Language-Aware Batching

• Language-aware batching is a collection of techniques that group LLM requests based on linguistic and execution characteristics to enhance performance.
• It employs methods such as prefix and cache-aware grouping, RL-based decisions, and deadline-driven scheduling to optimize batch formation.
• These strategies reduce padding, latency, and energy waste while improving throughput, goodput, and cost efficiency compared to conventional FCFS methods.

Language-aware batching is a family of batching and scheduling techniques for LLM systems that forms batches using properties induced by language workloads rather than arrival order alone. In the recent literature, those properties include prefix or KV-cache length, generation length, sequence length, token-speed service-level objectives (SLOs), prompt semantics, task structure, and phase-specific compute or memory behavior. The shared aim is to reduce padding, batch-level bubbles, head-of-line blocking, KV-cache traffic, verification interference, discounted outputs, or energy waste, while improving throughput, goodput, latency, cost efficiency, or faithfulness (Bai et al., 22 May 2026, Rathi et al., 7 May 2026, Cheng et al., 2024, Larionov et al., 4 Mar 2025).

1. Problem setting and defining characteristics

Conventional batching policies in LLM serving are frequently described as FCFS, static, SLO-blind, or size-only. Across multiple systems papers, this baseline is inadequate because requests differ sharply in prefix length, generation length, cache state, latency slack, and prompt structure. In decode-heavy serving, a few long-prefix or long-generation requests can dominate iteration latency and batch completion time; in speculative or distributed serving, heterogeneous batches can inflate verification latency; in prompt batching, grouping multiple examples can degrade output quality or faithfulness if the aggregation method is not aligned with the task (Li et al., 15 Jan 2026, Bai et al., 22 May 2026, Guldogan et al., 2024, Liu et al., 2024).

A central systems observation is that “batch size” alone is not a sufficient control variable. AlignedServe reports that with batch size 64, mixing just 4 long requests increased iteration latency by approximately 61%, from 13.49 ms to 21.73 ms, because tokens in the same decode iteration can incur different costs when their KV caches have different lengths (Bai et al., 22 May 2026). Feather generalizes the same point: smaller, prefix-homogeneous batches can outperform larger heterogeneous batches, and even a single divergent prefix in an otherwise homogeneous 500-request batch can halve throughput (Rathi et al., 7 May 2026). Multi-Bin Batching formalizes the length-heterogeneity problem queue-theoretically by treating batch service time as the maximum request time within the batch, so the slowest request determines effective service time (Guldogan et al., 2024).

The same heterogeneity appears in deadline-sensitive settings. WISP identifies two bottlenecks in distributed speculative serving—Wasted Drafting Time and Verification Interference—and attributes SLO violations to latency inflation when heterogeneous requests are batched together (Li et al., 15 Jan 2026). FairBatching makes an analogous argument for prefill/decode mixtures: rigid decode prioritization can waste decode slack and induce unnecessary prefill queuing delays, degrading global QoS (Lyu et al., 16 Oct 2025). On the prompt side, BatchGEMBA finds that batching generally negatively affects evaluation quality, while CliqueParcel frames the associated degradation as a “discounted output” problem (Larionov et al., 4 Mar 2025, Liu et al., 2024).

Workload signal	Representative systems	Operational purpose
Prefix or KV-cache length	AlignedServe, Feather	Reduce iteration-level bubbles and improve KV locality
Generation or execution length	Magnus, Multi-Bin Batching, BucketServe	Reduce stragglers, padding, and memory waste
SLO slack or deadlines	WISP, SABER, FairBatching, SBS	Control interference and deadline misses
Prompt semantics or task structure	CliqueParcel, BatchGEMBA, RoBatch	Limit quality degradation and improve cost utility trade-offs

This body of work suggests that “language-aware” is best understood as workload-structure-aware. Some methods use explicitly linguistic or semantic information, such as concept cliques or semantic embeddings; others use execution properties generated by the language workload, such as KV-cache depth or predicted generation length.

2. Prefix-, length-, and cache-aware batch construction

A major branch of language-aware batching groups requests by prefix, cache, or sequence-length homogeneity. AlignedServe is built around prefix-aware batching: it groups requests with similar KV-cache lengths into the same batch, uses a quad-tree over prefix-length ranges, and applies a Density First Search procedure with parameters such as maximum memory blocks per batch $B_{max}$ and minimum batch size $K_{min}$ (Bai et al., 22 May 2026). Its architecture places KV caches in a large CPU-resident KV pool, forms batches on the CPU, and then uses a GPU-Prefetch-For-GPU pipeline in which one GPU prefetches KV cache for another via NVLink. Reported results show up to $1.98\times$ higher decoding throughput and up to $7.4\times$ reduction in P99 TPOT over state-of-the-art systems (Bai et al., 22 May 2026).

Feather addresses a closely related problem but emphasizes the tradeoff between batch size and prefix homogeneity. Its key claim is that prefix-aware schedulers in existing engines generally maximize prefix reuse only to reduce KV-cache memory footprint, but do not stop batch formation at smaller homogeneous batches that may achieve higher decode throughput (Rathi et al., 7 May 2026). Feather therefore models batch construction as a sequential decision problem and uses RL to decide whether to admit the next request or stop and launch the batch. To avoid the radix-tree overhead of existing prefix detection, it introduces the Chunked Hash Tree, with candidate selection cost $O(\log W)$ rather than $O(W \cdot T)$ per decision. Integrated into vLLM and SGLang, Feather reports $2$–$10\times$ higher end-to-end throughput on workloads with significant prefix sharing while doing no worse than the status quo when the workload lacks sufficient prefix sharing (Rathi et al., 7 May 2026).

Length-aware batching occupies a parallel line of work. Magnus predicts generation length from raw input length, application-level semantics, and user-level semantics using a random forest regressor with LaBSE-derived features, then groups requests with similar predicted generation lengths and assigns each new request to the queued batch that minimizes post-insertion Wasted Memory Access (Cheng et al., 2024). It further applies adaptive batch sizing under a memory model and Highest Response Ratio Next scheduling. On its testbed, Magnus improves request throughput by up to 234% and reduces response time by up to 89.7% compared to baselines (Cheng et al., 2024). Multi-Bin Batching uses predicted or oracle execution times to place requests into predetermined bins, proving asymptotic throughput optimality as the number of bins increases, while also showing that excessively fine binning can increase waiting time for batch formation (Guldogan et al., 2024).

BucketServe makes the same length-homogeneity principle explicit at the serving-system level. It groups requests into size-homogeneous buckets based on sequence length, dynamically splits or merges buckets under changing load, and adjusts batch sizes in real time to prevent OOM errors (Zheng et al., 23 Jul 2025). The reported improvements include up to $3.58\times$ throughput over UELLM, the ability to handle $1.93\times$ more request load under 80% SLO attainment than DistServe, and $K_{min}$0 higher system load capacity than UELLM (Zheng et al., 23 Jul 2025).

BATON extends language-aware batching from batch formation to batch maintenance. Instead of waiting for a run-to-completion batch to drain, it uses vector shaping, attention-mask updates, and KV-cache embedding to insert newly prefilled queries into a running decode batch without duplicating nonlinear layers or incurring additional resource consumption (Cong et al., 2024). This dynamic re-batching is presented as achieving near-zero idle computations and improving query processing by up to $K_{min}$1 over Orca (Cong et al., 2024).

3. SLO-aware, deadline-aware, and fairness-aware scheduling

A second major branch uses per-request deadlines, token-rate requirements, or fairness envelopes as the organizing signal for batch construction. WISP is the clearest example. In distributed speculative serving, each request is assigned a token-speed SLO class with speed target $K_{min}$2, and the system computes a per-request deadline

$K_{min}$3

Batch construction is then formulated as a deadline-constrained knapsack problem over estimated verified tokens, estimated memory, available GPU memory, and estimated batch verification time (Li et al., 15 Jan 2026). WISP defines a utility density

$K_{min}$4

labels requests as critical or non-critical using the latest start time $K_{min}$5, admits critical requests in EDF order while feasible, and then greedily fills residual capacity with high-utility non-critical requests (Li et al., 15 Jan 2026). Its verification-time estimator models new tokens, interaction-heavy attention work, and cached tokens, with reported $K_{min}$6. On Qwen3-32B under strict 8 tokens/s SLOs, WISP supports 86 devices versus 21 for SLED and 41 for centralized serving, and at high load it achieves 631.86 tokens/s versus 170.82 for SLED and 326.12 for centralized serving (Li et al., 15 Jan 2026).

SABER adapts the same deadline logic to CodeLLM serving. It profiles token generation speed as a function of concurrency, fits an estimation function $K_{min}$7 using the Universal Scalability Law with $K_{min}$8, and admits a new request only if the predicted generation speed suffices for all batched requests to meet their SLAs (Chang et al., 24 Jun 2025). The system maintains a High-Priority Queue for requests that can still feasibly meet SLA and a Low-Priority Queue for requests that can no longer do so. Reported gains are up to 26% improvement in goodput and up to 45% reduction in latency variability over the best static configurations (Chang et al., 24 Jun 2025).

FairBatching replaces non-monotonic TBT-centric control with an envelope-based deadline model. For request $K_{min}$9 and token $1.98\times$0, it defines

$1.98\times$1

and computes request slack relative to the next token deadline (Lyu et al., 16 Oct 2025). Batch capacity is time-based rather than token-based, urgent decode tasks are prioritized first, then prefill tasks, then non-urgent decode tasks, and a calibrated batch-time model

$1.98\times$2

is used during admission (Lyu et al., 16 Oct 2025). The reported outcome is up to $1.98\times$3 lower TTFT tail latency, 20.0% improvement in single-node capacity, and 54.3% improvement in cluster-level capacity (Lyu et al., 16 Oct 2025).

Staggered Batch Scheduling applies related ideas to P/D-separated large-scale DP+EP deployments. Instead of immediate dispatch, SBS buffers requests to form execution batches before they enter the engine, enabling global assignment and eliminating in-engine queuing bubbles (Tian et al., 18 Dec 2025). Its analysis contrasts immediate dispatch, with expected queueing delay approximately $1.98\times$4, against staggered dispatch, with expected delay approximately $1.98\times$5 when the buffering interval is $1.98\times$6 (Tian et al., 18 Dec 2025). On a production H800 cluster serving Deepseek-V3, the system reports 30%–40% lower TTFT and 15%–20% higher throughput than immediate scheduling baselines (Tian et al., 18 Dec 2025).

4. Prompt batching, semantic grouping, and utility-aware aggregation

Language-aware batching is not limited to token-level serving. A separate literature studies prompt aggregation itself as the batched unit. BatchGEMBA-MQM extends GEMBA-MQM for machine translation evaluation by placing multiple examples in one prompt, sharing instructions and demonstrations across the batch, and then adding a batching-aware compression model (Larionov et al., 4 Mar 2025). Batching alone reduces token usage by $1.98\times$7–$1.98\times$8, and compression yields an additional 13%–15% token reduction on average. At the same time, the paper reports that batching generally negatively affects quality, whereas compression does not further degrade quality and can sometimes recover batch-induced loss; for GPT-4o, compression at batch size 4 retains over 90% of baseline performance, whereas uncompressed batching incurs a 44.6% drop in one reported comparison (Larionov et al., 4 Mar 2025).

CliqueParcel addresses prompt batching from an efficiency-faithfulness perspective. It explicitly excludes efficiency gains due to shorter outputs from its efficiency metric, defines a faithfulness score combining semantic similarity, textual overlap, and correctness, and organizes prompts into cliques using concept-based, semantic-similarity-based, concept-plus-semantic, average-length, and other grouping functions (Liu et al., 2024). Across eight datasets, CliqueParcel reports 138.93% average efficiency improvement over separate prompting, with faithfulness loss generally at or below 2% in most cases, though TREC shows drops up to 4.5% (Liu et al., 2024). This line of work makes language-aware batching literal: prompts are grouped by concept or semantic similarity, not merely by runtime cost.

RoBatch extends the idea to cost-aware model routing. It formulates the Route with Batching Problem, jointly choosing the target model and batch size for each query under a total budget constraint, proves the problem NP-hard by reduction from Maximum Coverage, and proposes a proxy utility model

$1.98\times$9

that separates unbatched utility estimation from model-specific utility degradation under batching (Xu et al., 27 May 2026). The routing stage then performs greedy upgrades along each query’s Pareto frontier until the budget is exhausted. On six benchmarks across Qwen3 and Gemma3, RoBatch is reported to achieve a superior cost-performance Pareto frontier relative to routing-only and batch-only baselines (Xu et al., 27 May 2026).

These prompt-level systems show that batching can act directly on prompt semantics, annotation structure, or model-utility curves, not only on GPU occupancy. A plausible implication is that the boundary between serving-time scheduling and prompt-time aggregation is becoming less rigid.

5. Edge, hardware, energy, and training extensions

Language-aware batching also appears in edge-serving, hardware-software co-design, and distributed training. In edge inference, the DFTSP algorithm groups requests by output length, prioritizes users with higher latency tolerance, shorter output length, and lower communication demand, and prunes infeasible branches under memory, latency, bandwidth, and accuracy constraints (Zhang et al., 2024). The paper reports that DFTSP surpasses other batching benchmarks in throughput across diverse user settings and quantization techniques, while reducing time complexity by over 45% relative to brute-force search (Zhang et al., 2024).

At the hardware level, Duplex studies continuous batching for MoE and grouped-query-attention models and argues that batching changes arithmetic intensity in ways that conventional devices handle poorly (Yun et al., 2024). Duplex therefore combines an xPU for high-Op/B work with Logic-PIM for low-Op/B work, and dynamically assigns MoE experts or attention subcomputations based on runtime characteristics. Reported gains are up to $7.4\times$0 throughput, up to 42.0% lower energy consumption, and $7.4\times$1 lower end-to-end latency than a GPU-only system (Yun et al., 2024).

The energy perspective is developed explicitly in the H100 study of quantization, batching, and serving configuration. That work finds that batching improves energy efficiency, especially in memory-bound decode, and that energy per output token can fall by about 65% at batch size 4 relative to batch size 1 in one reported setup (Delavande et al., 29 Jan 2026). Moving from naive static batching to Hugging Face TGI continuous batching reduces GPU energy per request from $7.4\times$2 Wh to $7.4\times$3 Wh, approximately $7.4\times$4 savings, and structured request timing can reduce per-request energy by up to 100 times (Delavande et al., 29 Jan 2026). The paper argues for phase-aware energy profiling because prefill is more compute-bound while decode is predominantly memory-bound.

In distributed training, AdLoCo uses adaptive batched DiLoCo rather than fixed local batch size. It applies a norm test to choose

$7.4\times$5

allowing batch size to grow as convergence approaches, combines this with Multi-Instance Training and weighted knowledge merging, and activates gradient accumulation only when the requested batch exceeds a hardware-friendly threshold (Kutuzov et al., 25 Aug 2025). Although this setting concerns training rather than inference, it extends the same principle: batching decisions are coupled to language-model-specific dynamics rather than fixed heuristics.

6. Trade-offs, limitations, and recurring misconceptions

A recurrent misconception is that larger batches are intrinsically better. Several papers contradict this directly. Feather shows that smaller, prefix-homogeneous batches can beat larger heterogeneous ones because locality dominates raw batch size under prefix-sharing workloads (Rathi et al., 7 May 2026). AlignedServe shows that iteration-level bubbles emerge when a small minority of long-prefix requests is mixed with short-prefix requests (Bai et al., 22 May 2026). The energy study similarly reports a U-shaped behavior in decode energy per token, with gains saturating after moderate batch sizes and padding costs eventually dominating in prefill (Delavande et al., 29 Jan 2026).

Another misconception is that batching is quality-neutral. BatchGEMBA reports quality decline as batch size increases, even though compression may recover some of that loss (Larionov et al., 4 Mar 2025). CliqueParcel treats discounted outputs as a first-class systems problem and explicitly redefines efficiency to avoid crediting shorter, incomplete answers as a batching win (Liu et al., 2024). RoBatch likewise assumes that utility degrades with batch size in a model-specific way and builds that degradation into its proxy utility model (Xu et al., 27 May 2026).

Prediction and control overhead form a separate limitation. Magnus depends on generation-length prediction and can still encounter OOM when prediction is inaccurate, although it mitigates this by splitting the offending batch and re-queueing it (Cheng et al., 2024). Multi-Bin Batching proves a latency trade-off in which more bins reduce service-time heterogeneity but can increase waiting time for batch formation (Guldogan et al., 2024). Feather shows that prefix detection itself can become a CPU bottleneck: radix-tree traversals may consume 50%–90% of scheduling time for long sequences, motivating the Chunked Hash Tree (Rathi et al., 7 May 2026).

A final misconception is that language-aware batching refers only to semantic grouping. In fact, the literature spans semantic cliques, prefix homogeneity, generation-length prediction, token-speed deadlines, phase separation, and hardware-aware arrival shaping. This suggests that the term now covers both content-aware and execution-aware batching. A plausible implication is that future systems will increasingly combine these views, jointly optimizing semantic structure, cache locality, SLO slack, and energy behavior rather than treating them as separate scheduling layers.