Gradient Under Microscope: Benchmarking Resource Utilization of Memory-Efficient Gradient Computation Methods

arXiv:2608.08961 · cs.LG · Submitted 2026-08-09 · Read on arXiv

Sarthak Mahapatra, Zihan Zhou, Khatoon Khedri, Mehdi Hosseinzadeh, Reza Rawassizadeh

cs.LG

Submitted: 2026-08-09

Updated: 2026-08-11

Code: https://github.com/huggingface/nanoVLM

Project page: https://v-chandra.github.io/on-device-llms

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adam — 77× in this paper · explained in 3 episodes
• Transformer — 24× in this paper · explained in 2 episodes
• Transformers — 9× in this paper · explained in 2 episodes
• Conjugate Gradient Descent — 6× in this paper
• Benchmark — 5× in this paper · explained in 2 episodes
• Decoder — 5× in this paper · explained in 2 episodes
• Weight Decay — 5× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 3× in this paper
• Regularization — 3× in this paper · explained in 3 episodes
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Benchmarking — 2× in this paper · explained in 4 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Pareto Frontier — 2× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 15 episodes
• Stochastic Gradient Descent — 2× in this paper
• Transformer Architecture — 2× in this paper · explained in 4 episodes
• Absorb — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Edge AI — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Language Models (LLMs) — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 9 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Overfitting — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Resource Constraints — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 5 episodes
• Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Model — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 3 episodes

Sources

• NeuroViz: Real-time Interactive Visualization of Forward and Backward Passes in Neural Network Training

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