# ByteCheckpoint: A Unified Checkpointing System for Large Foundation Model Development

arXiv: https://arxiv.org/abs/2407.20143 · HTML: https://mlsanity.com/paper/2407.20143/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 71 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://mlsanity.com/paper/2405.04434/) (2024)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel](https://mlsanity.com/paper/2304.11277/) (2023)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence](https://mlsanity.com/paper/2406.11931/) (2024)
- [Reducing Activation Recomputation in Large Transformer Models](https://mlsanity.com/paper/2205.05198/) (2022)
- [MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs](https://mlsanity.com/paper/2402.15627/) (2024)
- [ServerlessLLM: Low-Latency Serverless Inference for Large Language Models](https://mlsanity.com/paper/2401.14351/) (2024)
- [Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs](https://mlsanity.com/paper/2204.12013/) (2022)
- [Characterization of Large Language Model Development in the Datacenter](https://mlsanity.com/paper/2403.07648/) (2024)
- [Varuna: Scalable, Low-cost Training of Massive Deep Learning Models](https://mlsanity.com/paper/2111.04007/) (2021)
- [Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates](https://mlsanity.com/paper/2309.08125/) (2023)
- [Aryl: An Elastic Cluster Scheduler for Deep Learning](https://mlsanity.com/paper/2202.07896/) (2022)
- [Parcae: Proactive, Liveput-Optimized DNN Training on Preemptible Instances](https://mlsanity.com/paper/2403.14097/) (2024)
- [Tenplex: Dynamic Parallelism for Deep Learning using Parallelizable Tensor Collections](https://mlsanity.com/paper/2312.05181/) (2023)
- [Unicron: Economizing Self-Healing LLM Training at Scale](https://mlsanity.com/paper/2401.00134/) (2023)

## Built on by (most cited first, 24 of 24)

- [Laminar: A Scalable Asynchronous RL Post-Training Framework](https://mlsanity.com/paper/2510.12633/) (2025)
- [VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo](https://mlsanity.com/paper/2508.02317/) (2025)
- [Robust LLM Training Infrastructure at ByteDance](https://mlsanity.com/paper/2509.16293/) (2025)
- [Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training](https://mlsanity.com/paper/2509.03018/) (2025)
- [TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training](https://mlsanity.com/paper/2604.09107/) (2026)
- [TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling](https://mlsanity.com/paper/2608.10402/) (2026)
- [Sparse Checkpointing for Fast and Reliable MoE Training](https://mlsanity.com/paper/2412.15411/) (2024)
- [GhostServe: A Lightweight Checkpointing System in the Shadow for Fault-Tolerant LLM Serving](https://mlsanity.com/paper/2605.00831/) (2026)
- [FlashRecovery: Fast and Low-Cost Recovery from Failures for Large-Scale Training of LLMs](https://mlsanity.com/paper/2509.03047/) (2025)
- [Role-Based Fault Tolerance System for LLM RL Post-Training](https://mlsanity.com/paper/2512.22492/) (2025)
- [TrainMover: An Interruption-Resilient Runtime for ML Training](https://mlsanity.com/paper/2412.12636/) (2024)
- [LUMEN: Coordinated Failure Recovery for Distributed LLM Serving](https://mlsanity.com/paper/2606.17787/) (2026)
- [Nereus: Adaptive Parallelism for LLM Post-Training](https://mlsanity.com/paper/2609.34645/) (2026)
- [Alchemist: Towards the Design of Efficient Online Continual Learning System](https://mlsanity.com/paper/2503.01066/) (2025)
- [BootSeer: Analyzing and Mitigating Initialization Bottlenecks in Large-Scale LLM Training](https://mlsanity.com/paper/2507.12619/) (2025)
- [TensorDex: A Compact, Tensor-Centric Storage System for Modern AI Models](https://mlsanity.com/paper/2604.17104/) (2026)
- [From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs](https://mlsanity.com/paper/2605.09370/) (2026)
- [TierCheck: Tiered Checkpointing for Fault Tolerance in Large Language Model Training](https://mlsanity.com/paper/2605.17821/) (2026)
- [Orbax: Distributed Checkpointing with JAX](https://mlsanity.com/paper/2605.23066/) (2026)
- [PHOENIX: Resilient LLM Training with Hot-Swapping via Zero-Overhead Checkpoint](https://mlsanity.com/paper/2607.01646/) (2026)
- [The Life of a Token: from Words to Bits on the Wire](https://mlsanity.com/paper/2609.19924/) (2026)
- [Fast Recovery for LLM Serving via Decoupled Device Memory Lifetime in Dynamo](https://mlsanity.com/paper/2609.25451/) (2026)
- [Leto: Fast In-Place Recovery for LLM Training on Surviving Hardware](https://mlsanity.com/paper/2610.00687/) (2026)
- [Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines](https://mlsanity.com/paper/2610.03087/) (2026)

## Abstract (arXiv)

Checkpointing to preserve training states is crucial during the development of Large Foundation Models (LFMs), for training resumption upon various failures or changes in GPU resources and parallelism configurations. In addition, saved checkpoints are dispatched to evaluation tasks or transferred across different training stages (e.g., from pre-training to post-training). All these scenarios require resharding distributed checkpoints from one parallelism to another. In production environments, different LFMs are trained with various frameworks and storage backends, depending on model sizes and training scales. A high-performance checkpointing system is needed to enable efficient checkpoint management at scale throughout the lifecycle of LFM development. We introduce ByteCheckpoint, an industrial-grade checkpointing system for large-scale LFM training. ByteCheckpoint features: a parallelism-agnostic checkpoint representation that enables efficient load-time checkpoint resharding; a generic checkpoint saving/loading workflow to accommodate multiple training frameworks and support different storage backends; full-stack optimizations to ensure high I/O efficiency and scalability; a suite of monitoring tools to streamline large-scale performance analysis and bottleneck detection. Compared to existing open-source checkpointing systems [52, 58], ByteCheckpoint significantly reduces runtime checkpoint stalls, achieving an average reduction of 54.20x. For saving and loading times, ByteCheckpoint achieves improvements of up to 9.96x and 8.80x, respectively.