{"id":6981,"date":"2025-10-30T20:36:01","date_gmt":"2025-10-30T20:36:01","guid":{"rendered":"https:\/\/uplatz.com\/blog\/?p=6981"},"modified":"2025-11-06T16:01:55","modified_gmt":"2025-11-06T16:01:55","slug":"a-system-level-analysis-of-continuous-batching-for-high-throughput-large-language-model-inference","status":"publish","type":"post","link":"https:\/\/uplatz.com\/blog\/a-system-level-analysis-of-continuous-batching-for-high-throughput-large-language-model-inference\/","title":{"rendered":"A System-Level Analysis of Continuous Batching for High-Throughput Large Language Model (LLM) Inference"},"content":{"rendered":"

The Throughput Imperative in LLM Serving<\/b><\/h2>\n

The deployment of Large Language Models (LLMs) in production environments has shifted the primary engineering challenge from model training to efficient, scalable inference. While these models possess unprecedented capabilities, their sheer size and unique computational patterns present formidable obstacles to achieving the high throughput and low latency required by real-time applications. At the heart of this challenge lies a fundamental mismatch between the autoregressive nature of LLM inference and the massively parallel architecture of the Graphics Processing Units (GPUs) on which they run. This section establishes the system-level context for this problem, detailing the architectural bottlenecks that render naive inference strategies inefficient and introducing batching as the foundational optimization that paves the way for more advanced techniques.<\/span><\/p>\n

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bundle-combo—sap-ewm-ecc-and-s4hana By Uplatz<\/a><\/h3>\n

The Memory-Bound Nature of Autoregressive Inference<\/b><\/h3>\n

Despite the immense computational power of modern GPUs, capable of performing trillions of floating-point operations per second (FLOPs), LLM inference workloads often fail to fully utilize this capacity.<\/span>1<\/span> The primary reason for this inefficiency is that LLM inference is fundamentally <\/span>memory-bound, not compute-bound<\/b>.<\/span>1<\/span> The core bottleneck is the time required to load the model’s vast parameters\u2014often numbering in the tens or hundreds of billions\u2014from the GPU’s high-bandwidth memory (HBM) into the on-chip SRAM of the streaming multiprocessors (SMs) where computation occurs.<\/span>1<\/span><\/p>\n

This memory transfer latency significantly dominates the actual time spent on mathematical computations, particularly during the iterative token generation phase of inference.<\/span>3<\/span> A typical LLM forward pass involves loading gigabytes of weight data to process a comparatively small amount of activation data. Consequently, the powerful arithmetic units of the GPU spend a significant portion of their time idle, waiting for data to arrive from HBM.<\/span>1<\/span> This chronic underutilization of expensive hardware resources leads directly to suboptimal performance, low throughput, and poor cost-efficiency, creating a critical need for system-level optimizations that can better saturate the GPU’s computational capabilities.<\/span><\/p>\n

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The Dichotomy of LLM Inference Phases: Prefill and Decode<\/b><\/h3>\n

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The challenge of GPU utilization is further compounded by the fact that LLM inference is not a monolithic computational process. It is comprised of two distinct phases with starkly different performance profiles, creating a complex scheduling problem for any serving system.<\/span>6<\/span><\/p>\n

The <\/span>Prefill Phase<\/b>, also known as the prompt processing or initiation phase, is the initial step where the model processes the entire input prompt simultaneously.<\/span>6<\/span> This phase is characterized by large matrix-matrix multiplications (GEMM operations) that can effectively parallelize across the input sequence. As a result, the prefill phase is generally <\/span>compute-bound<\/b>, capable of saturating the GPU’s computational resources, especially for long prompts where the attention mechanism’s computational complexity scales quadratically with the sequence length.<\/span>5<\/span><\/p>\n

The <\/span>Decode Phase<\/b>, in contrast, is the iterative, autoregressive process of generating the output sequence one token at a time.<\/span>6<\/span> Each decoding step involves a forward pass to predict the next token, which is then appended to the input for the subsequent step. Computationally, each step is equivalent to a matrix-vector operation, which is too small to fully leverage the GPU’s massively parallel architecture.<\/span>5<\/span> The dominant operation in the decode phase is reading the entire, ever-growing Key-Value (KV) cache from HBM. The KV cache stores the attention keys and values for all previously processed tokens, and it must be accessed at every step. This makes the decode phase quintessentially <\/span>memory-bound<\/b>.<\/span>5<\/span><\/p>\n

This prefill\/decode dichotomy is the root cause of many advanced scheduling challenges. A simple batching strategy that treats all requests identically will inevitably be inefficient because it fails to account for these two different performance profiles. A long, compute-intensive prefill operation can stall the generation of single tokens for many other users, re-introducing a form of system-level inefficiency even within advanced batching frameworks.<\/span>8<\/span> Therefore, the core systems problem is not just batching requests, but intelligently scheduling the distinct <\/span>sub-computations<\/span><\/i> (prefill vs. decode) of those requests. This realization drives the architectural differences between various state-of-the-art inference frameworks.<\/span><\/p>\n

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Batching as a Foundational Optimization<\/b><\/h3>\n

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To counteract the severe underutilization of GPUs caused by the memory-bound nature of single-sequence inference, the most fundamental optimization is <\/span>batching<\/b>. Instead of processing requests sequentially, a serving system can group multiple requests together and process them as a single batch.<\/span>1<\/span> This approach allows the system to load the model weights from HBM once per layer and then apply them to many different input sequences simultaneously.<\/span>4<\/span><\/p>\n

By transforming the computation from a series of small matrix-vector operations into a single, large batch matrix-matrix multiplication, batching dramatically increases arithmetic intensity. This better utilizes the GPU’s parallel architecture, effectively amortizing the high cost of memory transfers across multiple requests.<\/span>2<\/span> The result is a substantial improvement in aggregate throughput, measured in tokens per second, and a more cost-effective use of hardware.<\/span>1<\/span> This principle of amortizing memory access costs through parallel computation is the foundational concept that motivates the development of all sophisticated batching strategies.<\/span><\/p>\n

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A Taxonomy of Batching Strategies<\/b><\/h2>\n

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The evolution of batching techniques for LLM inference reflects a progressively deeper understanding of the workload’s unique characteristics. The journey from simple, rigid methods to highly dynamic, fine-grained scheduling illustrates a clear progression in system design, aimed at systematically eliminating sources of inefficiency. This section provides a taxonomy of these strategies, detailing their mechanisms, inherent limitations, and the logical evolution that led to the development of continuous batching.<\/span><\/p>\n

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Static Batching: The Inflexible Foundation<\/b><\/h3>\n

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Static batching is the most straightforward implementation of the batching principle.<\/span>2<\/span> In this approach, the inference server waits until a <\/span>fixed number<\/b> of requests, corresponding to a predetermined batch size, has arrived. Only then does it group these requests into a single tensor, process them simultaneously, and wait for every single request in the batch to complete its full generation before returning the results and proceeding to the next batch.<\/span>2<\/span><\/p>\n

While simple to implement, this request-level, atomic batching model introduces a critical and performance-killing flaw known as <\/span>Head-of-Line (HOL) blocking<\/b>.<\/span>6<\/span> In any realistic production scenario, incoming requests will have varying prompt lengths and will require generating output sequences of different lengths. Because the entire batch is treated as an indivisible unit of work, its completion time is dictated by the single longest-running request. Consequently, GPU resources that were allocated to sequences that finish early\u2014for example, a simple question-answering request batched with a long document summarization task\u2014sit idle, waiting for the straggler to complete. This results in significant wastage of compute time and memory, often visualized as “white blocks” of underutilization in timelines of GPU activity.<\/span>1<\/span> The phenomenon is a classic performance pathology in computer networking and operating systems, where a single slow packet or process can hold up an entire queue.<\/span>13<\/span><\/p>\n

Furthermore, to form a single, uniform tensor that can be processed by the GPU, all sequences in a static batch must be padded with special tokens to match the length of the longest sequence in the batch. This forces the GPU to perform a substantial amount of useless computation on these padding tokens, wasting both compute cycles and valuable memory.<\/span>6<\/span><\/p>\n

Despite these significant drawbacks, static batching remains the optimal choice for a specific niche: <\/span>offline, predictable workloads<\/b> where latency is not a primary concern and requests are largely homogeneous. Examples include nightly jobs for bulk document processing or large-scale data analysis.<\/span>15<\/span> In such controlled environments, the variance in generation lengths is minimal, which naturally mitigates the impact of HOL blocking. Here, the low scheduling overhead of the static approach can lead to higher peak throughput compared to more complex dynamic methods.<\/span>17<\/span><\/p>\n

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Dynamic Batching: A Reactive Improvement<\/b><\/h3>\n

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Dynamic batching represents a reactive enhancement to the static model, designed to improve responsiveness in environments with variable traffic.<\/span>2<\/span> The core mechanism introduces a time-based trigger in addition to the size-based one. The server collects incoming requests and forms a batch either when the maximum configured batch size is reached or when a <\/span>pre-defined time window<\/b> expires, whichever occurs first.<\/span>2<\/span><\/p>\n

The primary advantage of this approach is that it improves the latency for individual requests, particularly during periods of low traffic. It ensures that the first few requests in a potential batch are not forced to wait indefinitely for the batch to fill up, striking a better balance between latency and throughput.<\/span>2<\/span> It effectively solves the problem of <\/span>when<\/span><\/i> to start processing a batch.<\/span><\/p>\n

However, the fundamental flaw of request-level atomicity persists. Dynamic batching still operates at the granularity of an entire request. Once a batch is formed and dispatched to the GPU, it is immutable and suffers from the exact same HOL blocking and padding inefficiencies as static batching.<\/span>2<\/span> It improves the batch formation process but does nothing to address the profound underutilization <\/span>within<\/span><\/i> the batch.<\/span><\/p>\n

The progression from static to dynamic and ultimately to continuous batching reflects a fundamental shift in system design philosophy. Static batching is <\/span>resource-centric<\/b>; its primary goal is to create a full batch to maximize the GPU’s utilization for a single compute kernel, largely ignoring the individual characteristics of the requests. Dynamic batching is <\/span>request-centric<\/b>; it introduces a time-out to improve the latency for the <\/span>first request<\/span><\/i> in a batch, acknowledging that request-level metrics matter and prioritizing getting a request started over waiting for perfect resource utilization. Continuous batching, as the next section will detail, is <\/span>work-centric<\/b>. It decomposes requests into their smallest unit of work\u2014a single token generation\u2014and schedules this work dynamically. This fine-grained, work-centric view is what allows it to achieve maximum resource utilization without penalizing individual requests, representing a more sophisticated and efficient paradigm.<\/span><\/p>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n
Feature<\/b><\/td>\nStatic Batching<\/b><\/td>\nDynamic Batching<\/b><\/td>\nContinuous Batching<\/b><\/td>\n<\/tr>\n
Batch Formation Trigger<\/b><\/td>\nFixed number of requests arrive <\/span>2<\/span><\/td>\nFixed number of requests OR time window expires <\/span>2<\/span><\/td>\nContinuous admission as resources free up <\/span>18<\/span><\/td>\n<\/tr>\n
Scheduling Granularity<\/b><\/td>\nRequest-level (entire sequence) <\/span>14<\/span><\/td>\nRequest-level (entire sequence) <\/span>2<\/span><\/td>\nIteration-level (single token step) <\/span>14<\/span><\/td>\n<\/tr>\n
Batch Composition<\/b><\/td>\nFixed and immutable once launched <\/span>14<\/span><\/td>\nFixed and immutable once launched <\/span>2<\/span><\/td>\nDynamic; changes at every iteration <\/span>14<\/span><\/td>\n<\/tr>\n
Head-of-Line Blocking<\/b><\/td>\nSevere; batch waits for the longest request <\/span>6<\/span><\/td>\nSevere; batch waits for the longest request <\/span>2<\/span><\/td>\nEliminated; requests finish and exit independently <\/span>6<\/span><\/td>\n<\/tr>\n
GPU Utilization<\/b><\/td>\nLow and variable (“sawtooth” pattern) due to idle time <\/span>1<\/span><\/td>\nLow and variable; similar to static once batch starts <\/span>2<\/span><\/td>\nConsistently high; freed resources are immediately backfilled <\/span>14<\/span><\/td>\n<\/tr>\n
Padding Overhead<\/b><\/td>\nHigh; all sequences padded to the longest in the batch <\/span>14<\/span><\/td>\nHigh; all sequences padded to the longest in the batch <\/span>2<\/span><\/td>\nEliminated; no padding across sequences is required <\/span>14<\/span><\/td>\n<\/tr>\n
Ideal Use Case<\/b><\/td>\nOffline, bulk processing with homogeneous requests <\/span>16<\/span><\/td>\nGeneral-purpose, balancing latency and throughput, but suboptimal for LLMs <\/span>2<\/span><\/td>\nOnline, interactive applications with heterogeneous requests (e.g., chatbots) <\/span>16<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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The Core Mechanism of Continuous Batching: Iteration-Level Scheduling<\/b><\/h2>\n

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Continuous batching represents a paradigm shift in how inference requests are scheduled and executed. It moves away from the coarse-grained, request-level batching of its predecessors to a fine-grained, dynamic approach that maximizes hardware utilization by fundamentally rethinking the unit of schedulable work. This section provides a deep, algorithmic breakdown of its core mechanism, iteration-level scheduling, and explains precisely how this innovation solves the long-standing problems of head-of-line blocking and padding.<\/span><\/p>\n

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The Paradigm Shift: From Request-Level to Iteration-Level Granularity<\/b><\/h3>\n

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The central innovation of continuous batching, first formally proposed and analyzed in the Orca paper from OSDI ’22, is to change the scheduling quantum from an entire request to a single autoregressive step, referred to as an “iteration”.<\/span>1<\/span> In this model, the system no longer conceives of its workload as discrete “batches of requests” that must be processed atomically. Instead, it manages a continuous, dynamic pool of active requests, and the fundamental unit of work is the generation of the next token for every request in that pool.<\/span><\/p>\n

The “batch” in a continuous batching system is therefore an ephemeral concept. It is simply the set of active sequences being processed at a given iteration, a composition that can\u2014and frequently does\u2014change at every single step.<\/span>14<\/span> This decouples the lifecycle of an individual request from the lifecycle of any other request in the system, eliminating the artificial synchronization barriers that plague static and dynamic batching.<\/span><\/p>\n

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The Continuous Batching Algorithm: A Step-by-Step Walkthrough<\/b><\/h3>\n

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The operational flow of a continuous batching system is a tight, continuous loop managed by a central scheduler. The process can be broken down into the following logical steps:<\/span><\/p>\n

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  1. Request Arrival:<\/b> New inference requests arrive asynchronously at the server. Instead of being held to form a static batch, they are immediately placed into a waiting_queue.<\/span>14<\/span><\/li>\n
  2. Iteration Forward Pass:<\/b> At each time step, the scheduler takes all sequences currently in the active_batch and executes a single forward pass on the GPU. This single pass performs one decoding step for every sequence in the active_batch, generating exactly one new token for each of them in parallel.<\/span>6<\/span><\/li>\n
  3. Completion Check:<\/b> After the forward pass is complete, the system inspects the newly generated tokens for each sequence. If a token is a designated end-of-sequence (EOS) token, or if a sequence has reached its user-defined maximum length, that sequence is marked as completed.<\/span>18<\/span><\/li>\n
  4. Immediate Eviction:<\/b> All sequences that were marked as completed are immediately removed from the active_batch. This is a critical step: their associated resources, most importantly the GPU memory allocated for their KV cache, are instantly freed and returned to the system’s global memory pool.<\/span>6<\/span><\/li>\n
  5. Admission of New Requests:<\/b> The scheduler now assesses the available system capacity, considering both GPU memory and any configured concurrency limits (e.g., maximum number of batched tokens). If capacity is available, it pulls new requests from the head of the waiting_queue and adds them to the active_batch.<\/span>1<\/span> These newly admitted requests will have their prefill phase executed as part of the next iteration’s forward pass, alongside the decode steps for the other requests already in the batch.<\/span><\/li>\n
  6. Loop:<\/b> The process immediately repeats from step 2. The active_batch is in a state of constant flux, with sequences joining and leaving at every iteration. There are no artificial synchronization points; the system is always performing useful work as long as there are requests to be processed.<\/span>14<\/span><\/li>\n<\/ol>\n

    This continuous cycle transforms the GPU utilization profile. Static batching exhibits a “sawtooth” pattern: utilization spikes to 100% when a full batch is running, then gradually declines as shorter requests finish and their allocated slots go idle. Utilization then drops to zero while the system waits for the last straggler to complete and for a new batch to assemble.<\/span>14<\/span> Continuous batching smooths this into a consistently high plateau. The “time between batches” is eliminated because the process is continuous, and the “decline as requests finish” is eliminated because freed resources are immediately backfilled with new work.<\/span>2<\/span> This sustained high utilization is the direct source of the dramatic throughput gains observed in these systems.<\/span><\/p>\n

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    How Iteration-Level Scheduling Solves HOL Blocking and Padding<\/b><\/h3>\n

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    The algorithmic design of iteration-level scheduling directly addresses the core inefficiencies of previous methods:<\/span><\/p>\n