Adaptive LLM Inference Pipelines How Dynamic Batching and Model Switching Reduce Latency At Scale
Struggling with slow LLM inference? Discover adaptive pipelines that smartly handle varied workloads, boosting speed and saving GPU power for real-time AI applications!
Introduction to Adaptive LLM Inference Pipelines
As large language models (LLMs) grow in size and complexity, delivering real-time inference at scale while maintaining low latency has become a significant challenge. Traditional static batching methods often struggle to efficiently handle variable token lengths and uneven computation loads, which leads to wasted GPU resources and increased response times. Adaptive LLM inference pipelines have emerged as a solution by dynamically optimizing how workloads are managed across distributed hardware.
One core technique is dynamic batching, where the system continuously refills batches with new requests as soon as previous ones finish, rather than waiting for all sequences to complete simultaneously. This approach, also known as continuous or iteration-level scheduling batching, maximizes GPU utilization and reduces idle time dramatically. Implementations have demonstrated throughput improvements up to 23 times compared to static batching, significantly cutting down latency in serving LLMs (source).
Beyond batching, adaptive pipelines incorporate model switching and intelligent scheduling to address imbalances caused by the varying difficulty of token generation steps. For example, gLLM leverages a globally balanced pipeline parallelism strategy with Token Throttling, which dynamically manages the flow of prefill and decode tokens across distributed nodes. This reduces pipeline bubbles—periods where some nodes are idle due to imbalance in computation—leading to nearly four times higher throughput and lower latency (source).
Similarly, Apt-Serve enhances scalability by combining two types of caches—a memory-heavy KV cache and a memory-efficient hidden state cache—resulting in an hybrid cache system. This adaptive request scheduling enables larger batch sizes and greater concurrency, improving effective throughput by up to 8.8 times through better batch composition and memory optimization (source).
Collectively, these adaptive inference pipelines tackle fundamental inefficiencies in large-scale LLM deployment. By dynamically balancing workloads, optimizing batch scheduling, and managing memory usage, they enable more cost-effective and low-latency inference serving, supporting the demands of modern applications.
Challenges in Scaling LLM Inference
Scaling large language model (LLM) inference presents several significant challenges that arise from the complexity of distributing computations efficiently while minimizing latency. One major hurdle is the imbalance in computation times during the generation of tokens. Since different parts of the model pipeline may take varying amounts of time to process tokens, static or naïve pipeline parallelism often leads to pipeline bubbles—idle periods in some nodes waiting for others to finish processing. This inefficiency constrains throughput and increases latency. The gLLM system tackles this issue by introducing a globally balanced pipeline parallelism approach with Token Throttling. This dynamic management of prefill and decode tokens across distributed nodes significantly reduces bubbles and improves throughput by nearly 4 times compared to baseline methods (source).
Another pressing issue is memory management, especially related to caching intermediate results during inference. Large KV (key-value) caches are memory-intensive but vital for reusing computations, while smaller hidden caches offer efficiency but limited capacity. Apt-Serve addresses this by employing a hybrid cache that adaptively switches between these cache types based on workload demands. This approach enables larger batch sizes and improves request concurrency, which ultimately boosts effective throughput by almost 9 times through optimized batch composition. Such adaptive caching mechanisms are critical for balancing memory usage without sacrificing performance (source).
Finally, scheduling rigidity in conventional batching approaches creates idle GPU time and limits scalability. Static batching waits for all sequences in a batch to complete before starting the next, leading to wasted resources when sequences have varying lengths. Continuous batching offers a solution by dynamically replacing finished sequences with new ones on a per-iteration basis. This dynamic, iteration-level scheduling drastically enhances GPU utilization, allowing throughput improvements up to 23 times and reducing latency considerably. This technique highlights the value of flexible, demand-driven batching in effectively scaling LLM inference workloads (source).
Together, these challenges—imbalanced compute loads, constrained memory usage, and rigid scheduling—underscore why scaling LLM inference is nontrivial. Adaptive strategies that balance pipeline execution dynamically, optimize caching tailored to workload characteristics, and support continuous, flexible batching are essential for enabling cost-effective, low-latency LLM deployment at scale.
Overview of Techniques to Reduce Latency and Improve Throughput
In large language model (LLM) inference pipelines, latency and throughput are critical metrics that often conflict due to the computational and memory demands of generating variable-length token sequences. Recent advances focus on dynamic approaches designed to overcome these challenges by optimizing how workload is balanced and scheduled across hardware resources.
One effective technique is dynamic batching, also known as continuous or iteration-level scheduling batching. Instead of waiting for all sequences in a batch to complete before starting the next, continuous batching replaces finished sequences immediately with new ones. This minimizes idle GPU time and significantly boosts utilization. Studies have demonstrated throughput improvements of up to 23 times, alongside substantial latency reductions, by decreasing the idle waiting periods inherent in traditional static batching (source).
In addition to batching, adaptive scheduling strategies manage token processing more granularly to address pipeline inefficiencies. For example, gLLM introduces a globally balanced pipeline parallelism approach leveraging Token Throttling to dynamically control the flow of tokens. By adjusting prefill and decode token handling across distributed nodes, gLLM reduces stall times (pipeline bubbles) caused by uneven computation loads, enabling throughput gains approaching 400% and lower latency (source).
Memory management is another bottleneck for scaling LLM inference. Apt-Serve proposes an adaptive request scheduler combined with a hybrid cache architecture that integrates a dense key-value memory cache and a lightweight hidden state cache. This design allows for increased batch sizes and promotes higher concurrency by tailoring memory use to the demands of incoming requests, yielding nearly nine times improvements in effective throughput (source).
Together, dynamic batching, adaptive token scheduling, and memory-efficient caching form a complementary set of techniques that address the core inefficiencies of LLM serving. By breaking from static, rigid scheduling and optimizing resource use in real time, these methods enable scalable, low-latency model inference deployments that are more cost-efficient and performance-effective at scale.
gLLM: Globally Balanced Pipeline Parallelism
A core challenge in large language model (LLM) inference at scale is balancing the computation load across distributed pipeline stages. Variations in token processing times can cause pipeline bubbles—idle slots that reduce overall throughput and increase latency. The gLLM system tackles this issue by introducing a globally balanced pipeline parallelism method centered on a technique called Token Throttling.
Unlike traditional pipeline parallelism that often suffers from imbalances due to uneven token generation rates across distributed nodes, gLLM dynamically manages the flow of both prefill and decode tokens through the entire pipeline. By carefully throttling tokens, it avoids stalls and keeps the pipeline stages continuously busy. This approach adapts token dispatching based on real-time processing speeds, ensuring that no stage is waiting idly for work while others are overloaded.
The result is a dramatic improvement in efficiency—reported gains include up to 398% higher throughput and significantly reduced latency compared to previous methods. This is achieved by smoothing out the computational load across the pipeline, preventing the bottlenecks created by tokens that take longer to process or uneven batch sizes (source).
gLLM’s strategy exemplifies how dynamic, fine-grained control of workload distribution can unlock better utilization of hardware resources in LLM serving. It complements other adaptive techniques like dynamic batching and hybrid caching by attacking the problem from the pipeline scheduling perspective, illustrating the multifaceted nature of scalability challenges in deploying large models efficiently.
Token Throttling to Manage Pipeline Bubbles
In large-scale LLM inference pipelines, uneven computation times across pipeline stages can create inefficiencies known as pipeline bubbles—periods when some compute resources are idle while others are still processing. Token throttling emerges as a practical technique to address these imbalances by regulating the flow of tokens through the pipeline, preventing some nodes from running ahead too far and causing stalls downstream.
The framework gLLM offers a clear example of token throttling in action. It implements a globally balanced pipeline parallelism system that dynamically controls token issuance—both prefill and decode tokens—across distributed nodes. By doing so, it effectively reduces pipeline bubbles caused by imbalanced computation delays at various stages. This helps smooth out the workload and leads to substantial throughput improvements, with reported gains of up to 398% and concurrent latency reduction. The key lies in adjusting token dispatch rates so that no single node is overwhelmed or underutilized, which maximizes the GPU resource utilization throughout the entire inference pipeline (source).
Beyond just improving throughput, token throttling also supports more efficient scaling. Techniques like Apt-Serve build on adaptive scheduling principles to optimize batch composition in hybrid caching environments. They can coordinate larger and more concurrent batches by managing how tokens and requests are fed into the system, aligning well with token throttling’s goal of balancing load dynamically. This synergy enables up to 8.8 times improvement in effective throughput by minimizing idle GPU periods and cache thrashing (source).
A complementary method related to token throttling is continuous batching, which replaces finished sequences in the batch immediately with new ones rather than waiting for the entire batch to complete. While continuous batching focuses on iteration-level scheduling, token throttling manages the pace of token generation at a finer granularity within the pipeline. Together, these techniques tackle different facets of latency and throughput issues in LLM inference by dynamically adapting to workload variability (source).
In summary, token throttling provides a dynamic feedback control mechanism in LLM inference pipelines that smooths computational imbalances, reduces idle GPU time, and ultimately boosts both throughput and latency performance. Its role is central in the adaptive strategies that make large-scale, low-latency LLM inference cost-effective and scalable.
Dynamic Management of Prefill and Decode Tokens Across Distributed Nodes
One of the core challenges in scaling large language model (LLM) inference pipelines is handling variable and asynchronous token generation workloads across distributed computing nodes. The process is split broadly into two phases: prefill, where initial tokens are generated to prime the model, and decode, where subsequent tokens are produced autoregressively. Efficiently managing these tokens across nodes is critical because imbalances in computation time during these phases can lead to pipeline stalls, wasting precious GPU cycles and increasing overall latency.
Recent advancements tackle this by introducing dynamic token management strategies that adaptively redistribute computation loads in real time. For example, gLLM’s approach leverages a globally balanced pipeline parallelism framework coupled with Token Throttling. This technique monitors token-level workloads and dynamically adjusts how prefill and decode tokens are handled on each node, preventing situations where one node becomes a bottleneck while others idle. By actively balancing these token streams, gLLM reduces pipeline bubbles—periods when stages wait for slower nodes—thereby achieving up to a 398% increase in throughput and lower latency across multi-node setups (source).
Complementing this, Apt-Serve introduces a hybrid cache system combining a memory-intensive key-value (KV) cache with a more memory-efficient hidden state cache. This design supports larger batch sizes and higher request concurrency by optimizing how token states are stored and accessed during decoding. Adaptive request scheduling on this hybrid cache further improves resource utilization by aligning token processing loads, which enables more efficient batching and throughput improvements of up to 8.8 times (source).
Another layer of optimization is continuous batching, or iteration-level scheduling batching, which immediately replaces completed token sequences within a batch with new ones instead of waiting for the entire batch to finish. This method is particularly effective when token generation lengths vary across requests. By dynamically maintaining a fully utilized batch throughout the decoding phase, continuous batching significantly reduces idle GPU time, yielding throughput gains of up to 23 times as well as substantial latency reductions (source).
Together, these strategies demonstrate that dynamic and fine-grained management of prefill and decode tokens across distributed nodes is essential for unlocking efficient, low-latency LLM inference at scale. By adapting to workload variability and memory constraints in real time, they allow inference pipelines to better utilize hardware resources, minimize wasted cycles, and improve user-perceived responsiveness.
Performance Gains Achieved by gLLM
gLLM demonstrates significant performance improvements in large language model (LLM) inference by introducing a globally balanced pipeline parallelism system that directly addresses uneven computation delays across distributed nodes. Traditional pipeline parallelism often suffers from idle times, or pipeline bubbles, as different stages complete their tasks at varying speeds. gLLM tackles this by incorporating Token Throttling, which dynamically manages the flow of prefill and decode tokens throughout the pipeline. This approach effectively balances the workload, reducing idle periods and increasing overall efficiency.
The practical impact of these optimizations is substantial. gLLM achieves up to a 398% increase in throughput compared to conventional pipeline parallelism techniques. Alongside throughput gains, the dynamic token management reduces latency, making LLM inference significantly faster and more scalable. This improvement is particularly important when serving models at scale, where the unpredictable length of token sequences can otherwise cause inefficiencies (source).
Beyond throughput, gLLM’s balanced pipeline approach means that hardware resources are better utilized, leading to more consistent performance and potentially lower operational costs. By adapting dynamically to actual token processing demands rather than relying on static scheduling, gLLM provides a more responsive and efficient inference pipeline. This kind of adaptive system is crucial given the growing model sizes and diverse user request patterns in real-world applications.
Apt-Serve: Adaptive Request Scheduling for LLM Serving
One of the critical challenges in serving large language models (LLMs) at scale is managing memory constraints while maximizing throughput. Apt-Serve tackles this by implementing an adaptive request scheduling approach that leverages a hybrid caching system. This design combines a memory-intensive key-value (KV) cache with a more memory-efficient hidden cache, allowing the system to handle larger batch sizes and more concurrent requests without exceeding hardware limits.
The core advantage of Apt-Serve lies in its intelligent batch composition strategy. Instead of rigidly grouping requests, it dynamically schedules them based on cache availability and memory usage, optimizing how batches are formed. This approach not only improves GPU utilization but also adapts to the variability in token generation lengths typical of LLM workloads. By doing so, Apt-Serve significantly reduces the bottlenecks that come from cache misses or imbalanced memory demands.
Empirical results show that this adaptive scheduling framework can boost effective throughput by up to 8.8 times compared to conventional static batching methods. This substantial improvement comes from more efficient handling of concurrent requests and better memory management, enabling the service to scale more gracefully under heavy load without sacrificing latency.
In summary, Apt-Serve demonstrates how combining hybrid caching with adaptive scheduling can address key inefficiencies in LLM serving. By tailoring batch composition dynamically, it unlocks higher throughput and lower latency, paving the way for more responsive and cost-effective deployment of large-scale language models (source).
Hybrid Cache Design: Combining KV Cache and Hidden Cache
One of the critical challenges in scaling large language model (LLM) inference pipelines is managing memory efficiently while maintaining high throughput. Apt-Serve addresses this by introducing a hybrid cache design that leverages two complementary caching mechanisms: a key-value (KV) cache and a hidden cache. This hybrid approach strikes a balance between memory use and computational speed, enabling more effective handling of larger batch sizes and higher concurrency.
The KV cache is memory-intensive but fast to access, storing the explicit key and value pairs generated at each decoding step. This cache provides quick retrieval of token embeddings, which is crucial for decoding efficiency. However, its high memory consumption often limits batch size and concurrency, especially when serving large-scale models.
To complement the KV cache, Apt-Serve integrates a hidden cache that stores intermediate hidden states in a more memory-efficient format. While accessing this cache is somewhat slower compared to the KV cache, it consumes significantly less memory. By smartly combining these two caches, the system can offload some memory pressure from the KV cache, freeing up resources to support larger batches and more simultaneous requests.
This hybrid cache setup allows adaptive request scheduling to optimize batch composition dynamically. The system can decide which cache to serve requests from based on workload characteristics and memory availability, improving both latency and throughput without sacrificing responsiveness. As a result, Apt-Serve achieves up to 8.8 times improvement in effective throughput, showing how hybrid caches can enable scalable, cost-effective LLM inference pipelines (source).
By integrating the memory-heavy KV cache with the lightweight hidden cache, LLM serving systems can better address the trade-offs between memory usage and speed. This hybrid cache design is a practical strategy to overcome bottlenecks in real-world deployments, facilitating adaptive batching and higher concurrency in inference pipelines.
Optimizing Batch Composition for Larger Batch Sizes and Concurrency
One of the core challenges in scaling large language model (LLM) inference lies in optimizing how batches are composed, especially as batch sizes grow and concurrency increases. Traditional static batching methods often lead to inefficiencies since the system waits for all inputs in a batch to complete before processing more, causing underutilization of GPU resources. This is where adaptive batching strategies such as continuous batching, dynamic batching, and intelligent request scheduling come into play.
Continuous batching, sometimes called iteration-level scheduling batching, significantly improves GPU utilization by immediately replacing finished sequences in a batch with new ones rather than waiting for the entire batch to complete. This means GPUs remain busy almost constantly, helping achieve throughput improvements as high as 23 times compared to static batching approaches. It also reduces latency by cutting down on idle waiting times between sequence generations (Anyscale).
Further refinements come from systems like Apt-Serve, which introduce adaptive request scheduling that leverages a hybrid cache composed of a large, memory-intensive key-value (KV) cache and a smaller, memory-efficient hidden state cache. This structure allows Apt-Serve to manage batch composition dynamically, effectively supporting larger batch sizes and higher concurrency. By intelligently selecting which requests to group based on their cache state and token generation patterns, Apt-Serve reaches up to an 8.8-fold increase in effective throughput (arXiv 2504.07494).
Similarly, the gLLM framework addresses the problem of imbalanced workloads within batches by employing a globally balanced pipeline parallelism technique called Token Throttling. This method dynamically adjusts computation across distributed nodes, reducing pipeline stalls due to uneven token processing times among sequences in a batch. By managing prefill and decode tokens adaptively, gLLM achieves nearly 4 times the throughput with lower latency, demonstrating the importance of balancing load at a granularity finer than entire batch units (arXiv 2504.14775).
Together, these innovations highlight how optimizing batch composition is not about simply increasing batch size. Instead, it is about dynamically managing workloads, memory, and token processing to keep GPUs fully occupied without running into memory bottlenecks or pipeline inefficiencies. This dynamic approach to batch composition enables LLM inference pipelines to scale more efficiently, providing both higher throughput and lower latency in production environments.
Throughput Improvements with Apt-Serve
Apt-Serve tackles the challenge of scaling LLM inference by introducing an adaptive request scheduling mechanism built on a hybrid cache design. Unlike traditional systems that rely solely on memory-heavy key-value caches, Apt-Serve combines this with a memory-efficient hidden cache. This hybrid approach allows the system to handle much larger batch sizes and increase request concurrency without hitting memory limits prematurely.
By optimizing how batches are composed with this adaptive scheduling, Apt-Serve achieves significant gains in effective throughput—up to 8.8 times higher than static batch scheduling methods. The system dynamically selects requests based on available cache states, minimizing redundant memory access and enabling the GPU to be utilized more fully. This directly addresses the inefficiencies caused by rigid batching and memory constraints common in large language model serving.
Effectively, Apt-Serve’s design allows the inference pipeline to process more requests concurrently by better matching the memory footprint of cached intermediate states to the available GPU resources. This leads to smoother load balancing and fewer bottlenecks during token generation phases. As a result, the throughput improvements are not just theoretical but translate to practical latency reductions and higher efficiency in real-world deployments (arXiv:2504.07494).
Continuous Batching: Dynamic Iteration-Level Scheduling
One of the critical challenges in large language model (LLM) inference is the inefficiency of static batching, where the system waits for all sequences in a batch to finish before starting new ones. This approach leads to underutilized GPU resources, especially when token generation lengths vary or some requests complete earlier than others. Continuous batching, also known as dynamic or iteration-level scheduling, presents a practical solution by immediately replacing completed sequences in the batch with new incoming sequences, rather than idling until the entire batch completes.
This method significantly smooths out the workload on GPUs by maintaining a steady flow of data rather than processing in fixed cycles. By dynamically scheduling on a per-iteration basis, GPUs avoid idle time, which results from waiting on slower sequences within the batch. The impact can be profound: studies show that continuous batching techniques can lead to up to a 23x increase in throughput alongside notable latency drops when compared to traditional static batching methods (source).
Further enhancing this approach, research from gLLM integrates continuous batching with global pipeline parallelism and token-level throttling. Their method balances workloads in distributed setups by dynamically managing prefill and decode tokens, reducing pipeline bubbles that cause delays. This approach achieves up to 398% throughput improvements while simultaneously cutting latency (source).
In parallel, systems like Apt-Serve demonstrate how combining adaptive request scheduling with hybrid caches can complement continuous batching. By organizing batches more effectively and increasing concurrency, they boost overall throughput up to 8.8 times (source).
Together, these advances reveal the power of iteration-level scheduling in adaptive LLM inference pipelines. Continuous batching addresses the inherent scheduling rigidity and uneven token generation lengths that challenge LLM serving systems. By dynamically balancing workloads and optimizing batch composition, continuous batching reduces wasted GPU cycles and effectively lowers latency, enabling more efficient and scalable LLM deployment in production environments.
Replacing Completed Sequences Immediately to Enhance GPU Utilization
One of the crucial challenges in large language model (LLM) inference is maintaining high GPU utilization while managing batches of varying-length token sequences. Traditional static batching waits for all sequences to complete before starting a new batch, which often leads to GPU idle time as shorter sequences finish earlier and the system sits idle waiting for the slowest sequence. To address this inefficiency, continuous batching—also known as dynamic or iteration-level scheduling batching—replaces completed sequences immediately with new ones within the same batch. This approach maximizes GPU usage by keeping the compute units fully occupied without unnecessary pauses.
By inserting new sequences as soon as a slot opens up, continuous batching prevents the "pipeline bubbles" that occur in static scheduling and optimizes throughput. This strategy has been shown to improve throughput by as much as 23 times compared to static batching while also reducing latency significantly. The real-time sequence replacement keeps the batch size stable and effectively balances the workload across the GPU, mitigating delays caused by variable token generation lengths.
This method is a natural complement to other adaptive techniques such as dynamic batching and model switching, which aim to balance computation load and memory usage dynamically. Together, these strategies unlock more cost-effective and scalable LLM deployments with lower latency, especially at high request volumes (source, source).
Comparing Continuous Batching with Static Batching
When handling large language model (LLM) inference workloads, how batches of requests are managed significantly impacts latency and throughput. Traditional static batching collects multiple requests into a fixed batch and processes them together, waiting until all sequences in the batch complete before starting a new one. While straightforward, static batching often leads to underutilized GPU resources because sequences in the batch can finish at different times, leaving some GPUs idle as they wait for the slowest request to complete.
Continuous batching, sometimes called dynamic or iteration-level scheduling batching, offers a more flexible alternative. Instead of waiting for all sequences to complete, continuous batching immediately replaces completed sequences with new ones in the batch. This approach better maintains GPU utilization and throughput by reducing idle periods between sequence processing. Research shows that continuous batching can achieve up to 23 times higher throughput and significantly lower latency compared to static batching, demonstrating its advantage in real-time LLM inference scenarios (source).
Beyond just improving device utilization, continuous batching adapts better to the variable token generation lengths typical in language models. Static batching suffers from padding inefficiencies because all sequences must conform to the longest request in the batch. Continuous batching avoids this by constantly refreshing the batch composition, reducing the padding overhead and allowing for more efficient memory use.
Recent frameworks like Apt-Serve leverage this adaptive scheduling in conjunction with hybrid caching strategies to further enhance batch size and concurrency, providing up to 8.8 times improvement in effective throughput (source). Meanwhile, pipelines such as gLLM incorporate dynamic token management across distributed nodes, tackling imbalanced computation delays and offering nearly 4 times better throughput than static alternatives (source).
In summary, continuous batching outperforms static batching by dynamically optimizing batch composition and resource allocation, leading to lower latency, better GPU utilization, and improved throughput. This makes continuous batching a critical technique for scalable, cost-effective LLM inference deployments.
Throughput and Latency Benefits from Continuous Batching
Continuous batching is a powerful technique that fundamentally improves both throughput and latency in large language model (LLM) inference pipelines. Unlike traditional static batching, which waits for all sequences in a batch to complete before processing the next batch, continuous batching immediately replaces completed sequences with new ones in the same batch. This dynamic, iteration-level scheduling maximizes GPU utilization by keeping computational resources consistently busy without idle gaps.
The key benefit is a dramatic improvement in throughput. For example, continuous batching can yield up to a 23x increase in throughput compared to static batching approaches. This happens because GPUs avoid stalling and remain fully utilized, continuously processing tokens from different sequences that finish at variable lengths and times. In addition to throughput gains, continuous batching reduces latency by minimizing waiting time caused by slower sequences blocking the pipeline. This means that overall request processing becomes more efficient and predictable, crucial for large-scale, real-time LLM applications (source).
Systems like gLLM have taken continuous batching further by integrating global pipeline balancing techniques. Their approach uses Token Throttling to dynamically manage prefill and decode tokens across distributed nodes, smoothing out imbalances that otherwise create pipeline bubbles or idle GPU cycles. This not only increases throughput by nearly 4 times but also decreases latency across the entire inference pipeline (source).
Similarly, Apt-Serve improves throughput by combining continuous batching with adaptive request scheduling on a hybrid cache. This hybrid cache merges a memory-intensive KV cache for speed with a memory-efficient hidden cache to enable larger batch sizes and more concurrent requests. Optimizing batch composition in this way has shown to improve effective throughput by as much as 8.8 times, illustrating how memory optimization and batching strategies complement each other effectively at scale (source).
Overall, continuous batching addresses core inefficiencies caused by variable token generation lengths, memory constraints, and rigid scheduling in LLM serving. By continuously feeding the GPU with ready data and balancing workloads dynamically, it unlocks both higher throughput and lower latency. This leads to more cost-effective deployment of LLM inference systems that scale efficiently without sacrificing responsiveness.
Addressing Inefficiencies in LLM Serving Systems
Large language model (LLM) serving systems face significant inefficiencies related to memory constraints, uneven token generation lengths, and rigid scheduling, all of which impact throughput and latency. To improve performance at scale, recent methods have focused on dynamic strategies that adjust computation and resource management in real time.
One major bottleneck is pipeline imbalance, where different stages of token processing take uneven amounts of time, leading to idle GPU cycles or "pipeline bubbles." The gLLM system addresses this with a globally balanced pipeline parallelism approach that applies Token Throttling. This technique dynamically controls the flow of prefill and decode tokens across distributed nodes, reducing idle time and improving utilization. The result is a throughput increase of up to 398% and reduced latency by balancing the workload more evenly (gLLM source).
Another challenge arises from the memory demands of caching intermediate states during inference. Apt-Serve tackles this by combining a memory-heavy key-value (KV) cache with a lighter hidden cache in a hybrid caching system. This adaptive request scheduling system optimizes batch composition by intelligently managing requests according to cache availability, allowing for larger batch sizes and greater concurrency. This method can achieve an effective throughput improvement of up to 8.8 times compared to static batching strategies (Apt-Serve source).
Static batching, where a batch is held until all sequences complete processing, limits hardware efficiency. Continuous batching, also called dynamic or iteration-level scheduling batching, improves upon this by immediately filling spots freed by completed sequences with new requests. This keeps GPUs running at higher utilization without unnecessary idle waiting. Such systems have demonstrated throughput improvements up to 23x and significant reductions in response latency (Continuous Batching source).
Together, these approaches address core inefficiencies in LLM serving systems by dynamically adapting computation loads and memory usage in response to real-time conditions. Leveraging adaptive batching and model switching techniques not only boosts throughput and reduces latency but also enables more cost-effective deployment of large models at scale.
Balancing Computation Loads Dynamically
A key challenge in scaling large language model (LLM) inference pipelines is the uneven distribution of computational loads caused by varying token generation lengths and memory requirements. Static batching systems often suffer from pipeline stalls or underutilized GPU resources because they wait for all sequences within a batch to complete before processing the next set. This inefficiency creates a bottleneck that increases latency and reduces throughput.
One effective approach to overcoming this is dynamic batching, also called continuous or iteration-level scheduling batching. Instead of waiting for every sequence in a batch to finish, dynamic batching immediately replaces completed sequences with new ones. This continuous flow of work maximizes GPU utilization and smooths out the computation pipeline, enabling significantly higher throughput and lower latency. Research shows that continuous batching can achieve up to a 23x improvement in throughput compared to static batching methods (Anyscale).
On a more granular level, systems like gLLM introduce globally balanced pipeline parallelism by dynamically managing token processing across distributed nodes. Their method, called Token Throttling, reduces pipeline bubbles—idle times caused by imbalanced compute delays when some nodes lag behind others—by adjusting how many prefill and decode tokens each node handles in real-time. This leads to close to a 4x increase in throughput, showing how balancing token workloads dynamically across a pipeline can significantly expedite processing (arXiv 2504.14775).
Additionally, the Apt-Serve framework tackles computational load balancing from a memory perspective by hybridizing caching mechanisms. It combines a memory-intensive key-value cache with a more memory-efficient hidden state cache to accommodate larger batch sizes and higher request concurrency. This adaptive request scheduling not only optimizes GPU memory usage but also improves batch composition, resulting in nearly ninefold effective throughput gains (arXiv 2504.07494).
Together, these techniques address the intrinsic hardware and workload variability in LLM inference pipelines. By dynamically redistributing computational tasks and optimizing memory allocation, adaptive pipelines maintain a balanced load across GPUs. This balance reduces idle times and memory bottlenecks, paving the way for low-latency, cost-effective deployment of large-scale language models.
Optimizing Memory Usage Across GPU Resources
One critical challenge in scaling large language model (LLM) inference is the efficient management of GPU memory. Memory constraints naturally limit the batch sizes and concurrency levels that can be achieved, directly affecting throughput and latency. Adaptive inference pipelines address this by dynamically optimizing memory usage across GPU resources to maintain high utilization without running into bottlenecks.
A notable approach is seen in Apt-Serve, which employs a hybrid caching strategy combining a memory-heavy key-value (KV) cache with a more memory-efficient hidden cache. This dual-cache mechanism allows the system to handle larger batch sizes and more concurrent requests than traditional single-cache architectures. By intelligently adapting request scheduling to the cache state, Apt-Serve maximizes the effective use of GPU memory. The result is a significant boost in throughput—up to 8.8 times higher—by ensuring memory resources are not wasted on redundant data or inactive sequences (source).
Similarly, gLLM tackles memory inefficiencies through globally balanced pipeline parallelism. It introduces a technique called Token Throttling to manage the flow of tokens dynamically across distributed nodes. By regulating prefill and decode tokens in real-time, gLLM prevents pipeline bubbles—idle periods caused by imbalanced computation loads—which typically waste GPU memory and computational resources. This balance leads to a smoother memory footprint across GPUs, achieving up to nearly four times improved throughput with lower latency (source).
Beyond caching and token flow control, continuous batching further optimizes memory use by managing batch compositions dynamically at the iteration level. Instead of waiting for entire batches to complete, completed sequences are immediately replaced with new ones, keeping GPU memory constantly occupied with active computations. This method eliminates idle memory periods common in static batching and significantly raises utilization, manifesting in up to a 23-fold increase in throughput and noticeable latency drop in LLM serving (source).
Together, these strategies highlight an overarching trend: adaptive memory management in LLM pipelines requires dynamic balancing of workloads and intelligent scheduling to fully leverage GPU resources. Properly optimized memory usage reduces bottlenecks and enables larger, more efficient batches that scale gracefully with demand. This approach forms a key foundation for delivering low-latency, cost-effective inference at scale.
Implications for Cost-Effective and Low-Latency LLM Deployment at Scale
Deploying large language models (LLMs) at scale presents clear challenges around cost and latency, largely due to the variability in token generation and the heavy memory demands on GPUs. Adaptive inference pipelines that incorporate dynamic batching and model switching offer promising ways to address these issues by improving resource utilization and throughput without compromising response times.
One major implication is that system architectures can become significantly more efficient by actively managing computation loads across distributed nodes. For example, gLLM’s globally balanced pipeline parallelism reduces idle times—referred to as pipeline bubbles—by dynamically throttling tokens during both prefill and decode stages. This method allows workloads to stay balanced even as token lengths vary, leading to throughput improvements up to nearly 4 times higher and corresponding latency reductions (source).
Another critical benefit comes from memory optimization strategies. Apt-Serve’s hybrid cache design merges a memory-intensive key-value cache with a lightweight hidden activations cache. This combination enables the system to handle larger batch sizes and concurrency without exceeding memory limits, boosting effective throughput by nearly 9 times. Efficient cache usage and adaptive request scheduling directly translate into cost savings because hardware resources are maximized rather than sitting idle or overprovisioned (source).
Furthermore, continuous batching or iteration-level scheduling allows for immediate replacement of completed token sequences within a batch, rather than waiting for all sequences to finish. This approach maximizes GPU utilization by keeping computation pipelines full and avoids the latency overhead caused by static batching techniques. The resulting throughput gains can be over an order of magnitude, significantly lowering the per-inference runtime—and thus operational costs—while maintaining or even improving latency targets (source).
Together, these adaptive pipeline innovations highlight a shift from rigid batching and static resource management toward a more fluid, demand-driven deployment model. For engineers and architects, adopting these techniques means they can scale LLM services more cost-effectively while ensuring that latency remains low enough to support real-time or interactive applications. The gains in throughput and memory efficiency reduce the need for excess infrastructure, making large-scale LLM deployment more accessible and sustainable.
Adaptive LLM inference pipelines represent a significant step forward in addressing the complex challenges of scaling large language model serving while minimizing latency. Techniques like dynamic batching and model switching are key to making inference more efficient and cost-effective across distributed GPU resources. For example, gLLM’s globally balanced pipeline with Token Throttling dynamically manages the flow of tokens between prefill and decode stages, which greatly reduces idle time in the pipeline and improves throughput by nearly 4x compared to traditional static parallelism approaches (source). This demonstrates how balancing computation across nodes rather than relying on fixed partitions can profoundly impact performance.
Similarly, Apt-Serve applies an adaptive request scheduler on a hybrid cache system. By combining a memory-heavy KV cache with a lightweight hidden cache, it dynamically increases batch sizes and concurrency, achieving close to 9x improvements in effective throughput through smarter batch composition and cache management (source). This shows the importance of memory optimizations alongside scheduling techniques in large-scale LLM serving.
Continuous batching is another complementary innovation that replaces static batch waits with iteration-level dynamic scheduling. This approach immediately fills GPU capacity by swapping completed sequences out for new ones, maximizing hardware utilization and reducing latency significantly, with reported throughput gains up to 23x (source). Taken together, these advancements confront fundamental inefficiencies tied to memory limits, token output variability, and fixed scheduling in LLM inference.
Looking ahead, further integration of dynamic load balancing, intelligent cache hierarchies, and fine-grained scheduling will continue to push the boundaries of scalable and low-latency LLM serving. As model architectures evolve and demand surges, these adaptive pipelines will enable more responsive AI applications without prohibitive infrastructure costs. The path forward lies in continuously refining these methods and exploring hybrid strategies to unlock ever higher levels of throughput and efficiency in practical deployments.