arxiv_cl 95% Match Research Paper ML Engineers,AI Researchers,Inference Optimization Specialists,Developers of LLM applications 3 weeks ago

3-Model Speculative Decoding

large-language-models › model-architecture

📄 Abstract

Abstract: Speculative Decoding (SD) accelerates inference in large language models by using a smaller draft model to propose tokens, which are then verified by a larger target model. However, the throughput gains of SD are fundamentally limited by a trade-off between draft model size and token acceptance: smaller draft models generate tokens more quickly but exhibit greater divergence from the target model, resulting in lower acceptance rates and reduced speedups. We introduce Pyramid Speculative Decoding (PyramidSD), an extension of SD that inserts an intermediate qualifier model between the draft and target to bridge the distributional gap in output predictions, allowing smaller model to be used for drafting. This hierarchical decoding strategy improves alignment across models, enabling higher acceptance rates and allowing the use of significantly smaller draft models without sacrificing overall performance. PyramidSD builds on fuzzy acceptance criteria to support relaxed divergence thresholds at each stage, improving throughput. In experiments, PyramidSD achieves up to 1.91x generation speed over standard SD, reaching 124 tokens per second on a consumer GPU (RTX 4090). In small-memory settings with a 1B-parameter draft model and an 8B target model, PyramidSD minimally trades target model quality for improved throughput. Overall, PyramidSD offers a practical approach to enhancing speculative decoding efficiency and can be readily applied to existing inference pipelines.

Authors (6)

Sanghyun Byun

Mohanad Odema

Jung Ick Guack

Baisub Lee

Jacob Song

Woo Seong Chung

Submitted

October 14, 2025

arXiv Category

cs.CL

arXiv PDF

Key Contributions

Introduces Pyramid Speculative Decoding (PyramidSD), an extension of Speculative Decoding that uses an intermediate qualifier model to bridge the distributional gap between a small draft model and a large target model. This hierarchical strategy improves token acceptance rates, allows the use of significantly smaller draft models without sacrificing performance, and enhances overall inference throughput.

Business Value

Significantly reduces the computational cost and latency of LLM inference, making large models more practical and cost-effective for real-time applications and high-volume deployments.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

High, as it's an optimization technique for existing LLM inference pipelines.

Limitations Addressed

Throughput limitations of standard Speculative Decoding,Trade-off between draft model size and token acceptance rate,Divergence between draft and target models,Performance degradation when using very small draft models

Performance Gains

Enables higher acceptance rates and allows use of significantly smaller draft models without sacrificing overall performance, leading to improved throughput.

Technical Tags

Speculative DecodingLarge Language Models (LLMs)Inference AccelerationDraft ModelTarget ModelToken Acceptance RatePyramid Speculative Decoding (PyramidSD)Intermediate Qualifier ModelDistributional GapFuzzy Acceptance Criteria

Research Topics

Improving the efficiency of LLM inferenceAccelerating token generation in LLMsBridging distributional gaps between modelsOptimizing speculative decoding strategies

Methods & Architectures

Pyramid Speculative Decoding (PyramidSD)Introduction of an Intermediate Qualifier ModelHierarchical Decoding StrategyFuzzy Acceptance Criteria Large Language Models (LLMs)Draft ModelTarget ModelIntermediate Qualifier Model

Applications & Tasks

Natural Language Generation AI Inference Optimization Real-time AI Applications Throughput limitations in Speculative DecodingTrade-off between draft model size and token acceptanceDivergence between draft and target modelsInefficiency of small draft models Text GenerationInference Acceleration

Related Fields

Natural Language ProcessingMachine LearningComputer ArchitectureAI OptimizationInference Engineering

Keywords

Speculative DecodingPyramidSDLLM InferenceInference AccelerationDraft ModelTarget ModelToken GenerationThroughputEfficiencyIntermediate ModelDistributional Gap

Academic Context

#Improving the efficiency of LLM inference#Accelerating token generation in LLMs#Bridging distributional gaps between models#Optimizing speculative decoding strategies

Commercial Potential

Potential Products

Optimized LLM inference enginesLibraries for faster LLM deployment

Target Industries

TechnologyCloud ComputingSaaSAI Services

Use Case Examples

Deploying large language models for real-time chatbots with lower latency and cost.Accelerating content generation services powered by LLMs.

Competitive Edge

Offers a more effective approach to speculative decoding by introducing an intermediate model, overcoming the limitations of the draft-target trade-off in existing methods.

Market Opportunity

Large and growing market for efficient LLM deployment solutions.

Revenue Models

Licensing of the PyramidSD technologyintegration into inference platforms.

Resource Requirements

Compute Needs

Moderate, as it optimizes inference, potentially reducing compute needs compared to non-speculative methods.

Data Requirements

Requires pre-trained LLMs (draft and target models).

Deployment Constraints

Requires careful implementation and tuning of the three-model system.

Scalability

Designed to improve scalability of LLM inference by increasing throughput.

Production Readiness

Maturity Level

Research

Time to Market

6-12 months

Patent Potential

Moderate, for the PyramidSD algorithm and the use of intermediate models.

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