Democratizing AI: A Comparative Study in Deep Learning Efficiency and Future Trends in Computational Processing

Lisan Al Amin; Md Ismail Hossain; Rupak Kumar Das; Mahbubul Islam; Abdulaziz Tabbakh

cs.PFMar 21, 2026

Democratizing AI: A Comparative Study in Deep Learning Efficiency and Future Trends in Computational Processing

Authors: Lisan Al Amin, Md Ismail Hossain, Rupak Kumar Das, Mahbubul Islam, Abdulaziz Tabbakh

Organizations: Cognitive Links, Maryland, USA · North South University, Bangladesh · Pennsylvania State University, USA · United International University, Bangladesh ·

Abstract

The exponential growth in data has intensified the demand for computational power to train large-scale deep learning models. However, the rapid growth in model size and complexity raises concerns about equal and fair access to computational resources, particularly under increasing energy and infrastructure constraints. GPUs have emerged as essential for accelerating such workloads. This study benchmarks four deep learning models (Conv6, VGG16, ResNet18, CycleGAN) using TensorFlow and PyTorch on Intel Xeon CPUs and NVIDIA Tesla T4 GPUs. Our experiments demonstrate that, on average, GPU training achieves speedups ranging from 11x to 246x depending on model complexity, with lightweight models (Conv6) showing the highest acceleration (246x), mid-sized models (VGG16, ResNet18) achieving 51-116x speedups, and complex generative models (CycleGAN) reaching 11x improvements compared to CPU training. Additionally, in our PyTorch vs. TensorFlow comparison, we observed that TensorFlow's kernel-fusion optimizations reduce inference latency by approximately 15%. We also analyze GPU memory usage trends and projecting requirements through 2025 using polynomial regression. Our findings highlight that while GPUs are essential for sustaining AI's growth, democratized and shared access to GPU resources is critical for enabling research innovation across institutions with limited computational budgets.

Figures & tables

Sl No .	Category	Authors	Contributions	Remarks
1	Parallel Computing & Machine Learning.	Matloff et al [ 14 ]	Perform parallel computer software	In this paper, customized set up and programmable parallel software concept is used
2	GPU & Machine Learning	Steinkraus et al [ 15 ]	Usage of GPU for Machine Learning	The author proposes a generic two-layer neural network GPU implementation
3	GPU & Deep Learning	Gawehn et al [ 16 ]	GPUs for Deep Learning	Advance drug development by GPU-based learning
4	GPU & Cloud Computing	Sisyukov et al [ 17 ]	GPU optimized data analysis of cloud infrastructure	This paper suggests an approach to organize a private cloud environment for real-time data processing
5	GPU & Statistics	Eller et al [ 18 ]	GPU backed massive volume of data analysis	Perform high statistical GPU analyzes to explore fundamental neutrino properties
6	GPU & Data Analytics	Nie et al [ 19 ]	Prediction of GPU error using ML in a wide HPC system.	The authors use six-month trace data to study the device conditions causing GPU errors.

TABLE I : Overview of Key Research Contributions in GPU Applications across Diverse Computing Domains

Fig. 1 : Streaming Multiprocessor Architecture in GPUs

Network	Conv-6	CycleGAN	ResNet-18	VGG-16
Conv	64, pool 128, pool 256, pool	c7s1-64 d128 d256 9 $\times$ [R256] u128 u64 c7s1-3	16 3 $\times$ [16,16] 3 $\times$ [32,32] 3 $\times$ [64,64]	2 $\times$ 64, pool 2 $\times$ 128, pool 3 $\times$ 256, pool 3 $\times$ 512, pool 3 $\times$ 512, pool
Weights	All: 397K Conv: 371K	All: 2.3M Conv: 2.2M	All: 11M	All: 136M Conv: 14
Size	3.06 MB	1.0 GB	45.00 MB	56.13 MB

TABLE II : Model architectures examined in this work. Brackets denote residual connections around layers.

Specification	CPU	GPU (T4)
Processor	Intel Xeon (shared) a	NVIDIA Tesla T4
Clock Speed	Up to 2.3 GHz	Up to 1.59 GHz
Cores	2 vCPUs b	2560 CUDA cores
Memory	12.7 GB	16 GB (GDDR6)
Peak Performance	$\sim$ 0.37 TFLOPS c	8.1 TFLOPS
Architecture	x86-64	Turing

TABLE III : Google Colab Free Tier - CPU vs GPU (T4) Specifications

Operation	CPU Time (ms)	GPU Time (ms)
model_inference	848.070	82.527
aten::conv2d	609.262	62.940
aten::convolution	609.155	62.940
aten::_convolution	608.817	62.940
aten::cudnn_convolution	604.944	62.940
volta_sgemm_128x64_nn	0.000	21.181

TABLE IV : Comparison of CPU and GPU Time for PyTorch Operations in ResNet18 Model

Name	CUDA time (ms)	CPU time (ms)	CUDA Mem	CPU Mem
ProfilerStep*	60.988	113.840	-6.76 Gb	36.75 Mb
aten::conv2d	7.805	445.333	2.19 Gb	0 b
aten::convolution	7.805	432.800	2.19 Gb	0 b
aten::_convolution	7.805	412.400	2.19 Gb	0 b
aten::cudnn_convolution	6.165	139.400	2.19 Gb	0 b
volta_sgemm_128x64_nn	4.640	0.000	0 b	0 b

TABLE V : Performance Metrics of GPU and CPU during Inference on Conv6 Model

Fig. 3 : Comparison of CPU and GPU metrics for different models: (a) Run time comparison across Conv6, VGG16, ResNet18 and CycleGAN, showing significant improvements in GPU over CPU; (b) Inference time comparison, illustrating reduced inference times when utilizing GPU; (c) Memory usage comparison, indicating a more efficient use of memory with GPU for all four models.

Model	Run time (ms)		Inference time (ms)		Mem usage (MB)		Mem BW (GB/sec)
	CPU	GPU	CPU	GPU	CPU	GPU	CPU to GPU
Tensorflow
Conv6	35123.42	142.67	617.29	19.38	3214	2287	102.10
VGG16	69875.21	603.54	2789.17	66.24	4985	6978	102.80
ResNet18	10127.83	198.42	4023.69	64.17	2011	1387	101.91
CycleGAN	5047.28	452.89	203.57	4.63	-	-	-

TABLE VI : Comparison of CPU and GPU metrics for different models

Fig. 4 : The GPU memory data from 2000 to 2025 provides an invaluable retrospective view into the advancements of graphical processing units, spanning from the early 64 MB cards to the 141 GB AI accelerators of today. By applying a logistic growth model to this dataset, we can project a roadmap through 2030, offering a clearer picture of how hardware scaling responds to the burgeoning demands of the AI era.

GPU Acceleration

GPU acceleration uses parallel processors, specialized kernels, and memory-aware systems to speed computation across AI and scientific workloads.

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Apr 16, 2026J/K move · Enter open · S save

DEEP-GAP: Deep-learning Evaluation of Execution Parallelism in GPU Architectural Performance

Kathiravan Palaniappan

Independent Researcher (University of Colorado Colorado Springs Alumni)

AI Accelerator Inference GPU Acceleration

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Modern datacenters increasingly rely on low-power, single-slot inference accelerators to balance performance, energy efficiency, and rack density constraints. The NVIDIA T4 GPU has become widely deployed due to strong performance per watt and mature software support. Its successor, the NVIDIA L4 GPU, introduces improvements in Tensor Core throughput, cache capacity, memory bandwidth, and parallel execution capability. However, limited empirical evidence quantifies the practical inference performance gap between these two generations under controlled and reproducible conditions. This work introduces DEEP-GAP, a systematic evaluation extending the GDEV-AI methodology to GPU inference. Using identical configurations and workloads, we evaluate ResNet18, ResNet50, and ResNet101 across FP32, FP16, and INT8 precision modes using PyTorch and TensorRT. Results show that reduced precision significantly improves performance, with INT8 achieving up to 58x throughput improvement over CPU baselines. L4 achieves up to 4.4x higher throughput than T4 while reaching peak efficiency at smaller batch sizes between 16 and 32, improving latency-throughput tradeoffs for latency-sensitive workloads. T4 remains competitive for large batch workloads where cost or power efficiency is important. DEEP-GAP provides practical guidance for selecting precision modes, batch sizes, and GPU architectures for modern inference deployments.

Abstract

GPU Acceleration

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