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FlashEVA: Accelerating LLM inference via Efficient Attention

large-language-models › model-architecture

📄 Abstract

Abstract: Transformer models have revolutionized natural language processing, achieving state-of-the-art performance and demonstrating remarkable scalability. However, their memory demands, particularly due to maintaining full context in memory, pose significant challenges for inference. In this paper, we present FlashEVA, an efficient implementation of EVA (Efficient Attention via Control Variates), and demonstrate how to finetune transformers to adapt to FlashEVA attention. Our method enables fine-tuning of Transformer models with as few as 1.5B tokens while preserving effectiveness across various downstream tasks. Notably, FlashEVA achieves up to 6.7x higher throughput and 5x lower peak GPU memory usage during inference compared to standard Transformer implementations. Despite these improvements, we observe limitations in retrieval-focused tasks. Our implementation offers control over the trade-off between throughput and accuracy through adjustable hyperparameters, providing flexibility for diverse use cases. This work represents a significant step towards more efficient and adaptable Transformer-based models for inference.

Authors (2)

Juan Gabriel Kostelec

Qinghai Guo

Submitted

November 1, 2025

arXiv Category

cs.CL

arXiv PDF

Key Contributions

FlashEVA presents an efficient implementation of EVA attention, enabling transformer models to achieve significantly higher throughput (up to 6.7x) and lower peak GPU memory usage (5x) during inference. It also demonstrates effective fine-tuning with a smaller dataset while preserving performance, though with limitations in retrieval tasks.

Business Value

Makes large transformer models more cost-effective and practical for deployment by reducing hardware requirements and increasing processing speed, enabling wider adoption in real-time applications.

Paper Metadata

Innovation Type

Implementation and Optimization

Deployment Feasibility

High, focuses on optimizing inference for existing architectures.

Limitations Addressed

High memory demands and latency challenges of standard transformer models during inference.

Performance Gains

up to 6.7x higher throughput,5x lower peak GPU memory usage

Technical Tags

LLM inferenceefficient attentionFlashEVAtransformer modelsmemory efficiencythroughputfine-tuningEVA (Efficient Attention via Control Variates)

Research Topics

LLM Inference OptimizationEfficient Deep LearningTransformer ArchitecturesMemory Management

Methods & Architectures

implementation of FlashEVA attentionfine-tuning transformers for FlashEVAanalysis of throughput and memory usage Transformer ModelsEVA (Efficient Attention via Control Variates)

Applications & Tasks

Natural Language Processing Machine Learning Inference Inference LatencyMemory Consumption Accelerating LLM inferenceReducing memory footprint of transformers

Related Fields

Machine LearningNatural Language ProcessingDeep Learning OptimizationComputer Architecture

Keywords

LLM inferenceefficient attentionFlashEVAtransformermemorythroughputfine-tuningEVANLP

Academic Context

#LLM Inference Optimization#Efficient Deep Learning#Transformer Architectures#Memory Management

Commercial Potential

Potential Products

Optimized inference libraries for transformersHardware-aware LLM implementations

Target Industries

TechnologyCloud ComputingAI Services

Use Case Examples

Deploying large language models on resource-constrained devicesAchieving faster response times for real-time NLP applications

Competitive Edge

Offers significant improvements in inference efficiency over standard transformer implementations, particularly in memory usage and throughput.

Market Opportunity

Large market for efficient LLM inference solutions.

Resource Requirements

Compute Needs

Moderate (for fine-tuning and evaluation)

Data Requirements

Large text corpora for fine-tuning (e.g., 1.5B tokens).

Deployment Constraints

Potential performance degradation on specific task types (e.g., retrieval-focused tasks).

Scalability

The efficiency gains are expected to scale with model size and context length.

Production Readiness

Maturity Level

Research

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