memory utilization while the system is serving inference requests. The system scales the batch size up or down incrementally, striving to maximize throughput without causing out-of-memory errors or increasing latency beyond SLA limits. This continuous batch size tuning helps avoid two common issues: underutilized GPU memory when batches are too small, and excessive queuing delays or failures when batches are too large.
By integrating feedback mechanisms tied to memory consumption, dynamic batching intelligently balances computational efficiency and latency. It achieves higher GPU utilization and allows the inference pipeline to process more requests concurrently, thus boosting overall throughput and system capacity.
Benefits Observed
In experiments, this adaptive batching strategy demonstrated throughput improvements ranging from 8% to 28% compared to traditional static batch sizes. Furthermore, it increased GPU capacity—effectively the number of requests handled per unit time—by around 22%. Notably, these gains come without requiring changes to existing inference infrastructure or model deployment, making it a practical enhancement for real-world systems (arXiv:2503.05248).
Relation to Pipeline Balancing and Scheduling
Complementary strategies enhance this approach by addressing the variability in workload composition. For example, a global balanced pipeline parallelism system reduces inefficiencies caused by unbalanced compute pipelines in distributed LLM serving. It uses token throttling to regulate workload flow and prevent pipeline bubbles, achieving higher throughput with lower latency (arXiv:2504.14775). Additionally, LLM inference pipelines that predict the expected response length can group queries of similar length into micro-batches, reducing computational redundancy and further boosting throughput by up to 86% (arXiv:2305.13144).
Together, these methods illustrate a broader trend toward dynamic, memory- and workload-aware batching that continuously adapts to system state and workload characteristics. Such intelligent approaches allow LLM inference systems to scale efficiently while meeting strict latency and SLA requirements in real-time environments.
Throughput and Capacity Improvements Over Static Batching
Dynamic batch sizing introduces a significant advancement over traditional static batching methods for large language model (LLM) inference, primarily by adapting batch sizes in real time to optimize GPU resource utilization and meet strict latency constraints.
One core improvement comes from the memory-aware and SLA-constrained dynamic batching approach proposed in recent research. Instead of fixing batch sizes ahead of time, this method continuously adjusts the batch size based on real-time GPU memory availability and service-level agreement (SLA) requirements. This allows the system to maximize throughput without sacrificing latency guarantees, resulting in throughput improvements between 8% and 28% and an overall capacity increase of about 22% compared to static batching setups. Importantly, these gains are achieved without modifying the existing inference infrastructure, making it a practical approach to scale LLM inference in real-world applications (source).
Beyond memory-aware batching, achieving balanced workload distribution during distributed inference is crucial to avoiding pipeline bottlenecks. The gLLM system advances this by implementing a global balanced pipeline parallelism combined with token throttling. It dynamically regulates prefill and decoding tokens across pipeline stages, minimizing pipeline stalls caused by imbalanced batch sizes. This technique leads to throughput gains ranging from 11% up to 398%, while also lowering latency compared to previous state-of-the-art methods. By controlling token flow globally, gLLM effectively smooths out the workload, making throughput maximization more robust in distributed LLM serving environments (source).
Another dimension of throughput improvement comes from length-aware scheduling strategies. Predicting the expected response length of queries enables the grouping of requests with similar output sizes into micro-batches. This approach reduces redundant computational overhead by minimizing variance in sequence lengths within a batch. For example, the use of a length perception module to pre-classify queries has demonstrated an 86% throughput gain by ensuring that computational resources are used more efficiently while preserving output quality and latency constraints (source).
Together, these strategies underscore that throughput and capacity improvements over static batching are achievable not only through adjusting batch sizes dynamically but also by intelligent workload balancing and query-aware scheduling. These techniques balance efficiency and responsiveness, crucial for maintaining high-performance real-time AI systems powered by LLMs.
Adjusting Batch Sizes Dynamically to Meet Latency Targets
Maintaining low latency in large language model (LLM) inference while scaling throughput is challenging because larger batch sizes generally improve GPU utilization but increase per-request delay. A promising approach to address this is dynamic batching that adapts batch sizes in real time based on memory availability and latency constraints. By monitoring GPU memory usage, the system can increase batch sizes when resources permit and reduce them just enough to avoid latency SLA violations. This method avoids the rigid trade-off inherent to static batching, where batch size is fixed and either throughput or latency suffers.
The recent work "Optimizing LLM Inference Throughput via Memory-aware and SLA-constrained Dynamic Batching" demonstrates this principle by continuously adjusting batch sizes to maintain SLA-tailored latency without changing the underlying inference infrastructure. Their approach reported throughput improvements of 8% to 28% alongside a 22% capacity gain—all while meeting latency Service Level Agreements (SLAs). This proves that intelligent, feedback-driven batch size tuning can respect latency budgets at scale without infrastructure overhauls (source).
Balancing Pipeline Efficiency Without Latency Compromise
Another method to maintain latency requirements involves balancing the distributed processing pipeline more evenly to avoid stalls and bubbles that cause latency spikes. The "gLLM" system introduces a global balanced pipeline parallelism that uses token throttling to regulate load in the pipeline dynamically. By synchronizing token processing rates for prefill and decode phases, it reduces idle times and ensures consistently low latency while boosting throughput by 11% to nearly 400%. This token-level control complements batch size adjustment by smoothing the flow of computation without infrastructure changes, meeting real-time constraints effectively (source).
Length-Aware Scheduling for Latency Control
Latency variability also arises from the diverse lengths of generated responses. Grouping queries with similar response lengths allows more efficient micro-batching, minimizing wasted computation on shorter sequences padded to the longest request size. The approach presented in "Response Length Perception and Sequence Scheduling" employs a length prediction module to cluster queries by expected response length, delivering an 86% throughput gain while sustaining output quality and latency consistency. Since this scheduling logic overlays existing inference pipelines, it preserves system architecture but enhances latency predictability and resource utilization (source).
Summary
Across these techniques, the common thread is adapting batch sizes, balancing pipeline workloads, and scheduling intelligently based on output characteristics—all without modifying the core inference hardware or software stack. This enables LLM serving systems to dynamically maintain latency SLAs in real-time environments while boosting throughput and capacity. Such solutions are crucial as deploying large models broadly demands both scale and strict latency guarantees.
Global Balanced Pipeline Parallelism for Distributed LLM Serving
Scaling large language model (LLM) inference across multiple GPUs or nodes typically requires pipeline parallelism, where a model is split into stages and each stage runs on a different device. While this approach increases overall capacity, it suffers from an issue known as pipeline bubbles—idle times when some stages wait for others to produce outputs, reducing hardware utilization and throughput. The recent work on gLLM introduces a global balanced pipeline parallelism system that specifically targets these bubbles by focusing on workload balancing and token-level control.
The core idea behind gLLM is to regulate the flow of tokens in the pipeline through a token throttling mechanism. This throttling synchronizes prefill tokens (which initialize the decoder state) and decode tokens (which produce the final output) across all stages. By aligning the token processing rates of each pipeline stage, gLLM prevents stages from becoming bottlenecks or idling unnecessarily, thus maintaining a smooth and balanced pipeline flow.
This global balancing approach contrasts with more static or local balancing methods, which often fail to address imbalances that arise dynamically due to varying batch sizes or token processing durations. By continuously adjusting the token flow based on runtime feedback, the system achieves significantly higher throughput—reported improvements range from 11% up to nearly 400% in some settings—with the added benefit of lower latency compared to existing state-of-the-art distributed serving frameworks (source).
Moreover, this dynamic token-level management complements other strategies like dynamic batching and length-aware scheduling. For instance, while dynamic batch sizing optimizes GPU memory usage and SLA adherence, and length prediction groups similar queries to reduce redundant computation, global balanced pipeline parallelism ensures that these optimized batches are efficiently processed without stalling the pipeline.
In summary, gLLM’s approach demonstrates that intelligently managing pipeline parallelism at the granularity of tokens is critical for maximizing throughput and minimizing latency in distributed LLM serving. It represents a practical and effective solution to the common challenge of pipeline bubbles, thereby enabling real-time AI systems to scale inference workloads while respecting strict latency requirements.
Addressing Pipeline Bubbles via Balanced Batch Computations
One major challenge in scaling large language model (LLM) inference is handling pipeline bubbles—idle times that occur when some stages of the inference pipeline wait for others to complete. These bubbles reduce overall throughput and waste computational resources. Recent approaches focus on balancing batch computations across pipeline stages to minimize these inefficiencies without sacrificing latency or throughput.
A key insight comes from the global balanced pipeline parallelism system introduced in "gLLM: Global Balanced Pipeline Parallelism System for Distributed LLM Serving with Token Throttling" (source). This method uses token throttling to regulate the flow of tokens between the prefill stage (initial input processing) and the decode stage (token generation). By dynamically adjusting how many tokens are processed in each stage, the system maintains better pipeline balance, significantly reducing idle times. This strategy achieves throughput gains from 11% up to 398%, all while lowering latency compared to existing pipeline parallelism techniques.
Complementing this, dynamic batching methods adapt batch sizes in real-time based on current GPU memory use and SLA constraints, as detailed in "Optimizing LLM Inference Throughput via Memory-aware and SLA-constrained Dynamic Batching" (source). Instead of static batch sizes that can cause pipeline stalls when buffers fill unevenly, this method continuously tunes batch sizes to match hardware capacity and SLA demands. The result is an 8% to 28% throughput improvement and a 22% increase in capacity without needing infrastructure changes.
Another angle to reducing pipeline bottlenecks is the notion of length-aware scheduling, highlighted in "Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline" (source). This technique predicts the expected response length of incoming queries and groups similar-length queries into micro-batches. By aligning sequences of comparable lengths, the system avoids uneven processing times that typically create bubbles between pipeline stages. This approach improved inference throughput by 86% while preserving output quality.
Taken together, these strategies show that addressing pipeline bubbles requires a combination of workload balancing, dynamic batch adjustments, and length-aware scheduling. Such balanced batch computations optimize resource usage and streamline token flow through the pipeline, enabling real-time LLM inference systems to meet stringent throughput and latency SLAs more effectively.
Token Throttling Mechanism for Prefill and Decode Stages
Scaling large language model (LLM) inference for real-time applications demands not only dynamic batch sizing but also intelligent regulation of token flow through the pipeline stages. A prominent approach to achieve this is through token throttling mechanisms that specifically manage the prefill and decode stages of LLM inference.
The prefill stage handles the input tokens that initiate the model’s attention context, while the decode stage generates tokens sequentially as output. Imbalances between these stages can cause pipeline bubbles—periods where some GPU resources are underutilized because other stages are waiting for data to process. This mismatch reduces overall throughput and increases latency.
Token throttling dynamically controls the rate at which tokens move from the prefill stage into the decoding stage. By carefully regulating how many tokens are processed and buffered at each step, the system maintains a balanced pipeline where computational resources are continuously utilized without bottlenecks. This results in smoother workload distribution and less idle time across GPUs.
For distributed LLM serving, the "gLLM" system demonstrated that implementing token throttling achieves substantial throughput gains—ranging from 11% up to 398%—while also reducing latency compared to state-of-the-art pipeline parallel methods. It does so by globally balancing the pipeline to prevent batch computation stalls and by selectively pausing token generation to ensure decoder workloads remain aligned with prefill progress (source).
This approach works in concert with dynamic batching strategies that adjust batch sizes based on real-time system constraints such as GPU memory and service level agreement (SLA) targets. Together, these mechanisms enable LLM inference pipelines that are both computationally efficient and latency-sensitive. The orchestration between token throttling and dynamic batch sizing avoids overloading any single stage, thereby increasing effective throughput by up to 28% and system capacity by over 20% without requiring changes to existing inference infrastructure (source).
In summary, token throttling mechanisms serve as critical control points in scaling LLM inference. By managing token flow between prefill and decode stages, they mitigate pipeline bubbles, balance workloads, and complement dynamic batch sizing to meet real-time AI system demands. This highlights the importance of stage-level regulation alongside batch-level optimization for achieving scalable, low-latency LLM serving.
Throughput and Latency Gains Against State-of-the-Art Methods
Scaling LLM inference while balancing throughput and latency is a critical challenge in real-time AI systems. Recent advances focus on dynamic and intelligent batching strategies that adjust to workload conditions, improving efficiency without sacrificing response times.
Dynamic Batching Based on Memory and SLA Constraints
One leading approach implements dynamic batching that adjusts batch sizes in real time by monitoring GPU memory usage and adhering to service-level agreement (SLA) constraints. This method avoids static batch sizes that can either underutilize resources or introduce unacceptable delays. By continuously tuning the batch size to the available memory and latency requirements, it achieves throughput improvements ranging from 8% to 28%, and increases model serving capacity by about 22% compared to traditional static batching techniques. Importantly, this dynamic batching works within existing inference infrastructure, making it a practical upgrade for many deployments (source).
Pipeline Parallelism with Token Throttling
Another noteworthy strategy targets the bottlenecks in distributed LLM serving through global balanced pipeline parallelism. Traditional pipeline methods often face "pipeline bubbles" caused by imbalanced batch computations, which reduce overall throughput. The system addressed this with a token throttling mechanism that controls the flow of prefill and decode tokens across distributed nodes, balancing the workload more effectively. As a result, this method achieves between 11% and 398% higher throughput while also reducing latency compared to state-of-the-art baselines. This approach exemplifies how regulating token flow in multi-node setups can unlock significant performance gains without complex changes to model architecture (source).
Length-Aware Scheduling for Micro-Batches
A complementary technique improves efficiency by predicting the response lengths of incoming queries. Using a length perception module, the system groups queries with similar expected output sizes into micro-batches. This reduces redundant computation and resource idling caused by processing very different-length sequences in the same batch. This scheduling method can increase inference throughput by as much as 86%, while maintaining output quality. It highlights the value of tailoring batch formation to the characteristics of queries, rather than purely workload volume or memory considerations (source).
Summary
Together, these studies demonstrate that adapting batch sizes dynamically based on memory availability and SLA targets, balancing workloads through token-level flow control in distributed pipelines, and scheduling by predicted response length can significantly improve LLM inference throughput without compromising latency. These insights provide an actionable blueprint for building scalable real-time AI systems that meet operational performance requirements more efficiently.
The Role of Response Length Perception in LLM Inference
One of the more nuanced challenges in scaling large language model (LLM) inference is handling the variability in response lengths generated per query. Since different prompts can produce significantly different token counts, the computational workload varies drastically, affecting both throughput and latency. The work titled "Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline" proposes an effective way to manage this variability by integrating a response length prediction step early in the inference pipeline.
This length perception module estimates the expected output length for incoming requests before processing. By accurately predicting response sizes, the system can group or micro-batch queries with similar predicted output lengths together. This strategy reduces inefficient resource allocation and redundant computations caused by imbalanced batch processing. The reported impact is substantial: an 86% increase in inference throughput was achieved without sacrificing output quality, demonstrating the practical advantages of response length-aware scheduling (source).
Sequence Scheduling and Micro-Batching for Balanced Pipelines
Beyond length perception, the efficient scheduling of sequences in the inference pipeline is crucial for maintaining balanced utilization of hardware resources. Grouping sequences with similar lengths means the pipelines can operate more synchronously, avoiding so-called "pipeline bubbles" where certain GPUs or compute units remain idle waiting for longer sequences to finish.
Using micro-batches composed of similar-length sequences narrows the execution time variance within a batch, leading to smoother pipeline parallelism and better GPU memory usage. This method contrasts with naive batching strategies that group requests indiscriminately, leading to large disparities in computation time and thus lower overall throughput.
When combined with global balancing strategies such as token throttling and distributed pipeline parallelism, length-aware scheduling unlocks additional performance gains. For example, one study achieved 11% to 398% throughput improvements by regulating prefill and decode tokens in tandem with balanced micro-batching (source).
Impact on Real-Time AI Systems and SLA Compliance
For real-time AI systems, maintaining a strict balance between throughput and latency is pivotal due to service-level agreements (SLAs). Dynamic batch sizing approaches already consider GPU memory and SLA constraints to adjust batch sizes on the fly, improving throughput by up to 28% and capacity by 22% (source).
Incorporating response length perception and sequence scheduling further refines this balancing act. By anticipating workload characteristics and structuring inference sequences accordingly, systems can avoid sudden latency spikes caused by processing outlier long sequences. This makes it easier to meet SLAs consistently while maximizing hardware utilization.
In summary, response length perception combined with intelligent sequence scheduling forms a cornerstone of advanced LLM inference pipelines. These techniques complement dynamic batching and distributed pipeline balancing methods, collectively enabling scalable, efficient, and SLA-compliant real-time LLM inference systems.
Length Prediction Module to Group Queries into Micro-Batches
One key strategy to improve the efficiency of large language model (LLM) inference is the use of a length prediction module to group incoming queries into micro-batches based on their expected output length. This approach, explored in the paper "Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline," leverages a prediction mechanism that estimates the number of tokens the model will generate in response to each input query. By accurately anticipating response lengths, the system can organize queries with similar output sizes into micro-batches, which offers several practical benefits.
Why Length-Based Grouping Matters
Grouping queries by predicted output length reduces the computational overhead that comes from padding shorter sequences to match longer ones in a batch. In typical batching, the entire batch runtime is bottlenecked by the longest sequence, so mixing queries of very different lengths leads to inefficiencies and wasted GPU cycles. Using length prediction to cluster similar-length responses allows the system to minimize padding, thus reducing redundant computations and improving throughput substantially without sacrificing response quality.
Performance Gains from Length-Aware Batching
The study employing this method reports an 86% improvement in inference throughput compared to conventional batching strategies. This gain is achieved because the length prediction-based scheduling enables more uniform workloads within each micro-batch. The result is faster batch processing, better GPU utilization, and reduced waiting times for shorter queries. This technique also helps maintain tight latency constraints, which is critical for real-time applications requiring predictable response times.
Integrating the Length Prediction Module into Dynamic Batching
In broader LLM serving environments, length prediction modules complement other dynamic batching techniques that adjust batch sizes based on GPU memory availability and SLA requirements. By incorporating length-aware grouping, systems can achieve a finer granularity of batch optimization. This modular approach allows seamless balancing between computational efficiency and latency, improving throughput and maximizing hardware utilization without requiring changes to existing inference infrastructure (arXiv:2305.13144, arXiv:2503.05248).
Together with dynamic batch sizing and token-level throttling mechanisms, length prediction-based micro-batching represents a significant advancement in scaling LLM inference, helping AI systems operate more responsively and cost-effectively in production.
Reducing Redundant Computation and Enhancing Throughput
One of the core challenges in scaling large language model (LLM) inference lies in minimizing redundant computation while maximizing throughput. Traditional static batching methods often lead to inefficiencies such as underutilized GPU memory or pipeline stalls. Recent research offers new approaches that dynamically adapt batching strategies and intelligently schedule workloads to address these problems without compromising latency.
Dynamic Batching Based on Memory and SLA Constraints
A promising method to reduce wasted computation is using dynamic batching that adjusts in real-time based on system constraints. The technique described in "Optimizing LLM Inference Throughput via Memory-aware and SLA-constrained Dynamic Batching" leverages continuous feedback from GPU memory utilization and service-level agreement (SLA) latency limits to determine optimal batch sizes. By doing so, it balances the computational load and avoids running batches that are either too small to maximize throughput or too large to violate SLA delays. This real-time adjustment results in throughput improvements ranging between 8% and 28%, while also expanding overall system capacity by 22% compared to static batch sizes. Importantly, this approach achieves these gains without requiring changes to the existing inference infrastructure, making it a practical upgrade for deployed systems (source).
Pipeline Parallelism and Token Throttling to Balance Workloads
Beyond batch size optimization, workload balancing at the pipeline level plays a significant role in preventing redundant computation caused by idle GPU stages. The "gLLM: Global Balanced Pipeline Parallelism System" proposes a solution that tackles pipeline bubbles—periods when certain GPUs wait idly due to imbalanced batch processing across the distributed model. gLLM introduces a token throttling mechanism that regulates the flow of prefill and decode tokens through the pipeline, effectively smoothing the workload distribution. This global balancing not only improves throughput substantially—by 11% to 398% in benchmarks—but also achieves lower latency compared to leading methods. Such finely tuned pipeline management ensures all GPUs remain productive, reducing wasted cycles and boosting overall efficiency (source).
Length-Aware Scheduling for Micro-Batches
Another important source of redundant computation occurs when queries with vastly different expected response lengths are grouped together. Handling these queries in the same batch can lead to some computations finishing earlier, causing delays or wasted work waiting for longer requests. The "Response Length Perception and Sequence Scheduling" approach directly addresses this by predicting the response length beforehand using an LLM-empowered length perception module. It then groups queries of similar predicted lengths into micro-batches, thereby synchronizing the processing time across batched queries. This length-aware scheduling strategy significantly cuts down on redundant processing and idle wait times, yielding an 86% improvement in throughput while preserving output quality. It exemplifies how incorporating query characteristics into scheduling enables smarter batching beyond simple size adjustments (source).
Summary
Combining these strategies—dynamic batching tuned by memory and SLA constraints, pipeline workload balancing through token throttling, and length-aware micro-batching—forms a robust framework for reducing redundant computation in real-time LLM inference systems. These techniques work together to maximize hardware utilization, minimize idle time, and maintain service quality, ultimately boosting throughput and enabling scalable, latency-sensitive AI applications.
Balancing Output Quality with Efficiency Improvements
Scaling large language model (LLM) inference involves a careful trade-off between maximizing throughput and maintaining low latency, all while preserving the quality of generated outputs. Recent research demonstrates that dynamic batching strategies and intelligent workload management can improve efficiency without compromising result fidelity.
One effective approach adapts batch sizes in real time by monitoring GPU memory usage and service-level agreement (SLA) constraints. This memory-aware dynamic batching continuously tunes throughput to available resources, achieving an 8% to 28% increase in throughput and 22% higher capacity over static batching methods—all without changing the underlying inference infrastructure (source). Crucially, this approach respects latency limits that are essential for maintaining user experience, ensuring that faster processing does not degrade response quality.
Intelligent Scheduling for Consistent Output Quality
Another dimension to balancing quality with efficiency is scheduling queries based on predicted response lengths. By integrating a length perception module, an LLM inference pipeline can group inputs into micro-batches of similar expected output sizes. This targeted grouping reduces redundant computation and idle pipeline stages, resulting in an 86% throughput improvement while preserving the quality of the generated sequences (source). This granularity of scheduling ensures that efficiency gains do not come at the cost of unpredictable or inconsistent output timing, which can otherwise affect real-time system reliability.
Workload Balancing in Distributed Systems
When inference is distributed across multiple GPUs or nodes, workload imbalance can cause pipeline stalls that degrade both throughput and output consistency. The global balanced pipeline parallelism system with token throttling addresses these issues by regulating the flow of tokens during the different phases of inference. This method mitigates pipeline bubbles—gaps caused by uneven batch processing—and achieves throughput improvements from 11% up to nearly 400% with simultaneously reduced latency (source). By smoothing the computational load, models maintain steady output generation rates, which is critical for real-time applications relying on consistent response quality.
Summary
Balancing output quality with efficiency improvements requires dynamic adjustment mechanisms sensitive to resource constraints and operational SLAs. Techniques like memory-aware dynamic batching, length-predictive scheduling, and workload balancing with token throttling have proven effective in scaling LLM inference. By combining these strategies, real-time AI systems can achieve higher throughput and lower latency without sacrificing the consistency and quality of generated responses.
Dynamic Batching for Real-Time Adaptation
Dynamic batching is a core technique for scaling large language model (LLM) inference by adjusting batch sizes on the fly according to current system conditions. Rather than relying on fixed batch sizes, dynamic batching continuously monitors GPU memory usage and service-level agreement (SLA) constraints, resizing batches to maximize throughput without exceeding latency limits. This approach improves GPU utilization and system capacity—research shows throughput gains of 8% to 28% and capacity improvements around 22% compared to static batching setups. Importantly, it achieves these benefits without requiring changes to the underlying inference infrastructure, making it practical for real-world deployment (source).
Workload Balancing via Global Pipeline Parallelism
Balancing workload across parallel computations is another crucial aspect of effective LLM inference scaling. Imbalanced batch processing often creates pipeline stalls or bubbles, degrading performance in distributed serving scenarios. The global balanced pipeline parallelism approach addresses this by evenly distributing both prefill and decode tokens through a token throttling mechanism. This method dynamically regulates the flow of tokens to maintain a well-balanced pipeline, reducing latency and boosting throughput. Reported improvements range from 11% up to 398% over other state-of-the-art techniques, demonstrating significant gains in handling distributed LLM workloads efficiently (source).
Length-Aware Scheduling for Micro-Batch Grouping
A complementary technique focuses on scheduling sequences by their expected response length. By incorporating a length perception module that predicts how long model outputs will be, inference pipelines can group queries with similar lengths together into micro-batches. This reduces the overhead caused by padding and redundant computations, which commonly occur when mixing short and long sequences. The effect is a dramatic 86% throughput improvement while preserving output quality, enabling faster and more efficient real-time inference (source).
Bringing It All Together
Together, these techniques—dynamic batching, workload balancing via global pipeline parallelism, and length-aware scheduling—form a cohesive strategy for scaling LLM inference. They address different but complementary bottlenecks: resource utilization, computational pipeline efficiency, and sequence processing overhead. By intelligently adjusting batch sizes, balancing workloads across distributed systems, and grouping sequences by length, real-time AI systems can achieve both higher throughput and lower latency, meeting demanding SLA requirements in practical deployments.
Overall Impact on Real-Time AI System Performance and SLA Compliance
Scaling large language model (LLM) inference in real-time AI systems involves a complex trade-off between maximizing throughput and minimizing latency to meet strict service-level agreements (SLAs). Dynamic batch sizing methods have emerged as a powerful solution to this challenge by adapting batch sizes on the fly based on current system conditions.
One major benefit of dynamic batching is its ability to increase GPU utilization efficiency without violating latency constraints. The approach described in "Optimizing LLM Inference Throughput via Memory-aware and SLA-constrained Dynamic Batching" demonstrates that real-time adjustment of batch sizes depending on GPU memory usage and SLA thresholds yields throughput improvements ranging from 8% to 28%. At the same time, system capacity—the number of concurrent requests handled—can increase by about 22%, all while maintaining latency boundaries required by SLAs (source). This balance translates to higher overall system performance under varying workloads and resource availability.
Workload Balancing and Latency Reduction
Beyond adjusting batch sizes locally, global workload balancing strategies further enhance throughput and latency. The "gLLM: Global Balanced Pipeline Parallelism System" introduces a token throttling mechanism that regulates the flow of prefill and decode tokens during distributed LLM serving. This approach prevents pipeline stalls or "bubbles" caused by uneven batch computations across parallel units. The result is a throughput boost between 11% and 398%, accompanied by lower latency compared to previous state-of-the-art methods (source). This improvement underlines the importance of holistic pipeline management alongside dynamic batching to ensure steady output rates and SLA compliance.
Intelligent Scheduling via Length Perception
A complementary technique addressing inference efficiency focuses on response length prediction. The "Response Length Perception and Sequence Scheduling" study implements a length perception module to group incoming queries into micro-batches with similar expected response lengths. This reduces wasted computation on overly long sequences processed alongside shorter ones, enabling an 86% gain in throughput while maintaining output quality and meeting latency requirements (source). Integrating response length awareness into batching decisions enhances predictability in latency, thereby supporting tighter SLA adherence.
Summary
Together, these dynamic batching and scheduling innovations improve real-time AI system performance by intelligently balancing throughput and latency. By incorporating memory-aware batch adjustment, global workload balancing, and length-aware scheduling, systems can achieve higher utilization, reduce bottlenecks, and maintain SLA compliance without requiring major changes to existing inference infrastructure. This results in more scalable, efficient, and reliable deployment of LLMs in production environments.
Dynamic Batching and Real-Time Adaptation
A promising direction for scaling large language model (LLM) inference lies in dynamic batching techniques that adjust batch sizes in real-time based on system constraints. One innovative approach focuses on continuously monitoring GPU memory usage and service-level agreement (SLA) requirements to determine the ideal batch size on the fly. This method can significantly boost throughput—by 8% to 28%—while increasing system capacity by over 20% compared to fixed batch sizes. The key advantage is that it improves computational efficiency without requiring changes to existing inference architecture, making it easier to deploy in production environments (source).
Pipeline Parallelism and Token Throttling
Beyond dynamic batching, distributed LLM serving can benefit strongly from balancing workloads across pipeline stages. A novel system uses global balanced pipeline parallelism to smooth out inefficiencies caused by imbalanced batch computations, commonly seen as pipeline bubbles that throttle throughput. By regulating the flow of tokens during both prefill (context loading) and decode steps, this token throttling mechanism achieves much higher throughput—ranging from 11% to nearly 400% improvements—while reducing latency compared to current state-of-the-art solutions. These gains highlight the importance of fine-grained workload management in large-scale LLM deployments (source).
Length-Aware Scheduling for Micro-Batching
Another exciting technique addresses variation in response length, which often leads to inefficiencies in batch processing. By incorporating an LLM-powered length perception module, systems can predict expected response lengths and form micro-batches of queries with similar output sizes. This approach significantly cuts down redundant computations and leads to an 86% increase in overall throughput without compromising response quality. Such length-aware scheduling remedies mismatches in batch execution times, enabling more optimized use of resources under real-time constraints (source).
Looking Ahead
Future advancements in LLM inference scaling will likely combine these dynamic and intelligent strategies—real-time batch size adjustments, balanced pipeline token management, and response length-aware scheduling—to optimally balance throughput and latency. Integrating these methods within adaptive control frameworks and automating SLA-driven decision-making will be critical to supporting diverse production workloads at scale while maintaining responsiveness. Continued innovation in this space promises to make LLM-powered applications more efficient and capable of handling increasing demand in real-time AI systems.