DiLoCoX (Qwen1.5-107B on WT-103)
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
- China Mobile,Zero Gravity Labs (0g AI)
- Organisation type
- Industry,Industry
- Country
- China, United States of America
- Published
- 26 June 2025
- Authors
- Ji Qi, WenPeng Zhu, Li Li, Ming Wu, YingJun Wu, Wu He, Xun Gao, Jason Zeng, Michael Heinrich
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
- 107B
- Training data
- tokens
4000 steps unknown number of epochs or sequence length
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 A800 PCIe 40 GB
- Chips used
- 160
- Power draw
- 78.3 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
- Unreleased
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
- DiLoCoX: A Low-Communication Large-Scale Training Framework for Decentralized Cluster
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
DiLoCoX (Qwen1.5-107B on WT-103) was published by China Mobile,Zero Gravity Labs (0g AI), in the country recorded as China, during June 2025. The category the publisher falls under is industry,Industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
DiLoCoX (Qwen1.5-107B on WT-103) — common questions
DiLoCoX (Qwen1.5-107B on WT-103)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
DiLoCoX (Qwen1.5-107B on WT-103)— what GPU do I need to run it?
None. This 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.
DiLoCoX (Qwen1.5-107B on WT-103)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DiLoCoX (Qwen1.5-107B on WT-103)— how many parameters does it have?
It has a parameter count of 107B. 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.
DiLoCoX (Qwen1.5-107B on WT-103)— who created it?
It was published by China Mobile,Zero Gravity Labs (0g AI), based in China, an organisation categorised as industry,Industry.
DiLoCoX (Qwen1.5-107B on WT-103)— when was it released?
It was published in June 2025.
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.