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Efficient Large Language Model Inference with Neural Block Linearization

large-language-models › model-architecture

📄 Abstract

Abstract: The high inference demands of transformer-based Large Language Models (LLMs) pose substantial challenges in their deployment. To this end, we introduce Neural Block Linearization (NBL), a novel framework for accelerating transformer model inference by replacing self-attention layers with linear approximations derived from Linear Minimum Mean Squared Error estimators. NBL leverages Canonical Correlation Analysis to compute a theoretical upper bound on the approximation error. Then, we use this bound as a criterion for substitution, selecting the LLM layers with the lowest linearization error. NBL can be efficiently applied to pre-trained LLMs without the need for fine-tuning. In experiments, NBL achieves notable computational speed-ups while preserving competitive accuracy on multiple reasoning benchmarks. For instance, applying NBL to 12 self-attention layers in DeepSeek-R1-Distill-Llama-8B increases the inference speed by 32% with less than 1% accuracy trade-off, making it a flexible and promising solution to improve the inference efficiency of LLMs. The implementation is available at: https://github.com/LIONS-EPFL/NBL.

Authors (3)

Mete Erdogan

Francesco Tonin

Volkan Cevher

Submitted

May 27, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

Introduces Neural Block Linearization (NBL), a novel framework that accelerates transformer LLM inference by replacing self-attention layers with linear approximations derived from LMMSE. NBL uses CCA to bound approximation error and selects layers for substitution based on this error, achieving significant speed-ups (e.g., 32% for DeepSeek-R1-Distill-Llama-8B) with minimal accuracy loss (<1%). Crucially, it works on pre-trained LLMs without fine-tuning.

Business Value

Significantly reduces the cost and latency of deploying LLMs, making them more accessible for real-time applications and on devices with limited computational power. This broadens the market for LLM-powered services.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High. NBL is applied to pre-trained models without fine-tuning, making integration straightforward. The linear approximations are computationally efficient.

Limitations Addressed

High inference demands of transformer-based LLMs,Computational cost and latency of LLM deployment,Need for fine-tuning to achieve efficiency gains

Performance Gains

32% inference speed increase on DeepSeek-R1-Distill-Llama-8B,<1% accuracy trade-off

Technical Tags

LLM inferenceneural network accelerationlinear approximationself-attentiontransformer modelscomputational speed-upaccuracy preservationCanonical Correlation AnalysisDeepSeek-R1-Distill-Llama-8B

Research Topics

Efficient LLM InferenceModel Compression and AccelerationTransformer Architecture Optimization

Methods & Architectures

Neural Block Linearization (NBL)Linear Minimum Mean Squared Error (LMMSE) estimatorsCanonical Correlation Analysis (CCA) Transformer-based LLMsSelf-attention layers

Applications & Tasks

LLM deployment Edge AI Resource-constrained environments High inference latency in LLMsHigh computational cost of LLM deploymentBalancing speed and accuracy in LLM inference Accelerating transformer model inferenceReducing computational demands of LLMsEnabling LLM deployment on devices with limited resources

Datasets & Benchmarks

Benchmarks

DeepSeek-R1-Distill-Llama-8B (32% speed-up, <1% accuracy loss)

Inference speedAccuracy

Related Fields

Machine LearningDeep LearningNatural Language ProcessingComputer Architecture

Keywords

LLMinferenceefficiencyaccelerationtransformerself-attentionlinearizationNBLCCALMMSEcomputational costlatencyDeepSeek

Academic Context

#Efficient LLM Inference#Model Compression and Acceleration#Transformer Architecture Optimization

Commercial Potential

Potential Products

Optimized LLM inference enginesLLM deployment SDKsHardware-aware LLM optimization tools

Target Industries

TechnologySaaSCloud ComputingMobile Devices

Use Case Examples

Enabling real-time conversational AI on mobile devices.Reducing server costs for large-scale LLM API deployments.Deploying LLMs for on-device translation or summarization.

Competitive Edge

Offers a method for inference acceleration that does not require fine-tuning, differentiating it from quantization or pruning techniques that often necessitate retraining.

Market Opportunity

Massive market for efficient LLM deployment and inference solutions.

Revenue Models

Licensing the NBL technologyoffering optimized LLM inference servicesdeveloping specialized hardware/software co-design.

Resource Requirements

Compute Needs

Reduced inference compute compared to standard transformers, but training/application of NBL might require significant compute.

Data Requirements

Requires pre-trained LLMs; no specific training datasets mentioned for NBL itself.

Deployment Constraints

The approximation error needs to be carefully managed. May not be suitable for all LLM layers or architectures.

Scalability

The linearization approach is inherently scalable as it simplifies complex operations.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into inference frameworks.

Patent Potential

High, for the NBL framework and its application.

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