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Accelerating Local AI on Consumer GPUs: A Hardware-Aware Dynamic Strategy for YOLOv10s

Mahmudul Islam Masum, Miad Islam

TL;DR

The paper tackles the challenge of running real-time object detection on consumer GPUs by revealing system-level bottlenecks that limit performance beyond compute capacity. It introduces a model-agnostic Two-Pass Adaptive Inference strategy that dynamically allocates compute by conducting a fast low-resolution pass and escalating to a high-resolution pass only when needed, outperforming a PyTorch Early-Exit baseline with a $1.85\times$ speedup at a $5.51\%$ mAP loss on COCO-2017 validation. The work demonstrates that hardware-aware inference strategies can achieve practical, real-time AI on everyday devices and that improvements at deployment time can surpass architectural changes under constrained conditions. The findings have broad implications for on-device AI, including privacy-preserving scenarios and potential applicability to edge LLMs, while acknowledging limitations such as evaluation on a single GPU and model. Future work includes testing across additional hardware and exploring more adaptive threshold policies.

Abstract

As local AI grows in popularity, there is a critical gap between the benchmark performance of object detectors and their practical viability on consumer-grade hardware. While models like YOLOv10s promise real-time speeds, these metrics are typically achieved on high-power, desktop-class GPUs. This paper reveals that on resource-constrained systems, such as laptops with RTX 4060 GPUs, performance is not compute-bound but is instead dominated by system-level bottlenecks, as illustrated by a simple bottleneck test. To overcome this hardware-level constraint, we introduce a Two-Pass Adaptive Inference algorithm, a model-independent approach that requires no architectural changes. This study mainly focuses on adaptive inference strategies and undertakes a comparative analysis of architectural early-exit and resolution-adaptive routing, highlighting their respective trade-offs within a unified evaluation framework. The system uses a fast, low-resolution pass and only escalates to a high-resolution model pass when detection confidence is low. On a 5000-image COCO dataset, our method achieves a 1.85x speedup over a PyTorch Early-Exit baseline, with a modest mAP loss of 5.51%. This work provides a practical and reproducible blueprint for deploying high-performance, real-time AI on consumer-grade devices by shifting the focus from pure model optimization to hardware-aware inference strategies that maximize throughput.

Accelerating Local AI on Consumer GPUs: A Hardware-Aware Dynamic Strategy for YOLOv10s

TL;DR

The paper tackles the challenge of running real-time object detection on consumer GPUs by revealing system-level bottlenecks that limit performance beyond compute capacity. It introduces a model-agnostic Two-Pass Adaptive Inference strategy that dynamically allocates compute by conducting a fast low-resolution pass and escalating to a high-resolution pass only when needed, outperforming a PyTorch Early-Exit baseline with a speedup at a mAP loss on COCO-2017 validation. The work demonstrates that hardware-aware inference strategies can achieve practical, real-time AI on everyday devices and that improvements at deployment time can surpass architectural changes under constrained conditions. The findings have broad implications for on-device AI, including privacy-preserving scenarios and potential applicability to edge LLMs, while acknowledging limitations such as evaluation on a single GPU and model. Future work includes testing across additional hardware and exploring more adaptive threshold policies.

Abstract

As local AI grows in popularity, there is a critical gap between the benchmark performance of object detectors and their practical viability on consumer-grade hardware. While models like YOLOv10s promise real-time speeds, these metrics are typically achieved on high-power, desktop-class GPUs. This paper reveals that on resource-constrained systems, such as laptops with RTX 4060 GPUs, performance is not compute-bound but is instead dominated by system-level bottlenecks, as illustrated by a simple bottleneck test. To overcome this hardware-level constraint, we introduce a Two-Pass Adaptive Inference algorithm, a model-independent approach that requires no architectural changes. This study mainly focuses on adaptive inference strategies and undertakes a comparative analysis of architectural early-exit and resolution-adaptive routing, highlighting their respective trade-offs within a unified evaluation framework. The system uses a fast, low-resolution pass and only escalates to a high-resolution model pass when detection confidence is low. On a 5000-image COCO dataset, our method achieves a 1.85x speedup over a PyTorch Early-Exit baseline, with a modest mAP loss of 5.51%. This work provides a practical and reproducible blueprint for deploying high-performance, real-time AI on consumer-grade devices by shifting the focus from pure model optimization to hardware-aware inference strategies that maximize throughput.

Paper Structure

This paper contains 12 sections, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Bottleneck Test
  5. Dynamic Inference Strategies
  6. Early-Exit (EE) Baseline
  7. Two-Pass Adaptive Inference
  8. Threshold Calibration and Tuning
  9. Dataset and Categorization
  10. Results and Analysis
  11. Discussion
  12. Conclusion

Figures (7)

  • Figure 1: Conceptual workflows of the PyTorch Early-Exit and PyTorch Adaptive Two-Pass strategies.
  • Figure 2: Image categorization based on object count and visual clutter.
  • Figure 3: GPU power consumption patterns.
  • Figure 4: Accuracy and speed comparison between the two models.
  • Figure 5: Early-Exit trade-off between mAP and exit rate across different gate thresholds.
  • ...and 2 more figures