arxiv: 2604.09722 · v1 · submitted 2026-04-08 · 💻 cs.DC · cs.AI

Recognition: unknown

ConfigSpec: Profiling-Based Configuration Selection for Distributed Edge--Cloud Speculative LLM Serving

Xiangchen Li , Saeid Ghafouri , Jiakun Fan , Babar Ali , Hans Vandierendonck , Dimitrios S. Nikolopoulos

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:51 UTC · model grok-4.3

classification 💻 cs.DC cs.AI

keywords speculative decodingLLM servingedge-cloud computingconfiguration selectiongoodputenergy efficiencycost efficiency

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The pith

Profiling reveals conflicting optima for goodput, cost, and energy in edge-cloud speculative LLM serving.

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

ConfigSpec is a framework that profiles edge devices and draft model alignments to model throughput, acceptance rates, and power draw. It then evaluates goodput along with cost and energy efficiency over the space of draft models, quantization, speculative lengths, and device types. The resulting analysis across three platforms and two LLM families shows that goodput peaks with the smallest fastest drafter at device-specific lengths of 2 to 10, while both cost and energy efficiency converge on length 2, with cost preferring the largest drafter and energy the smallest. These structural conflicts mean no fixed configuration can optimize all three objectives at once.

Core claim

Across three edge platforms and two LLM families, goodput is maximised by the smallest, fastest draft model at device-dependent speculative lengths (K*=2-10). Both cost and energy efficiency converge to K=2 due to a dominant bonus-token effect, with cost favouring the largest drafter for its high acceptance rate and energy favouring the smallest for its low power draw. These conflicts confirm that no single fixed configuration can simultaneously optimise all objectives.

What carries the argument

ConfigSpec profiling framework that measures drafting throughput, acceptance rate, and power on target edge devices to compute goodput, verification cost efficiency, and energy efficiency across configuration choices.

If this is right

Goodput is maximised by the smallest, fastest draft model at device-dependent speculative lengths between 2 and 10.
Cost efficiency converges to speculative length 2 and favours the largest drafter due to higher acceptance rates.
Energy efficiency converges to speculative length 2 and favours the smallest drafter due to lower power draw.
No single fixed configuration optimises goodput, cost, and energy efficiency together in distributed speculative LLM serving.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Runtime adaptation that switches configurations based on observed workload could exploit the identified conflicts more effectively than static selection.
The bonus-token effect implies that small gains in acceptance rate can outweigh differences in base model size for efficiency metrics.
Extending the profiling to capture network latency between edge and cloud would allow tighter bounds on achievable efficiency gains.
Similar trade-offs are likely to appear in other disaggregated inference pipelines that separate lightweight and heavyweight components.

Load-bearing premise

The profiled metrics for drafting throughput, acceptance rate, and power, along with the derived models for goodput, cost, and energy efficiency, accurately predict real-world performance without unmodeled factors such as network latency variability or dynamic workload changes.

What would settle it

Measure actual goodput, cost, and energy for a fixed configuration chosen without the profiling step and compare the results against the framework's predictions; large discrepancies would indicate the models miss key factors.

Figures

Figures reproduced from arXiv: 2604.09722 by Babar Ali, Dimitrios S. Nikolopoulos, Hans Vandierendonck, Jiakun Fan, Saeid Ghafouri, Xiangchen Li.

**Figure 1.** Figure 1: System abstraction used by ConfigSpec. Heterogeneous edge devices generate speculative tokens using local draft models and interact with a centralized cloud verifier. Configuration profiling. For each draft model and edge platform, ConfigSpec profiles: (i) drafting throughput𝑣𝑑 , (ii) draft–target acceptance rate 𝛼 (𝐾), (iii) device power draw 𝑃. Configuration space and selection. A configuration consists… view at source ↗

**Figure 4.** Figure 4: Cost efficiency of draft models. draft tokens are produced quickly, the system must still wait for the remote server to verify them. As 𝑇verify dominates the roundtrip time, additional drafting speed yields diminishing returns on goodput. On the RPi 4B, all models above 1B fall below 1 tok/s, rendering them impractical for interactive use. These results reveal a consistent structural trend across both mod… view at source ↗

**Figure 3.** Figure 3: Verified token generation speed. relative to the fixed verification latency 𝑇verify, which is effectively amortised over more candidates. 𝑇verify has been carefully selected based on historical experiments where the considered target models have been observed taking on average 0.5s to verify tokens, and it can vary for different target models and underlying hardware. Moreover, within each device, smaller d… view at source ↗

**Figure 5.** Figure 5: Energy efficiency of draft models. 2 4 6 8 10 Verified Goodput (tok/s) = (Kα(K)+1)/(K/tps+Tverify) 0.0 2.5 5.0 7.5 J / Verified Token 5 W 10 W 20 W 40 W 60 W Llama-3.2-1B-Inst. (Q4_K_M) 1.28 J/tok Llama-3.2-1B-Inst. (Q4_K_M) 0.64 J/tok Speed–Energy Tradeoff (Target: Llama-3.1-70B, K=5) RPi 5 Jetson AGX Orin 2 4 6 8 Verified Goodput (tok/s) = (Kα(K)+1)/(K/tps+Tverify) 0 5 10 J / Verified Token 5 W 10 W 20 W… view at source ↗

**Figure 6.** Figure 6: Energy efficiency and speed (goodput) comparison. both model families, the smallest-draft model with lowest quantization bit-width on Jetson configurations occupy the Pareto-optimal corner (high goodput, low energy), while all RPi 5 configurations are Pareto-dominated by their Jetson counterparts. This architectural advantage stems from the Jetson’s GPU-accelerated inference, which exploits massive paral… view at source ↗

read the original abstract

Speculative decoding enables collaborative Large Language Model (LLM) inference across cloud and edge by separating lightweight token drafting from heavyweight verification. While prior systems show performance and cost benefits, practical deployment requires navigating a large configuration space spanning draft model variants, quantisation levels, speculative lengths, and heterogeneous edge devices. This paper presents ConfigSpec, a configurationselection framework for distributed speculative LLM serving. ConfigSpec profiles edge devices and draft-target alignment, and models drafting throughput, acceptance rate, and power to evaluate goodput, verification cost efficiency, and energy efficiency across the joint configuration space. Our analysis across three edge platforms and two LLM families reveals structurally conflicting optima. Firstly, goodput is maximised by the smallest, fastest draft model at device-dependent speculative lengths (K*=2-10). Secondly, both cost and energy efficiency converge to K=2 due to a dominant bonus-token effect-with cost favouring the largest drafter for its high acceptance rate and energy favouring the smallest for its low power draw. These conflicts confirm that no single fixed configuration can simultaneously optimise all objectives, underscoring the need for profiling-based configuration selection in disaggregated edge-cloud LLM inference.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Profiling on real edge devices shows that goodput, cost, and energy optima conflict in speculative LLM serving, so no fixed config works for all three.

read the letter

The main thing to know is that this paper finds structurally conflicting optima when you try to optimize goodput, cost, and energy together in distributed speculative LLM serving. Goodput is highest with the smallest fastest drafter at device-specific speculative lengths from 2 to 10. Cost and energy both converge on length 2, but cost likes the largest drafter for its acceptance rate while energy likes the smallest for lower power draw. The takeaway is that you need profiling to pick a config because nothing optimizes everything at once.

Referee Report

1 major / 3 minor

Summary. The manuscript introduces ConfigSpec, a profiling-based configuration selection framework for distributed speculative LLM serving across edge and cloud. It profiles edge devices to derive models of drafting throughput, acceptance rate, and power consumption, which are used to evaluate goodput, verification cost efficiency, and energy efficiency over the joint space of draft model variants, quantization levels, and speculative lengths K. Analysis across three edge platforms and two LLM families identifies structurally conflicting optima: goodput is maximized by the smallest/fastest drafter at device-dependent K* (2-10), while cost and energy efficiency both converge to K=2 (cost favoring largest drafter due to acceptance rate, energy favoring smallest due to power draw). The conclusion is that no single fixed configuration optimizes all objectives simultaneously.

Significance. If the profiled models are representative, the result is significant for practical deployment of speculative decoding in heterogeneous edge-cloud systems, as it provides empirical evidence of inherent trade-offs and motivates adaptive, profiling-driven selection over static configurations. The breadth of the evaluation across multiple platforms and model families is a clear strength, offering concrete guidance for system designers.

major comments (1)

[§5] §5 (Evaluation): The central claim of structurally conflicting optima for goodput versus cost/energy rests on the accuracy of the derived efficiency models in predicting real distributed performance. However, the models do not incorporate network latency variability between edge and cloud or dynamic workload changes, which is load-bearing for the practical validity of the identified optima and the conclusion that profiling is required.

minor comments (3)

[§4] The 'bonus-token effect' is referenced as dominant for the K=2 convergence but is not formally defined or derived with explicit equations in the modeling section; adding this would improve clarity.
[Figures] Figure legends and captions (e.g., those showing K* and efficiency curves) should more explicitly label the three edge platforms and two LLM families to aid interpretation of the cross-platform results.
The manuscript would benefit from a brief discussion of how ConfigSpec's profiling overhead compares to inference time in a production serving loop.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and for recognizing the significance of our findings on conflicting optima in distributed speculative LLM serving. We address the major comment point-by-point below.

read point-by-point responses

Referee: [§5] §5 (Evaluation): The central claim of structurally conflicting optima for goodput versus cost/energy rests on the accuracy of the derived efficiency models in predicting real distributed performance. However, the models do not incorporate network latency variability between edge and cloud or dynamic workload changes, which is load-bearing for the practical validity of the identified optima and the conclusion that profiling is required.

Authors: We thank the referee for this important observation. Our efficiency models are derived directly from profiling runs executed in the actual distributed edge-cloud testbed; consequently, the measured drafting throughput, acceptance rates, and power draw already embed the network latencies observed during those sessions. The models therefore predict performance under the real conditions captured in profiling rather than idealized zero-latency assumptions. We agree, however, that the current formulation does not explicitly parameterize network latency variability (e.g., congestion-induced fluctuations) or dynamic workload shifts, both of which could influence the location of the reported optima in highly variable production settings. In the revised manuscript we will add a dedicated limitations subsection to §5 that (i) states these modeling assumptions explicitly and (ii) presents a sensitivity study in which we inject controlled network delays and re-evaluate the goodput/cost/energy surfaces. This addition will clarify the scope of our claims while reinforcing the practical value of profiling-based selection. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper derives its central claims through direct empirical profiling of edge devices to obtain measurements of drafting throughput, acceptance rate, and power draw across draft-model variants, quantization levels, and speculative lengths K. These raw profiled quantities are then combined via standard speculative-decoding formulas to compute the three efficiency metrics (goodput, cost efficiency, energy efficiency). The reported structural conflicts among optima follow immediately from comparing the resulting values; no equation reduces an output to its own fitted inputs by construction, no parameter is presented as a prediction after being fitted to the target quantity, and no load-bearing premise rests on a self-citation chain. The derivation chain therefore remains self-contained and externally falsifiable against the profiled data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are identifiable from the abstract alone; the work appears to rest on standard performance modeling assumptions not detailed here.

pith-pipeline@v0.9.0 · 5526 in / 1454 out tokens · 50923 ms · 2026-05-10T16:51:02.864908+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 3 canonical work pages · 2 internal anchors

[1]

Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Lau- rent Sifre, and John Jumper. 2023. Accelerating large language model decoding with speculative sampling.arXiv preprint arXiv:2302.01318(2023)

work page internal anchor Pith review arXiv 2023
[2]

Mengzhao Chen, Wenqi Shao, Peng Xu, Jiahao Wang, Peng Gao, Kaipeng Zhang, and Ping Luo. 2025. Efficientqat: Efficient quantization-aware training for large language models. InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 10081–10100

2025
[3]

Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023.Free Dolly: Introducing the World’s First Truly Open Instruction- Tuned LLM.https://www.databricks.com/blog/2023/04/12/dolly-first-open- commercially-viable-instruction-tuned-llm

2023
[4]

Fireworks AI. 2025. Pricing – Fireworks AI.https://fireworks.ai/pricing. Server- less tier: $0.90 / 1M tokens for models >16B parameters. Accessed: 2025-07-15

2025
[5]

Elias Frantar and Dan Alistarh. 2023. Sparsegpt: Massive language models can be accurately pruned in one-shot. InInternational Conference on Machine Learning. PMLR, 10323–10337

2023
[6]

Groq. 2025. GroqCloud On-Demand Pricing.https://groq.com/pricing. Qwen3- 32B: $0.29 / 1M input tokens, $0.59 / 1M output tokens. Accessed: 2025-07-15

2025
[7]

Chengzhuo Han, Tingting Yang, Zhengqi Cui, and Xin Sun. 2025. A privacy- preserving and trustworthy inference framework for LLM-IoT integration via hierarchical federated collaborative computing.IEEE Internet of Things Journal (2025)

2025
[8]

Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023. Fast inference from transformers via speculative decoding. InInternational Conference on Machine Learning. PMLR, Honolulu, Hawaii, USA, 19274–19286

2023
[9]

Hui Li, Xiuhua Li, Qilin Fan, Qiang He, Xiaofei Wang, and Victor CM Leung. 2025. Adaptive model partitioning and pruning for collaborative DNN inference in mobile edge-cloud computing networks.IEEE Transactions on Mobile Computing (2025)

2025
[10]

Xiangchen Li, Jiakun Fan, Qingyuan Wang, Dimitrios Spatharakis, Saeid Ghafouri, Hans Vandierendonck, Deepu John, Bo Ji, Ali R Butt, and Dimitrios S Nikolopou- los. 2026. WISP: Waste-and Interference-Suppressed Distributed Speculative LLM Serving at the Edge via Dynamic Drafting and SLO-Aware Batching.arXiv preprint arXiv:2601.11652(2026)

work page internal anchor Pith review Pith/arXiv arXiv 2026
[11]

Xiangchen Li, Dimitrios Spatharakis, Saeid Ghafouri, Jiakun Fan, Hans Vandieren- donck, Deepu John, Bo Ji, and Dimitrios S Nikolopoulos. 2025. Sled: A speculative llm decoding framework for efficient edge serving. InProceedings of the Tenth ACM/IEEE Symposium on Edge Computing. 1–8

2025
[12]

Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Xinhao Cheng, Zeyu Wang, Rae Ying Yee Wong, Zhuoming Chen, Daiyaan Arfeen, Reyna Abhyankar, and Zhi- hao Jia. 2023. Specinfer: Accelerating generative llm serving with speculative inference and token tree verification.arXiv preprint arXiv:2305.097811, 2 (2023), 4

work page arXiv 2023
[13]

Ranajoy Sadhukhan, Jian Chen, Zhuoming Chen, Vashisth Tiwari, Ruihang Lai, Jinyuan Shi, Ian En-Hsu Yen, Avner May, Tianqi Chen, and Beidi Chen. 2024. Mag- icDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding. InInternational Conference on Learning Representa- tions

2024
[14]

Chunlin Tian, Xinpeng Qin, Kahou Tam, Li Li, Zijian Wang, Yuanzhe Zhao, Minglei Zhang, and Chengzhong Xu. 2025. CLONE: customizing LLMs for efficient latency-aware inference at the edge. InProceedings of the 2025 USENIX Conference on Usenix Annual Technical Conference(Boston, MA, USA)(USENIX ATC ’25). USENIX Association, USA, Article 34, 23 pages

2025
[15]

Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. 2023. Smoothquant: Accurate and efficient post-training quantization for large language models. InInternational Conference on Machine Learning. PMLR, 38087–38099

2023
[16]

Daliang Xu, Wangsong Yin, Hao Zhang, Xin Jin, Ying Zhang, Shiyun Wei, Meng- wei Xu, and Xuanzhe Liu. 2024. Edgellm: Fast on-device llm inference with speculative decoding.IEEE Transactions on Mobile Computing24, 4 (2024), 3256– 3273

2024
[17]

Zhongzhi Yu, Zheng Wang, Yuhan Li, Ruijie Gao, Xiaoya Zhou, Sreenidhi Reddy Bommu, Yang Zhao, and Yingyan Lin. 2024. Edge-llm: Enabling efficient large language model adaptation on edge devices via unified compression and adaptive layer voting. InProceedings of the 61st ACM/IEEE Design Automation Conference. 1–6

2024
[18]

Mingjin Zhang, Xiaoming Shen, Jiannong Cao, Zeyang Cui, and Shan Jiang. 2024. EdgeShard: Efficient LLM inference via collaborative edge computing.IEEE Internet of Things Journal(2024)

2024