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REVIEW 3 major objections 4 minor 42 references

BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A hybrid AD/SD scheduler for edge LLMs serves about a third more users.

desk verdict New problem, sensible decomposition, but a memory-accounting error in Algorithm 1 invalidates the AD subproblem's optimality and the claimed 1/2 approximation guarantee. read the letter →

arxiv 2608.05926 v1 pith:X7IMXQ4K submitted 2026-08-06 cs.NI cs.AI

Guanqiao Qu , Shuo Chen , Qian Chen , Kin K. Leung , Xianhao Chen This is my paper · ORCID
classification cs.NIcs.AI
keywords edgeLLMinferencespeculativedecodingautoregressiveuserschedulingresourceallocationtaskthroughputapproximationalgorithmhybrid
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that an edge server hosting both a large language model and a small draft model should not commit all its users to either autoregressive decoding (AD) or speculative decoding (SD), but instead assign each user to whichever mode fits their latency budget and memory cost. It formulates this as a task-throughput maximization problem: choose which users to serve, which mode to give each, and how to split GPU compute and memory between the two modes, subject to per-user end-to-end latency limits and a server memory cap. Because the problem is NP-hard, the authors decompose it into separate AD and SD scheduling sub-problems and stitch the solutions together by enumerating discrete resource-split ratios. They prove the resulting algorithm always serves at least half the optimal number of users, and their GPU measurements show it typically serves 27–39 percent more users than AD-only or SD-only baselines, landing within about 0.9 percent of an exhaustive search.

What carries the argument

The carrying mechanism is the decomposition of the original problem P1 into two scheduling sub-problems, linked by two auxiliary splits: the GPU computing fraction $z$ and the GPU memory fraction $\lambda$. For fixed $z$ and $\lambda$, the AD and SD modes become independent knapsack-like problems; each is solved optimally by enumerating feasible tuples (maximum input length, maximum output length, latency budget, upload time) and, for each tuple, sorting candidate users by their marginal latency contribution and selecting as many as fit the memory and latency budgets. Algorithm 3 wraps these two solvers in a loop over the finite sets $Z$ and $\Lambda$, and Theorem 1 proves that the best union across all pairs is a $1/2$-approximation to the optimum. The approximation proof exploits the fact that $X$ and $Y$ are disjoint, so $r(X+Y)=r(X)+r(Y)$, and charges every user in the optimal solution either to the greedy AD schedule or to the greedy SD schedule.

What would settle it

Measure actual per-prompt acceptance rates for TinyLlama-1.1B drafting for Llama-2-7B across the input and output length ranges used in the experiments; if the distribution is wide enough that a scheduler using a single constant $A_k$ per user misses latency deadlines at the claimed throughput, the central scheduling model fails.

Watch

Extended reading notes

Core claim

The central claim is that hybrid provisioning of AD and SD is both tractable and practically beneficial: the joint user-scheduling and resource-allocation problem can be approximated in polynomial time with a constant factor of 1/2, and the resulting BALANCE scheduler consistently outperforms single-mode baselines. The argument proceeds by decomposition: once the GPU computing fraction $z$ and the GPU memory fraction $\lambda$ are fixed, the problem splits into an AD sub-problem and an SD sub-problem, each solvable optimally by enumerating the few discrete parameters that define a scheduling scenario (maximum input length, maximum output length, latency budget, upload time) and filling the latency and memory budgets with users sorted by their marginal cost. Enumerating all $(z, \lambda)$ pairs and keeping the best union gives the approximation guarantee, because any optimal user selected for SD can be charged either to the AD schedule or to the SD schedule. The experiments on an RTX 4090 with Llama-2-7B and TinyLlama-1.1B report normalized throughput gains of roughly 27–39 percent over AD-only and SD-only baselines and only about 0.9 percent degradation relative to exhaustive search.

Load-bearing premise

The scheduler assumes it knows each user's speculative-draft acceptance rate and output length as fixed numbers; in practice these vary with the prompt and the model, and if the assumed values are too optimistic, users can miss their latency deadlines.

Editorial extensions

If this is right

  • An operator running BALANCE gets a hard guarantee: no other user-to-mode assignment under the same latency and memory budget can serve more than twice as many users.
  • The decomposition means that adding new decoding modes with their own memory footprints would slot into the same enumeration framework as additional sub-problems, preserving a constant-factor bound.
  • The near-optimal throughput measured against exhaustive search suggests the discrete resource-split grids lose little, so further gains must come from sharper workload and acceptance-rate models rather than different optimization.
  • The algorithm's $O(K^6 \log K)$ runtime with $K$ users is practical at the scales tested (tens of users per server), making it a candidate for online re-scheduling as users arrive and leave.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension is to replace the constant acceptance rate $A_k$ with a per-prompt distribution, turning the SD sub-problem into a stochastic scheduling problem with chance-constrained latency guarantees; the current model is deterministic in this parameter.
  • The memory-split variable $\lambda$ invites a closed-loop controller: the server could re-solve BALANCE periodically, adjusting $z$ and $\lambda$ as measured acceptance rates and queue lengths drift, rather than assuming static values.
  • Because the proof only needs $X$ and $Y$ disjoint and each sub-problem solved optimally, the same $1/2$ guarantee should carry over to multi-GPU servers where AD and SD run on separate devices, as long as resource splits remain enumerable.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes BALANCE, a hybrid autoregressive-speculative decoding framework for a single edge server serving multiple LLM inference users. It formulates a mixed-integer program P1 that maximizes the number of served users under per-user latency requirements and a GPU memory constraint, proves NP-hardness, and proposes Algorithm 3, which decomposes the problem into an AD-mode scheduling subproblem (Algorithm 1) and an SD-mode scheduling subproblem (Algorithm 2) and claims a 1/2 approximation guarantee. The evaluation uses measured single-user latencies plus multi-user simulations and reports normalized throughput gains of roughly 27--39% over AD-only and SD-only baselines, with about 0.9% average degradation versus exhaustive search.

Significance. The problem addressed is timely and practically relevant, and the paper provides a detailed workload, latency, and memory model for hybrid AD/SD edge inference. If the theoretical results were correct, the decomposition into AD and SD subproblems with a constant-factor approximation would be a useful contribution. The inclusion of an exhaustive-search baseline and ablation studies is also a strength. However, the central approximation guarantee is not established: Algorithm 1's memory accounting is inconsistent with the problem constraint, and the P1--P2 equivalence relies on an unjustified assumption about the discrete memory-split set. The empirical claims are further weakened by the fact that the simulator reuses the same analytic model that the scheduler optimizes, with no sensitivity analysis for the assumed acceptance rate.

major comments (3)
  1. [§IV-D, Eq. (27), Proposition 4] The memory contribution assigned to every AD candidate in Eq. (27) is \tilde q_ad = M_L θ (L_1+L_2) 2D_{L,h}H_L, but the actual constraint (22c) with q_ad from Eq. (15) charges user k only M_L θ (L_ad,I_max + L_O^k) 2D_{L,h}H_L. Since L_2 is the tuple maximum output length, Eq. (27) overestimates the memory of every user with L_O^k < L_2, and the cap d_1^* ≤ floor(Q_1/\tilde q_ad) in Eq. (28) is stricter than the true memory constraint. Concretely, take Q_1 = 10 M_L θ·2D_{L,h}H_L, L_1=2, and two users A (L_O=4, variable latency 1) and B (L_O=1, variable latency 100) with latency budget 101. The set {A,B} has true q_ad = (6+3)M_L θ·2D_{L,h}H_L = 9M_L θ·2D_{L,h}H_L ≤ Q_1 and total variable latency 101, so it is feasible in P3.1; Algorithm 1 with L_2=4 uses \tilde q_ad = 6M_L θ·2D_{L,h}H_L per user, giving floor(10/6)=1, so it serves at most one user. Hence Proposition 4 is false, and the inequality r(\hat X) ≥ r(X*) on which Theorem 1's proof relies is not available. Because the overestimate factor (L_1+L_2)/(L_1+L_O^k) is unbounded as L_2 grows, this is a structural flaw rather than a local typo.
  2. [§IV-B, Proposition 2] Proposition 2 claims P1 and P2 are equivalent because Λ 'ensures that every feasible memory split can be represented by some λ∈Λ.' This is an assertion, not a proof: Λ is only defined as a finite set {0,λ_1,...,λ_J,1}. For a feasible solution with given q_ad and q_sd, a suitable λ must lie in the interval [q_ad/C, 1−q_sd/C] with C = Q−q_L−I{z<1}q_S; a coarsely quantized Λ need not contain any such value. Since Algorithm 3 searches only over λ∈Λ and Theorem 1 compares against the P1 optimum (X*,Y*), the equivalence gap can break the comparison. The authors should either define Λ to include all achievable memory ratios (with a proof that this is finite and computable) or incorporate the discretization loss into the approximation factor.
  3. [§V-A, §V-B, Eq. (9)] The numerical evaluation uses the same analytic latency and memory equations (1)--(20) in the simulator as in the scheduling algorithms, and it assumes a known per-user acceptance rate A_k in Eq. (9). The reported throughput gains are therefore predictions of the model being optimized, not independent measurements, and no error bars or sensitivity analyses are provided. In particular, an optimistic A_k underestimates the SD iteration count N_k and can cause the scheduler to miss the deadlines T_k. I recommend validating the multi-user model against a batch-serving prototype, and reporting results under misspecified A_k (e.g., ±0.2), or at least labeling the results as model-based.
minor comments (4)
  1. [§IV-D and §IV-E] Algorithms 1 and 2 enumerate L_1=0, L_2=0, L_3=0, L_4=0, and \tilde N_1=0 as tuple values, but the workload formulas Γ_{L,p}(L_1), Γ_{L,a_l}(L_1), etc. are only defined for positive lengths; please specify the zero-length convention.
  2. [§V-B] Figures 4--7 report only mean normalized throughput; adding per-instance standard deviations or box plots would make the claimed gains more convincing.
  3. [Abstract and Introduction] There are spacing and typographical artifacts in the compiled text, e.g., 'hyb rid a utoregressive-speculative' in the abstract; a careful proofread is needed.
  4. [§IV-F, Theorem 2] The complexity statement treats |Z| and |Λ| as constants; if these sets are part of the problem input, the O(K^6 log K) claim should be qualified accordingly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: BALANCE's approximation guarantee and numerical comparisons are derived from an explicit model with fixed inputs, not from fitting or from a self-citation chain; the only self-citation is background.

full rationale

The claimed derivation chain is not circular. P1 is an optimization model built from explicit workload, latency, and memory formulas (Eqs. (4)-(20)); the acceptance rate A_k, draft length L_D, and model constants are inputs, not parameters fitted to the claimed throughput gains. Algorithm 3's 1/2 approximation is proved by a self-contained argument in Theorem 1 from Propositions 4 and 5, and the experiments compare against exhaustive search and AD-/SD-only baselines on the same explicit model, which is a consistency check rather than a prediction forced by construction. The only same-author citation ([2], a survey) is used as background and is not load-bearing. The internally inconsistent memory accounting between Eq. (15) (per-user output length L_O^k) and Eq. (27) (uniform L2) identified by the skeptic is a correctness issue in the proof of Proposition 4, not a circular dependency, and therefore does not raise the circularity score.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

No new physical entities are introduced. The framework's component (simultaneous AD and SD on one GPU) is a system architecture, not a new force, particle, or conserved quantity.

free parameters (2)
  • alpha_L, beta_L, alpha_S, beta_S = offsets and slopes in the linear latency model, not reported
    Used in all latency computations in Section III-F and in the experiments; measured offline on the RTX 4090 but values are not given.
  • acceptance rate A_k = set within [0.6, 0.8] in experiments
    Assumed known per user; drives the SD iteration count N_k in Eq. (9) and all SD latency and memory estimates. No measurement or model is provided for how A_k depends on task content.
assumptions (5)
  • domain assumption Total inference latency is a linear function of workload divided by the allocated GPU fraction, plus a fixed overhead (tau = alpha W / z + beta).
    Invoked in Section III-F for both AD and SD; real GPU resource partitioning (e.g., MIG) does not give perfectly linear speedup.
  • domain assumption The workload of a batched forward pass is the sum of per-user workloads, with padding to the maximum input/output length in the batch.
    Used in Eqs. (4)-(6) and (26); this additive model ignores parallelization and memory-bandwidth effects in real batched inference.
  • domain assumption KV cache memory is the only dynamic memory cost beyond model weights, and peak SD memory is the sum of SLM and LLM KV caches.
    Stated in Section III-E; activation memory, temporary buffers, and fragmentation are not modeled.
  • domain assumption The number of accepted draft tokens follows the geometric-series formula L_A = A(1 - A^{L_D}) / (1 - A) with constant A.
    Used in Section III-D; real acceptance rates are position-dependent and correlated, so this is an idealization.
  • ad hoc to paper The discrete sets Z and Lambda contain all resource splits that any optimal solution might use.
    Proposition 2 (equivalence of P1 and P2) requires that every feasible memory split is representable by some lambda in Lambda; the paper asserts this without characterizing the required granularity.

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Pith. "Pith review of BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks." pith.science (2026). https://pith.science/paper/X7IMXQ4K

@misc{pith2026260805926,
  author       = {Pith},
  title        = {Pith review of: BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X7IMXQ4K}},
  note         = {Machine review of arXiv:2608.05926}
}
read the original abstract

Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: Autoregressive decoding (AD) generates output tokens sequentially, resulting in long latency; Speculative decoding (SD) accelerates inference by using a small language model (SLM) to generate multiple draft tokens for LLM verification, but incurs extra memory costs. Due to this latency-memory tradeoff, neither approach alone can efficiently serve users with heterogeneous demands under limited edge computing resources. To address this challenge, we propose a hybrid autoregressive-speculative inference (BALANCE) framework for edge LLM inference. In BALANCE, an edge server hosts both an SLM and an LLM, assigns each user to AD or SD, and performs the two modes simultaneously. To maximize the number of served users, we formulate a task throughput maximization problem to jointly determine user scheduling and computing resource allocation between AD and SD under user latency requirements and server memory constraints. Since the problem is NP-hard, we develop a polynomial-time algorithm that transforms the original problem into two sub-problems and obtains a sub-optimal solution with a constant approximation guarantee. Experiments demonstrate that BALANCE consistently outperforms conventional AD and SD and significantly improves task throughput.

Figures

Figures reproduced from arXiv: 2608.05926 by the authors.

Figure 1
Figure 1. Comparison between AD and SD when serving a single user using [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Each user k ∈ K generates an inference task with an E2E latency requirement Tk and requests the edge server to perform inference by uploading the task data to the edge server. The edge server hosts both an LLM and an SLM and simultaneously supports the AD mode, where the LLM generates output tokens autoregressively, and the SD mode, where the SLM generates draft tokens and the LLM verifies them. To serve heterogeneo… view at source ↗
Figure 2
Figure 2. Illustration of BALANCE, where an edge server simultaneously [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figures from the paper (3 more)
Figure 3
Figure 3. Figure 3: The procedure of the proposed BALANCE framework. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png]
Figure 6
Figure 6. Figure 6: Comparisons of algorithm running time and task throughput between [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Ablation study results for the proposed algorithm. The system [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 36 canonical work pages

  1. [1]

    Network edge inference for large language models: Principles, tech- niques, and opportunities,

    Z. Chen, B. Zhu, J. Wang, H. Shin, A. Nallanathan, and D. T. Niyato, “Network edge inference for large language models: Principles, tech- niques, and opportunities,”ACM Comput. Surv., vol. 58, no. 12, pp. 1–35, May 2026

    work page 2026

  2. [2]

    Mobile edge intelligence for large language models: A contemporary survey,

    G. Qu, Q. Chen, W. Wei, Z. Lin, X. Chen, and K. Huang, “Mobile edge intelligence for large language models: A contemporary survey,”IEEE Commun. Surveys Tuts., vol. 27, no. 6, pp. 3820–3860, Dec. 2025

    2025

  3. [3]

    QLLMS: Quantization-adaptive LLM scheduling for partially informed edge serving systems,

    M. Hu, Q. He, and D. Wu, “QLLMS: Quantization-adaptive LLM scheduling for partially informed edge serving systems,” inProc. IEEE Int. Conf. Comput. Commun. (INFOCOM), May 2025, pp. 1–10

    work page 2025

  4. [4]

    Edge-first language model inference: Models, metrics, and tradeoffs,

    S. Jang and R. Morabito, “Edge-first language model inference: Models, metrics, and tradeoffs,” inProc. IEEE 45th Int. Conf. Distrib. Comput. Syst. Workshops (ICDCSW), Jul. 2025, pp. 309–314

    work page 2025

  5. [5]

    Large language model partitioning for low-latency inference at the edge,

    D. Kafetzis, R. Khalili, and I. Koutsopoulos, “Large language model partitioning for low-latency inference at the edge,” inProc. 23rd Int. Symp. Model. Optim. Mobile, Ad Hoc, Wireless Netw. (WiOpt), Aug. 2025, pp. 1–8

    work page 2025

  6. [6]

    Taming throughput-latency tradeoff in LLM inference with sarathi-serve,

    A. Agrawal, N. Kedia, A. Panwar, J. Mohan, N. Kwatra, B. S. Gulavani, A. Tumanov, and R. Ramjee, “Taming throughput-latency tradeoff in LLM inference with sarathi-serve,” inProc. 18th USENIX Conf. Oper. Syst. Des. Implement. (OSDI), Jul. 2024

    work page 2024

  7. [7]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” inProc. Adv. Neural Inform. Process. Syst. (NeurIPS), Long Beach, CA, USA, Dec. 2017, pp. 5998–6008

    work page 2017

  8. [8]

    FFN-SkipLLM: A hidden gem for autoregressive decoding with adap- tive feed forward skipping,

    A. K. Jaiswal, B. Hu, L. Yin, Y . Ro, T. Chen, S. Liu, and A. Akella, “FFN-SkipLLM: A hidden gem for autoregressive decoding with adap- tive feed forward skipping,” inProc. Conf. Empirical Methods Natural Lang. Process. (EMNLP), Miami, Florida, USA, Nov. 2024, pp. 16 943– 16 956

    work page 2024

Show all 42 references
  1. [9]

    Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding,

    H. Xia, Z. Yang, Q. Dong, P. Wang, Y . Li, T. Ge, T. Liu, W. Li, and Z. Sui, “Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding,” inProc. Findings Assoc. Comput. Linguistics: ACL 2024, Aug. 2024, pp. 7655–7671

    2024

  2. [10]

    A theoretical perspective for speculative decoding algorithm,

    M. Yin, M. Chen, K. Huang, and M. Wang, “A theoretical perspective for speculative decoding algorithm,” inProc. Adv. Neural Inform. Process. Syst. (NeurIPS), vol. 37, Dec. 2024, pp. 128 082–128 117

    2024

  3. [11]

    Decoding speculative decoding,

    M. Yan, S. Agarwal, and S. Venkataraman, “Decoding speculative decoding,” inProc. Conf. Nations Americas Chapter Assoc. Comput. Linguistics: Human Lang. Technol., Albuquerque, New Mexico, Apr. 2025, pp. 6460–6473

    2025

  4. [12]

    SpecExec: Massively parallel speculative decoding for interactive LLM inference on consumer devices,

    R. Svirschevski, A. May, Z. Chen, B. Chen, Z. Jia, and M. Ryabinin, “SpecExec: Massively parallel speculative decoding for interactive LLM inference on consumer devices,” inProc. Adv. Neural Inform. Process. Syst. (NeurIPS), vol. 37, Dec. 2024, pp. 16 342–16 368

    2024

  5. [13]

    QuantSpec: Self-speculative decoding with hierarchical quantized KV cache,

    R. Tiwari, H. Xi, A. Tomar, C. R. C. Hooper, S. Kim, M. Horton, M. Najibi, M. W. Mahoney, K. Keutzer, and A. Gholami, “QuantSpec: Self-speculative decoding with hierarchical quantized KV cache,” in Proc. 42nd Int. Conf. Mach. Learn. (ICML), Jul. 2025, pp. 59 668– 59 686

    2025

  6. [14]

    Draft & verify: Lossless large language model acceleration via self- speculative decoding,

    J. Zhang, J. Wang, H. Li, L. Shou, K. Chen, G. Chen, and S. Mehrotra, “Draft & verify: Lossless large language model acceleration via self- speculative decoding,” inProc. 62nd Annu. Meeting Assoc. Comput. Linguistics (ACL), Bangkok, Thailand, Aug. 2024, pp. 11 263–11 282

    2024

  7. [15]

    SWIFT: On-the-fly self- speculative decoding for LLM inference acceleration,

    H. Xia, Y . Li, J. Zhang, C. Du, and W. Li, “SWIFT: On-the-fly self- speculative decoding for LLM inference acceleration,” inProc. Int. Conf. Learn. Represent. (ICLR), Apr. 2025, pp. 1–24

    2025

  8. [16]

    Edge inference for large language models with pipeline parallelism and batching,

    J. Jiang, Z. Chen, H. Shin, and A. Nallanathan, “Edge inference for large language models with pipeline parallelism and batching,”IEEE Trans. Commun., vol. 74, pp. 8390–8406, Apr. 2026

    2026

  9. [17]

    Beyond the cloud: Edge inference for generative large language models in wireless networks,

    X. Zhang, J. Nie, Y . Huang, G. Xie, Z. Xiong, J. Liu, D. Niyato, and X. Shen, “Beyond the cloud: Edge inference for generative large language models in wireless networks,”IEEE Trans. Wireless Commun., vol. 24, no. 1, pp. 643–658, Jan. 2025

    2025

  10. [18]

    Resource allocation in large language model integrated 6G vehicular networks,

    C. Liu and J. Zhao, “Resource allocation in large language model integrated 6G vehicular networks,” inProc. IEEE 99th Veh. Technol. Conf. (VTC), Sep. 2024, pp. 1–6

    2024

  11. [19]

    Communication- efficient distributed on-device LLM inference over wireless networks,

    K. Zhang, H. He, S. Song, J. Zhang, and K. B. Letaief, “Communication- efficient distributed on-device LLM inference over wireless networks,” IEEE J. Sel. Topics Signal Process., vol. 19, no. 7, pp. 1301–1317, Oct. 2025

    2025

  12. [20]

    Efficient LLM inference over heterogeneous edge networks with speculative decoding,

    B. Zhu, Z. Chen, L. Zhao, H. Shin, and A. Nallanathan, “Efficient LLM inference over heterogeneous edge networks with speculative decoding,” arXiv preprint arXiv:2510.11331, 2025

    2025

  13. [21]

    DiP-SD: Distributed pipelined speculative decoding for efficient LLM inference at the edge,

    Y . Xu, S. Zhou, and Z. Niu, “DiP-SD: Distributed pipelined speculative decoding for efficient LLM inference at the edge,”arXiv preprint arXiv:2604.20919, 2026

    2026 arXiv

  14. [22]

    SLED: A speculative LLM decoding framework for efficient edge serving,

    X. Li, D. Spatharakis, S. Ghafouri, J. Fan, H. Vandierendonck, D. John, B. Ji, and D. S. Nikolopoulos, “SLED: A speculative LLM decoding framework for efficient edge serving,” inProc. 10th ACM/IEEE Symp. Edge Comput. (SEC), Dec. 2025

    2025

  15. [23]

    OPT-tree: Speculative decoding with adaptive draft tree structure,

    J. Wang, Y . Su, J. Li, Q. Xia, Z. Ye, X. Duan, Z. Wang, and M. Zhang, “OPT-tree: Speculative decoding with adaptive draft tree structure,” Transactions of the Association for Computational Linguistics, vol. 13, pp. 188–199, Feb. 2025

    2025

  16. [24]

    PEARL: Parallel speculative decoding with adaptive draft length,

    T. Liu, Y . Li, Q. Lv, K. Liu, J. Zhu, W. Hu, and X. Sun, “PEARL: Parallel speculative decoding with adaptive draft length,” inProc. Int. Conf. Learn. Represent. (ICLR), vol. 2025, May 2025, pp. 1085–1104

    2025

  17. [25]

    Think fast, infer smart: A hybrid distributed LLMs inference at the wireless edge,

    A. Albaseer, E. Bentafat, M. Hamood, M. Abdallah, A. Al-Fuqaha, and M. Hamdi, “Think fast, infer smart: A hybrid distributed LLMs inference at the wireless edge,” inProc. IEEE 36th Int. Symp. Pers., Indoor Mobile Radio Commun. (PIMRC), Sep. 2025, pp. 1–6

    2025

  18. [26]

    Hybrid LLM: Cost-efficient and quality-aware query routing,

    D. Ding, A. Mallick, C. Wang, R. Sim, S. Mukherjee, V . Ruhle, L. V . Lakshmanan, and A. H. Awadallah, “Hybrid LLM: Cost-efficient and quality-aware query routing,” inProc. Int. Conf. Learn. Represent. (ICLR), Vienna Austria, May 2024, pp. 1–19

    2024

  19. [27]

    Communication-efficient hybrid language model via uncertainty-aware opportunistic and compressed transmission,

    S. Oh, J. Kim, J. Park, S.-W. Ko, J. Choi, T. Q. Quek, and S.-L. Kim, “Communication-efficient hybrid language model via uncertainty-aware opportunistic and compressed transmission,”IEEE Trans. Commun., early access 2026

    2026

  20. [28]

    Jakiro: Boosting speculative decoding with decoupled multi-head via MoE,

    H. Huang, F. Yang, Z. Liu, Y . Xu, J. Li, Y . Liu, X. Yin, D. Li, P. Ren, and E. Barsoum, “Jakiro: Boosting speculative decoding with decoupled multi-head via MoE,”arXiv preprint arXiv:2502.06282, 2025

    2025 arXiv

  21. [29]

    When, what, and how: Rethinking retrieval-enhanced speculative decoding,

    M. Fang, Z. Fu, Q. Zhao, and J. Wang, “When, what, and how: Rethinking retrieval-enhanced speculative decoding,”arXiv preprint arXiv:2511.01282, 2025

    2025

  22. [30]

    ExeGPT: Constraint-aware resource scheduling for LLM inference,

    H. Oh, K. Kim, J. Kim, S. Kim, J. Lee, D.-s. Chang, and J. Seo, “ExeGPT: Constraint-aware resource scheduling for LLM inference,” in in Proc. 29th ACM Int. Conf. Archit. Support Program. Lang. Operating Syst. (ASPLOS), Apr. 2024, p. 369–384

    2024

  23. [31]

    Efficient interactive LLM serving with proxy model-based sequence length prediction,

    H. Qiu, W. Mao, A. Patke, S. Cui, S. Jha, C. Wang, H. Franke, Z. T. Kalbarczyk, T. Bas ¸ar, and R. K. Iyer, “Efficient interactive LLM serving with proxy model-based sequence length prediction,” inProc. 5th Int. Workshop Cloud Intell./AIOps, vol. 5, San Diego, CA, USA, Apr. 20...

    2024

  24. [32]

    Past-future scheduler for LLM serving under SLA guarantees,

    R. Gong, S. Bai, S. Wu, Y . Fan, Z. Wang, X. Li, H. Yang, and X. Liu, “Past-future scheduler for LLM serving under SLA guarantees,” in Proc. 30th ACM Int. Conf. Archit. Support Program. Lang. Oper. Syst. (ASPLOS), Mar. 2025, pp. 798–813

    2025

  25. [33]

    Orca: A distributed serving system for{Transformer-Based}generative models,

    G.-I. Yu, J. S. Jeong, G.-W. Kim, S. Kim, and B.-G. Chun, “Orca: A distributed serving system for{Transformer-Based}generative models,” inProc. 16th USENIX Symp. Operating Syst. Design Implement. (OSDI), Jul. 2022, pp. 521–538

    2022

  26. [34]

    ColBERT: Efficient and effective passage search via contextualized late interaction over BERT,

    O. Khattab and M. Zaharia, “ColBERT: Efficient and effective passage search via contextualized late interaction over BERT,” inProc. 43rd Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval (SIGIR), Mar. 2020, pp. 39–48

    2020

  27. [35]

    Reference API

    PyTorch, “Reference API.” [Online]. Available: https://docs.pytorch. org/docs/stable/generated/torch.nn.utils.rnn.pad sequence.html

  28. [36]

    FlexGen: High-throughput generative inference of large language models with a single GPU,

    Y . Sheng, L. Zheng, B. Yuan, Z. Li, M. Ryabinin, B. Chen, P. Liang, C. R ´e, I. Stoica, and C. Zhang, “FlexGen: High-throughput generative inference of large language models with a single GPU,” inProc. 40th Int. Conf. Mach. Learn. (ICML), Honolulu, Hawaii, USA, Jul. 2023, pp....

    2023

  29. [37]

    NVIDIA multi-instance GPU user guide release r580,

    NVIDIA, “NVIDIA multi-instance GPU user guide release r580,”

  30. [38]

    iGniter: Interference-aware GPU resource provisioning for predictable DNN inference in the cloud,

    F. Xu, J. Xu, J. Chen, L. Chen, R. Shang, Z. Zhou, and F. Liu, “iGniter: Interference-aware GPU resource provisioning for predictable DNN inference in the cloud,”IEEE Trans. Parallel Distrib. Syst., vol. 34, no. 3, pp. 812–827, Mar. 2023

    2023

  31. [39]

    Multiuser co- inference with batch processing capable edge server,

    W. Shi, S. Zhou, Z. Niu, M. Jiang, and L. Geng, “Multiuser co- inference with batch processing capable edge server,”IEEE Trans. Wireless Commun., vol. 22, no. 1, pp. 286–300, Jan. 2023

    2023

  32. [40]

    A fast and scal- able multidimensional multiple-choice knapsack heuristic,

    H. Shojaei, T. Basten, M. Geilen, and A. Davoodi, “A fast and scal- able multidimensional multiple-choice knapsack heuristic,”ACM Trans. Design Autom. Electron. Syst., vol. 18, no. 4, pp. 1–32, Oct. 2013

    2013

  33. [41]

    Heuristic algorithms for the multiple-choice multidimensional knapsack problem,

    M. Hifi, M. Michrafy, and A. Sbihi, “Heuristic algorithms for the multiple-choice multidimensional knapsack problem,”J. Oper. Res. Soc., vol. 55, no. 12, pp. 1323–1332, Jul. 2004

    2004

  34. [2025]

    Available: https://docs.nvidia.com/datacenter/tesla/pdf/ MIG User Guide.pdf

    [Online]. Available: https://docs.nvidia.com/datacenter/tesla/pdf/ MIG User Guide.pdf

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.