Democratizing Agentic AI with Fast Test-Time Scaling on the Edge

• URL: http://arxiv.org/abs/2509.00195v1
• Date: Fri, 29 Aug 2025 19:12:04 GMT
• Title: Democratizing Agentic AI with Fast Test-Time Scaling on the Edge
• Abstract summary: FlashTTS is a serving system that makes TTS practical for memory-constrained LLM reasoning.<n>Built as a plug-and-play library for vLLM, FlashTTS enables edge LLMs on a single consumer GPU to match the accuracy and latency of large cloud models.<n>Our evaluation demonstrates that FlashTTS achieves an average 2.2x higher goodput and reduces latency by 38%-68% compared to a vLLM baseline.
• Score: 14.889726225226175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying agentic AI on edge devices is crucial for privacy and responsiveness, but memory constraints typically relegate these systems to smaller Large Language Models (LLMs) with inferior reasoning capabilities. Test-Time Scaling (TTS) can bridge this reasoning gap by dedicating more compute during inference, but existing methods incur prohibitive overhead on edge hardware. To overcome this, we introduce FlashTTS, a serving system that makes TTS practical for memory-constrained LLM reasoning. FlashTTS introduces three synergistic optimizations: (i) Speculative Beam Extension to mitigate system stragglers from irregular reasoning paths; (ii) Asymmetric Multi-Model Memory Allocation to dynamically balance memory between generation and verification; and (iii) Dynamic Prefix-Aware Scheduling to maximize KV-cache reuse. Built as a plug-and-play library for vLLM, FlashTTS enables edge LLMs on a single consumer GPU (24 GB) to match the accuracy and latency of large cloud models. Our evaluation demonstrates that FlashTTS achieves an average 2.2x higher goodput and reduces latency by 38%-68% compared to a vLLM baseline, paving the way for democratized, high-performance agentic AI on edge devices.

Related papers

• LLM Inference in a Flash! [66.43896490006965]
  Large Language Models (LLMs) have shown impressive capabilities across a range of natural language processing tasks.<n>We present an end-to-end integer-only quantization approach to eliminate expensive floating-point computations.
  arXiv  Detail & Related papers  (2026-09-14T18:04:30Z)
• Faster Than Flash: Exploiting Attention Sparsity for Efficient Long-Context Decoding [51.48425605758328]
  We present Faster Flash Decoding (FFD), a novel hardware-algorithm co-design framework designed to break the memory wall in long-context decoding.<n>FFD integrates the selector and computer into a fully fused kernel, replacing external metadata indices with content-aware scanning.<n>It achieves up to 11.6x kernel-level speedup and scaling to 256K context length, with 2.37x end-to-end throughput improvement.
  arXiv  Detail & Related papers  (2026-08-31T15:13:20Z)
• FastTPS: An Optimized Method for LLM Token Phase for AI accelerators [11.379739631959822]
  FastTPS is a high performance and low-precision loss method for accelerating the token-phase in LLM inference on general AI accelerators.<n>Our results confirm that FastTPS significantly alleviates memory bottlenecks in the token phase, delivering a 6x speed improvement.
  arXiv  Detail & Related papers  (2026-07-13T08:02:26Z)
• Lever: Speculative LLM Inference on Smartphones [11.768438191539374]
  Large language models (LLMs) are increasingly needed for interactive mobile applications.<n>We present Lever, an end-to-end system for efficient flash-backed LLM inference on smartphones.
  arXiv  Detail & Related papers  (2026-05-16T03:43:10Z)
• Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation [1.2559585990041289]
  LARS (Low-memory Activation-Rank Subspace) is a novel adaptation framework that decouples memory consumption from sequence length.<n>We show that LARS reduces the memory footprint by an average of 33.54% on GPUs and 51.95% on CPUs in comparison to LoRA across reasoning, understanding and long-context datasets.
  arXiv  Detail & Related papers  (2026-04-03T17:05:33Z)
• TS-Memory: Plug-and-Play Memory for Time Series Foundation Models [63.21390142212087]
  Time Series Foundation Models (TSFMs) achieve strong zero-shot forecasting through large-scale pre-training.<n>Existing solutions face a trade-off: Parametric Adaptation can cause catastrophic forgetting, while Non-Parametric Retrieval improves forecasts but incurs high latency due to datastore search.<n>We propose Parametric Memory Distillation and implement it as TS-Memory, a lightweight memory adapter that augments frozen TSFMs.
  arXiv  Detail & Related papers  (2026-02-12T04:16:19Z)
• Remoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing [29.98726492279776]
  Mixture-of-Experts (MoE) has become a dominant architecture in large language models.<n>MoEs incurs high inference costs due to memory-intensive parameter caching.<n>We propose Remoe, a heterogeneous MoE inference system tailored for serverless computing.
  arXiv  Detail & Related papers  (2025-12-21T10:27:50Z)
• Harli: SLO-Aware Co-location of LLM Inference and PEFT-based Finetuning on Model-as-a-Service Platforms [33.64527903547734]
  Harli improves the finetune throughput by 46.2% on average (up to over state-of-the-art serving systems)<n>Harli improves the finetune throughput by 46.2% on average (up to over state-of-the-art serving systems)
  arXiv  Detail & Related papers  (2025-11-13T05:58:52Z)
• Memory- and Latency-Constrained Inference of Large Language Models via Adaptive Split Computing [8.705453442427585]
  Large language models (LLMs) have achieved near-human performance across diverse reasoning tasks.<n>Their deployment on resource-constrained Internet-of-Things (IoT) devices remains impractical due to massive parameter footprints and memory-intensive autoregressive decoding.<n>This work introduces the first autoregressive-aware split computing framework designed explicitly for LLM deployment on edge devices.
  arXiv  Detail & Related papers  (2025-11-06T02:55:07Z)
• TinyServe: Query-Aware Cache Selection for Efficient LLM Serving [5.216774377033164]
  We present TinyServe, a system for serving large language models (LLMs) efficiently.<n>TinyServe executes real-time decoding with sparsity strategies and fine-grained instrumentation.<n>Our experiments show TinyServe up to textbf3.4x speedup and over textbf2x memory savings with negligible accuracy drop.
  arXiv  Detail & Related papers  (2025-08-28T16:17:18Z)
• Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction [58.044803442346115]
  Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and parallel decoding but suffer from prohibitive computational complexity and memory overhead during inference.<n>We propose Sparse-dLLM, the first training-free framework integrating dynamic cache eviction with sparse attention via delayed bidirectional sparse caching.
  arXiv  Detail & Related papers  (2025-08-04T16:14:03Z)
• FlashSVD: Memory-Efficient Inference with Streaming for Low-Rank Models [15.244129138320782]
  FlashSVD is an end-to-end rank-aware streaming inference framework for SVD-compressed large language models.<n>It cuts peak activation memory by up to 70.2% and intermediate transient memory by 75%.<n>It incurs no accuracy loss with upstream encodering compression methods, offering a practical path toward memory-constrained deployment of low-rank LLMs.
  arXiv  Detail & Related papers  (2025-08-02T22:06:46Z)
• Efficient Multi-Instance Generation with Janus-Pro-Dirven Prompt Parsing [53.295515505026096]
  Janus-Pro-driven Prompt Parsing is a prompt- parsing module that bridges text understanding and layout generation.<n>MIGLoRA is a parameter-efficient plug-in integrating Low-Rank Adaptation into UNet (SD1.5) and DiT (SD3) backbones.<n>The proposed method achieves state-of-the-art performance on COCO and LVIS benchmarks while maintaining parameter efficiency.
  arXiv  Detail & Related papers  (2025-03-27T00:59:14Z)
• Online Dense Point Tracking with Streaming Memory [54.22820729477756]
  Dense point tracking is a challenging task requiring the continuous tracking of every point in the initial frame throughout a substantial portion of a video.<n>Recent point tracking algorithms usually depend on sliding windows for indirect information propagation from the first frame to the current one.<n>We present a lightweight and fast model with textbfStreaming memory for dense textbfPOint textbfTracking and online video processing.
  arXiv  Detail & Related papers  (2025-03-09T06:16:49Z)
• LiVOS: Light Video Object Segmentation with Gated Linear Matching [116.58237547253935]
  LiVOS is a lightweight memory network that employs linear matching via linear attention. For longer and higher-resolution videos, it matched STM-based methods with 53% less GPU memory and supports 4096p inference on a 32G consumer-grade GPU.
  arXiv  Detail & Related papers  (2024-11-05T05:36:17Z)
• TPI-LLM: Serving 70B-scale LLMs Efficiently on Low-resource Edge Devices [36.714057078457195]
  We present TPI-LLM, a compute- and memory-efficient tensor parallel inference system for 70B-scale models. TPI-LLM keeps sensitive raw data local in the users' devices and introduces a sliding window memory scheduler. We show that TPI-LLM demonstrated over 80% less time-to-first-token and token latency compared to Accelerate.
  arXiv  Detail & Related papers  (2024-10-01T09:18:56Z)
• vTensor: Flexible Virtual Tensor Management for Efficient LLM Serving [53.972175896814505]
  Large Language Models (LLMs) are widely used across various domains, processing millions of daily requests. Large Language Models (LLMs) are widely used across various domains, processing millions of daily requests.
  arXiv  Detail & Related papers  (2024-07-22T14:37:58Z)
• Efficient NLP Inference at the Edge via Elastic Pipelining [0.42970700836450487]
  WRX reconciles the latency/memory tension via two novel techniques. We build WRX and evaluate it against a range of NLP tasks, under a practical range of target latencies, and on both CPU and GPU.
  arXiv  Detail & Related papers  (2022-07-11T17:15:57Z)
• LiteTransformerSearch: Training-free On-device Search for Efficient Autoregressive Language Models [34.673688610935876]
  We show that the latency and perplexity pareto-frontier can be found without need for any model training. We evaluate our method, dubbed Lightweight Transformer Search (LTS), on diverse devices. We show that the perplexity of Transformer-XL can be achieved with up to 2x lower latency.
  arXiv  Detail & Related papers  (2022-03-04T02:10:43Z)

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.