Ablation Study Collection for Distributed Training of Large Language Models: Systematic Comparison of MoE Architecture and Memory Optimization Strategies

Title: Ablation Study Collection for Distributed Training of Large Language Models: Systematic Comparison of MoE Architecture and Memory Optimization Strategies Abstract: A collection of ablation studies on distributed training techniques, Mixture of Experts (MoE) architecture, and memory-efficient training methods for large language models, providing reproducible code, benchmark results, and references for engineering decisions. Original Author/Maintainer: Scicom-AI-Enterprise-Organization Source Platform: GitHub Original Title: small-ablation: Ablation studies on distributed training, MoE, and memory-efficient LLM training Original Link: https://github.com/Scicom-AI-Enterprise-Organization/small-ablation Release Time: June 2026

This project is a systematic collection of ablation studies aimed at providing quantitative decision-making basis for large model training engineers, addressing practical issues such as the selection of distributed training strategies, application of MoE architecture, and choice of memory optimization methods.

As the scale of large language models (LLMs) continues to grow, the computational resources and memory overhead required to train these models have increased exponentially. Distributed training, Mixture of Experts (MoE) architecture, and memory optimization technologies have become key means to reduce training costs. However, facing numerous framework and technology options such as PyTorch Distributed, DeepSpeed, and Megatron-LM, engineers often face difficult choices: Which distributed strategy is most suitable for my model? How should data parallelism and model parallelism be combined? How does the sparsity of MoE architecture affect training efficiency?

small-ablation project was born to solve these practical problems. It is not a simple technical demonstration, but a systematic collection of ablation studies that provides quantitative decision-making basis for engineers through controlled variables and comparative testing.

The project focuses on three core challenges in large model training:

1. Distributed Training Techniques

Distributed training is the cornerstone of large model training. The project compares and analyzes the performance of mainstream distributed strategies:

• Data Parallelism (DP): Distribute batch data across multiple GPUs, each GPU holds a complete copy of the model.
• Model Parallelism (MP): Distribute model parameters across multiple GPUs, each GPU only holds part of the layers.
• Pipeline Parallelism (PP): Group the model by layers, assign different groups to different GPUs to form a pipeline.
• Tensor Parallelism (TP): Split the parameters of a single layer among multiple GPUs.
• Fully Sharded Data Parallelism (FSDP): PyTorch's native data parallelism scheme that reduces memory usage by sharding optimizer states and gradients.

2. Mixture of Experts (MoE) Architecture

The core idea of MoE is to increase model parameters without increasing computational load:

• Each layer contains multiple "expert" sub-networks
• A gating network selects active experts for each input
• Only selected experts participate in computation The project's research directions include the trade-off of different numbers of experts, load balancing strategies, routing algorithms, and the combined effect with distributed strategies.

3. Memory-Efficient Training Methods

The project evaluates various memory optimization techniques: gradient checkpointing, Flash Attention, Liger Kernel, and ZeRO optimizer state sharding.

Technical References and Ecosystem Integration

The project draws on industry best practices, with reference to technology stacks including PyTorch Distributed, torchtitan, Flash Attention, and Liger Kernel, reflecting an attitude of open integration.

What is an Ablation Experiment?

Ablation experiments originate from neuroscience and in machine learning refer to systematically removing or modifying model components to evaluate their contribution to overall performance.

Experimental Design Principles of This Project

• Single Variable Principle: Only change one variable per experiment to ensure performance differences come from that variable.
• Reproducibility: Provide complete configurations, code, and random seeds.
• End-to-End Measurement: Focus on actual training throughput, memory usage, and convergence speed.
• Multi-Dimensional Evaluation: Comprehensive evaluation from training speed, memory efficiency, model quality, and other dimensions.

Engineering Practice Value

• Provide basis for training infrastructure decisions: Help answer questions such as the number of GPUs needed for training a 70B parameter model, the ratio of parallel strategies, and whether to introduce MoE.
• Avoid repeated pitfalls: Reduce expensive trial-and-error costs and predict technical selection risks.

Application Scenarios and Target Users

• Large model training engineers: Choose the optimal distributed strategy.
• AI infrastructure teams: Evaluate training frameworks and design training platforms.
• Researchers: Understand the impact of technical components on efficiency and quality.
• Learners: Gain an in-depth understanding of distributed training principles.

Project Limitations

• Scale Limitation: Targeted at small to medium-sized models (hundreds of millions to billions of parameters), with limited reference value for 100-billion-level models.
• Hardware Specificity: Results depend on GPU models (A100/H100) and interconnection bandwidth.
• Model Architecture Limitation: Mainly focused on Transformer architecture.

Usage Recommendations

1. As a starting point rather than an end point: Verify on your own hardware and model configurations.
2. Focus on trends rather than absolute values: Relative differences are more reference-worthy.
3. Combine with theoretical analysis: Understand the principles to make correct decisions.

The small-ablation project embodies the pragmatic spirit in engineering practice: In the face of complex technical selections, obtain decision-making basis through systematic and quantifiable experiments. In the high-cost and high-risk field of large model training, this data-driven decision-making method is particularly important. The project provides not only code and results, but also a methodology: How to design ablation experiments, control variables, conduct multi-dimensional evaluation, and translate into engineering decisions. For any large model training team, small-ablation is a valuable technical resource worth referencing.