OpenDiLoCo 1.1B
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Prime Intellect
- Organisation type
- Industry
- Country
- United States of America
- Published
- 10 July 2024
- Authors
- Sami Jaghouar, Jack Min Ong, Johannes Hagemann
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 1.1B
- Training data
- tokens
"We adopt the same hyperparameters as TinyLlama (Zhang et al., 2024), employing a model with 1.1B parameters, a learning rate of 4e−4 and a batch size of 2048."
44000 steps "For the scaled-up 1.1B parameter experiment, we limit it to 44, 000 steps because of the 4× larger batch size. " batch size 2048 sequence length 1024 2048*1024*44000 = 92274688000
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 6.1 × 10²⁰ FLOP
- How it was established
- Operation counting
6ND = 6 FLOP / token / parameter * 1.1*10^9 parameters * 92274688000 tokens [see dataset size notes] = 6.0901294e+20 FLOP 4 replicas of 8xH100 (figure 8) -> 32 H100 GPUs
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA H100 SXM5 80GB
- Chips used
- 32
- Power draw
- 44.2 kW
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
https://github.com/PrimeIntellect-ai/OpenDiLoCo Apache 2.0
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training
- Last updated
- 28 November 2025
What the numbers mean
About this model
OpenDiLoCo 1.1B was published by Prime Intellect, in United States of America, in July 2024. industry is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling/generation, Question answering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Producing it required around 6.1 × 10²⁰ FLOP of arithmetic, on NVIDIA H100 SXM5 80GB, which is a statement about the training budget rather than about inference.
Answers
OpenDiLoCo 1.1B — common questions
Who created OpenDiLoCo 1.1B?
OpenDiLoCo 1.1B was published by Prime Intellect, based in United States of America, categorised as industry.
When was OpenDiLoCo 1.1B released?
OpenDiLoCo 1.1B was published in July 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is OpenDiLoCo 1.1B used for?
OpenDiLoCo 1.1B works in Language, and is recorded as handling language modeling/generation, Question answering. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train OpenDiLoCo 1.1B?
Around 6.1 × 10²⁰ FLOP, on NVIDIA H100 SXM5 80GB. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run OpenDiLoCo 1.1B?
None. OpenDiLoCo 1.1B is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is OpenDiLoCo 1.1B open source?
No. OpenDiLoCo 1.1B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does OpenDiLoCo 1.1B have?
OpenDiLoCo 1.1B has 1.1B parameters. "We adopt the same hyperparameters as TinyLlama (Zhang et al., 2024), employing a model with 1.1B parameters, a learning rate of 4e−4 and a batch size of 2048.". That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.