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Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments

Yuzhe Yang, Yipeng Du, Ahmad Farhan, Claudio Angione, Yue Zhao, Harry Yang, Fielding Johnston, James Buban, Patrick Colangelo

arXiv:2410.21340v1cs.LGcs.AIcs.DC

TL;DR

Decentralized large-model inference needs acceleration choices that account for heterogeneous tasks and system constraints. The paper introduces MetaInf, a meta-learning framework that uses historical performance data to select methods dynamically. The framework is reported to outperform traditional selection strategies in efficiency and performance while supporting scalable, cost-effective deployment.

  • Problem

    Heterogeneous decentralized environments make it difficult to select the best acceleration method for each model, dataset, and hardware configuration without costly evaluations.

    Source: §1.1 · p. 3, §4.1 · p. 7

  • Method

    MetaInf transfers acceleration-performance information from historical tasks to predict suitable methods for new inference tasks and environments.

    Source: §1.1 · p. 3, §4.1 · p. 7

  • Results

    The framework surpasses traditional acceleration methods in efficiency and performance across diverse decentralized deployment settings.

    Source: §1.1 · p. 3, §5 · p. 11

  • Takeaways & Limitations

    Meta-learning can support scalable and cost-effective large-model inference across distributed environments.

    Source: §5 · p. 11

    The experiments excluded speculative decoding and quantization because of hardware and time limitations.

    Source: §2.3 · p. 6

Abstract

from arXiv · show

The deployment of large-scale models, such as large language models (LLMs) and sophisticated image generation systems, incurs substantial costs due to their computational demands. To mitigate these costs and address challenges related to scalability and data security, there is a growing shift towards decentralized systems for deploying such models. In these decentralized environments, efficient inference acceleration becomes crucial to manage computational resources effectively and enhance system responsiveness. In this work, we address the challenge of selecting optimal acceleration methods in decentralized systems by introducing a meta-learning-based framework. This framework automates the selection process by learning from historical performance data of various acceleration techniques across different tasks. Unlike traditional methods that rely on random selection or expert intuition, our approach systematically identifies the best acceleration strategies based on the specific characteristics of each task. We demonstrate that our meta-learning framework not only streamlines the decision-making process but also consistently outperforms conventional methods in terms of efficiency and performance. Our results highlight the potential of meta-learning to revolutionize inference acceleration in decentralized AI systems, offering a path towards more democratic and economically feasible artificial intelligence solutions.

1 INTRODUCTION

Large-model deployment motivates decentralized architectures, but heterogeneous resources make acceleration-method selection difficult. The paper extends BSNS with MetaInf, which uses historical performance data to select methods dynamically for decentralized inference.

1 INTRODUCTION

  • Decentralized architectures distribute model computation across nodes to improve scalability, latency, privacy, and resource availability.BSNS distributes model blocks across consumers and supports inference for users with limited local computing power.Source: §1 · p. 1, §1.1 · pp. 1–2

1.1 NESA’S SYSTEM OVERVIEW

  • BSNS routes model blocks across selected consumers, dynamically updating assignments using hardware and reputation features.Its message-queuing system prioritizes tasks by computational demand, response requirements, hardware, geography, and node reliability.Source: §1.1 · pp. 2–3
  • Heterogeneous and constrained nodes require adaptive selection of acceleration methods based on task characteristics and system conditions.The paper identifies this as the central challenge of fast inference in decentralized systems.Source: §1.1 · p. 3
  • MetaInf learns from historical performance data to predict effective acceleration strategies across decentralized operating scenarios.It extends BSNS and aims to improve efficiency, performance, scalability, and cost-effectiveness over random or expert-driven selection.Source: §1.1 · p. 3

2 A CLOSER LOOK AT FAST INFERENCE

The paper surveys acceleration techniques for language and text-to-image models, then evaluates several methods under heterogeneous hardware settings. Continuous batching and prefix caching improve throughput substantially, while scaling GPUs eventually yields diminishing returns.

2.1 LANGUAGE MODELS

  • Continuous batching interleaves incoming requests at token level with prefill and decoding to improve utilization, throughput, and latency.It addresses waiting costs introduced by static batching.Source: §2.1 · p. 4
  • Prefilling precomputes KV-cache states for input tokens, reducing generation-time computation and latency, especially for long inputs.Prompt caching similarly reuses outputs for repeated prompt prefixes to avoid redundant computation.Source: §2.1 · p. 4
  • Speculative decoding uses a smaller auxiliary model to propose token sequences that a larger model confirms or refines.The paper focuses on speculative decoding through vLLM.Source: §2.1 · p. 4

2.2 TEXT-TO-IMAGE MODELS

  • Text-to-image acceleration includes distilled models, reduced precision, optimized attention kernels, combined projections, and dynamic quantization.Distilled Stable Diffusion provides up to ∼1.5X speedup, rising to ∼1.6X with a distilled autoencoder.Source: §2.2 · p. 5

2.3 IMPLEMENTATION

  • The evaluation excluded speculative decoding and quantization because of hardware and time limitations.Tested methods included continuous batching, prefix caching, chunked prefill, and their combined configuration.Source: §2.3 · p. 6
  • Prefix caching reached up to 20X inference speed gains, while continuous batching reduced latency by up to 13X versus baseline.Across tested models, these methods produced marked throughput improvements.Source: §2.3 · p. 6
  • Moving from 4 GPUs to 8 GPUs produced only a 5% gain, indicating diminishing returns from communication and memory-bandwidth bottlenecks.The experiments used ShareGPT, NVIDIA L4 GPUs, several language models, and outputs limited to 10 tokens.Source: §2.3 · pp. 5–6

3 FAST INFERENCE IN HETEROGENEOUS SYSTEMS

Fast-inference performance varies across heterogeneous hardware, batch sizes, and workload-distribution strategies. These observations motivate a meta-scheduler that predicts suitable methods and configurations from input and system state.

Conceptualization

  • Heterogeneous hardware, network latency, and dynamic load require scheduling based on hardware, throughput, and network status.Decentralized systems must accommodate differing computational power, memory limits, and communication overheads.Source: §3 · p. 6

Observations from the Motivating Example

  • For smaller batches, combining all tested techniques performed best, whereas larger batches favored single-method strategies.The motivating example shows that acceleration gains become less predictable as batch sizes vary.Source: §3 · p. 6

Interpretation

  • The observed variability motivates a learnable meta-scheduler that predicts acceleration methods and hardware configurations from input data and current system status.The proposed adaptive framework is intended for fast inference across distributed systems.Source: §3 · p. 7

4 META-LEARNING FOR INFERENCE ACCELERATION OPTIMIZATION

MetaInf selects inference acceleration methods for new decentralized tasks by transferring performance knowledge from historical tasks across datasets, models, and hardware. It combines offline embedding-based meta-training with online, budget-constrained method selection.

4.1 PROBLEM STATEMENT AND FRAMEWORK OVERVIEW

  • MetaInf addresses cost-constrained acceleration selection across heterogeneous datasets, models, and hardware environments for new inference tasks.The selected method must satisfy the deployment budget while accounting for hardware-specific computational constraints and capabilities.Source: §4.1 · pp. 7–8
  • The framework learns historical performance mappings offline, then applies them online to select an acceleration method for a new task economically.Its two phases are offline meta-training on Dtrain and online method selection for new tasks.Source: §4.1 · pp. 7–8

4.2 OFFLINE META-TRAINING

  • Offline training encodes dataset, method, and hardware characteristics and trains a regression-based meta-predictor to estimate performance metrics such as runtime, energy usage, and accuracy.Historical combinations are represented through Edata, Emodel, and Ehardware and mapped to observed performance P.Source: §4.2 · pp. 8–9

4.2.1 DATA, MODEL, AND HARDWARE EMBEDDINGS

  • LLM-based embeddings replace manual, heuristic meta-features with dense representations of nuanced dataset, model, and hardware characteristics.The approach uses textual descriptions processed by models such as BERT or GPT to capture characteristics relevant to acceleration selection.Source: §4.2.1 · pp. 9–10

4.3 ONLINE MODEL SELECTION

  • Online selection embeds the new dataset, model, and hardware, predicts each acceleration method’s performance, and retains methods whose calculated cost does not exceed budget b.The cost Cj,k is the product of hardware cost and expected runtime; the method with highest predicted performance within budget is returned.Source: §4.3 · pp. 10–11

5 CONCLUSION

The paper concludes that MetaInf improves inference acceleration in decentralized systems by selecting methods dynamically for task and architecture characteristics. Its successful application supports faster, more efficient, scalable, and cost-effective deployment across distributed environments.

5 CONCLUSION

  • MetaInf dynamically selects acceleration methods tailored to task characteristics and system architecture, improving adaptability and effectiveness in decentralized inference.The framework uses historical performance data to guide method selection.Source: §5 · p. 11
  • The framework surpasses traditional acceleration methods in efficiency and performance while offering scalable, cost-effective deployment across diverse distributed environments.Source: §5 · p. 11
  • MetaInf enables rapid, efficient inference that may support broader adoption and democratization of advanced AI technologies in decentralized settings.Source: §5 · p. 11
  • The developed methodologies provide a foundation for future research on expanding AI capabilities and improving large-model infrastructure efficiency in decentralized networks.Source: §5 · pp. 11–12

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