LLMs on a Budget? Say HOLA

• URL: http://arxiv.org/abs/2506.18952v1
• Date: Mon, 23 Jun 2025 10:20:47 GMT
• Title: LLMs on a Budget? Say HOLA
• Authors: Zohaib Hasan Siddiqui, Jiechao Gao, Ebad Shabbir, Mohammad Anas Azeez, Rafiq Ali, Gautam Siddharth Kashyap, Usman Naseem,
• Abstract summary: Running Large Language Models (LLMs) on edge devices is constrained by high compute and memory demands.<n>We introduce HOLA, an end-to-end optimization framework for efficient LLM deployment.<n>We show that HOLA delivers significant gains: 17.6% EMA on GSM8K, 10.5% MCA on ARC, and reduced latency and memory on edge devices like Jetson Nano--proving both scalable and production-ready.
• Score: 8.120251020350587
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Running Large Language Models (LLMs) on edge devices is constrained by high compute and memory demands posing a barrier for real-time applications in sectors like healthcare, education, and embedded systems. Current solutions such as quantization, pruning, and retrieval-augmented generation (RAG) offer only partial optimizations and often compromise on speed or accuracy. We introduce HOLA, an end-to-end optimization framework for efficient LLM deployment. Internally, it leverages Hierarchical Speculative Decoding (HSD) for faster inference without quality loss. Externally, AdaComp-RAG adjusts retrieval complexity based on context needs. Together with LoBi, which blends structured pruning (LoRA) and quantization, HOLA delivers significant gains: 17.6% EMA on GSM8K, 10.5% MCA on ARC, and reduced latency and memory on edge devices like Jetson Nano--proving both scalable and production-ready.

Related papers

• BucketServe: Bucket-Based Dynamic Batching for Smart and Efficient LLM Inference Serving [3.620158146761518]
  BucketServe is a bucket-based dynamic framework designed to optimize inference performance.<n>It can handle 1.93x more request load load attainment of 80% compared with UELLM and demonstrates 1.975x higher system load capacity compared to the UELLM.
  arXiv  Detail & Related papers  (2025-07-23T01:51:48Z)
• Hybrid Systolic Array Accelerator with Optimized Dataflow for Edge Large Language Model Inference [8.475319961845903]
  Edge accelerator should achieve high area efficiency and minimize external memory access.<n>This paper proposes an edge LLM inference accelerator featuring a hybrid systolic array architecture.<n>Our solution achieves 247/117 (token/s/mm2) while running a 1.3B LLM on long-input/long-output scenarios.
  arXiv  Detail & Related papers  (2025-07-11T20:27:30Z)
• Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition [95.54406667705999]
  Pangu Embedded is an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs)<n>It addresses the significant computational costs and inference latency challenges prevalent in existing reasoning-optimized LLMs.<n>It delivers rapid responses and state-of-the-art reasoning quality within a single, unified model architecture.
  arXiv  Detail & Related papers  (2025-05-28T14:03:02Z)
• TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval [10.268774281394261]
  Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain coverage.<n>Modern RAG pipelines rely on large datastores, leading to system challenges in latency-sensitive deployments.<n>We propose TeleRAG, an efficient inference system that reduces RAG latency with minimal GPU memory requirements.
  arXiv  Detail & Related papers  (2025-02-28T11:32:22Z)
• Dynamic Low-Rank Sparse Adaptation for Large Language Models [54.1231638555233]
  Low-rank Sparse Adaptation (LoSA) is a novel method that seamlessly integrates low-rank adaptation into sparse LLM sparsity.<n>LoSA dynamically sparsifies the LoRA outcomes based on the corresponding sparse weights during fine-tuning.<n>LoSA can efficiently boost the efficacy of sparse LLMs within a few hours, without introducing any additional inferential burden.
  arXiv  Detail & Related papers  (2025-02-20T18:37:32Z)
• Efficient Deployment of Large Language Models on Resource-constrained Devices [12.644230479753476]
  It is necessary to fine-tune Large Language Models (LLMs) on resource-constrained devices for various downstream tasks.<n>FedSpine is a framework that combines Efficient Fine-Tuning (PEFT) with structured pruning for efficient deployment of LLMs on resource-constrained devices.<n>We show that FedSpine can speed up fine-tuning by 1.4times$$$times and improve final accuracy by 0.4%-4.5% under the same sparsity level compared to other baselines.
  arXiv  Detail & Related papers  (2025-01-05T04:38:11Z)
• EoRA: Fine-tuning-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation [84.70637613266835]
  EoRA is a fine-tuning-free method that augments compressed Large Language Models with low-rank matrices.<n>EoRA consistently outperforms prior training-free low rank methods in recovering the accuracy of compressed LLMs.
  arXiv  Detail & Related papers  (2024-10-28T17:59:03Z)
• Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs [75.11449420928139]
  Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. Low-Rank Adaptation (LoRA) has emerged as a promising solution, but there exists a gap between the practical performance of low-rank adaptations and its theoretical optimum. We propose eXtreme Gradient Boosting LoRA, a novel framework that bridges this gap by leveraging the power of ensemble learning.
  arXiv  Detail & Related papers  (2024-10-25T17:07:13Z)
• Search for Efficient Large Language Models [52.98684997131108]
  Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research. Weight pruning, quantization, and distillation have been embraced to compress LLMs, targeting memory reduction and inference acceleration. Most model compression techniques concentrate on weight optimization, overlooking the exploration of optimal architectures.
  arXiv  Detail & Related papers  (2024-09-25T21:32:12Z)
• Flat-LoRA: Low-Rank Adaptation over a Flat Loss Landscape [52.98187034726091]
  We introduce Flat-LoRA, which aims to identify a low-rank adaptation situated in a flat region of the full parameter space.<n>We show that Flat-LoRA improves both in-domain and out-of-domain generalization.
  arXiv  Detail & Related papers  (2024-09-22T11:24:10Z)
• LUT Tensor Core: A Software-Hardware Co-Design for LUT-Based Low-Bit LLM Inference [10.608817382813786]
  Mixed-precision general matrix multiplication is a critical yet under-explored operation.<n>Current hardware lacks native support for mpGEMM, leading to inefficient dequantization-based implementations.<n>We propose LUT Core, a software- hardware co-designed solution optimized for low-bit LLM inference.
  arXiv  Detail & Related papers  (2024-08-12T08:52:14Z)
• JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning [16.86356520836045]
  We introduce a novel framework for PEFT-compatible fine-tuning of Llama-2 models, leveraging distributed training. Our framework uniquely utilizes JAX's just-in-time (JIT) compilation and tensor-sharding for efficient resource management. Our experiments show more than 12x improvement in runtime compared to Hugging Face/DeepSpeed implementation with four GPU while consuming less than half the VRAM per GPU.
  arXiv  Detail & Related papers  (2024-03-17T23:02:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.