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Boris Dayma wrote A Recipe for Training Large Models
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The HuggingFace team published The Ultra-Scale Playbook: Training LLMs on GPU Clusters.
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Jacob Austin maintains How to Scale Your Model: A Systems View of LLMs on TPUs
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The Harvard Edge group publishes Machine Learning Systems (CS249r textbook; site: mlsysbook.ai).
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@StellaAthena created the Common LLM Settings spreadsheet which can be a super-useful resource when you're about to embark on a new LLM training - as it tells you how many known LLM trainings were created.
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A few years back I started compiling information on which dtype the models were trained in - it only contains a handful of models but if you're doing a research on dtypes it can still be useful. I was using this information to try and write a model pretraining dtype auto-detection and here is a related float16 vs bfloat16 numerical properties comparison.
Logbooks, chronicles, and technical reports of training LLMs/VLMs are among the best sources to learn from about dealing with training instabilities, infrastructure and scaling challenges, and choosing good hyper parameters. The earlier entries here are logbooks/chronicles kept during a training run; increasingly the same hard-won lessons appear as dedicated sections of technical reports - typically titled something like Infrastructure, Scaling, and Efficiency or Engineering Challenges and Solutions, and often covering reliability and operational challenges that rarely make it into a model card.
If you know of a public LLM/VLM training logbook, or a technical report with such a section, that is not on this list please kindly let me know via an Issue or add it via a PR. Thank you!
The listing is in no particular order other than being grouped by the year.
- BigScience pre-BLOOM 108B training experiments (2021): chronicles | the full spec and discussions (backup: 1 | 2)
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BigScience BLOOM-176B (2022): chronicles-prequel | chronicles | the full spec and discussions (backup: 1 | 2 | 3)
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THUDM GLM-130B (2022): en logbook | Mandarin version (backup: 1 | 2)
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HuggingFace IDEFICS-80B multimodal (Flamingo repro) (2023): Learning log | Training Chronicles (backup: 1 | 2)
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BloombergGPT 50B LLM - section C in BloombergGPT: A Large Language Model for Finance
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MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs - the paper covers various training issues and their resolution - albeit on models that are proprietary yet just as instructional/useful.
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Imbue's From bare metal to a 70B model: infrastructure set-up and scripts very detailed technical post covers many training-related issues that they had to overcome while training a proprietary 70B-param model.
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Meta's The Llama 3 Herd of Models devotes §3.3 Infrastructure, Scaling, and Efficiency to the training stack, including §3.3.4 Reliability and Operational Challenges - notably how environmental factors (diurnal temperature swings, and the effect of tens of thousands of GPUs' fluctuating power draw on the datacenter grid) perturbed training throughput, a dimension I haven't seen documented in other reports.
- Project Apertus (ETH Zürich, EPFL, CSCS) - Apertus v1 Technical Report: Democratizing Open and Compliant LLMs for Global Language Environments - a fully open 8B/70B suite trained on 15T tokens across 1800+ languages using up to 4096 GPUs on CSCS's Alps. Its Infrastructure, Scaling, and Efficiency and Engineering Challenges and Solutions sections (~5 pages) read like a logbook of the run.
- Supercharging Olmo-core for Efficient and Scalable MoE Training - practical recommendations for training large scale MoE models in 2026!
- Marin 535B-A23B MoE live - each short entry goes to a details GitHub Issue where you can read the details. Finished trainings retrospectives: 8B, 32B
- Project Apertus - Apertus v1.5 Technical Report: From Open Text Models to Open Multimodal Reasoners - continued multimodal pretraining of the Apertus 8B/70B models; like v1 it includes Infrastructure, Scaling, and Efficiency and Engineering Challenges and Solutions sections (~5 pages).
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Imbue published a detailed log of how they have set up a 512-node IB-fat-tree cluster and made it to work: From bare metal to a 70B model: infrastructure set-up and scripts, they also open-sourced the cluster tooling they created in the process.
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SemiAnalysis published a great detailed writeup about what it takes to set up a Neocloud cluster.