Ring-1T TPS calculator
Each card below is assessed against this model at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from the card's memory bandwidth and the size of the model once compressed.
Calculated for this model
Which GPUs can run Ring-1T?
Set the inputs, read the answer
A longer conversation needs more memory, which can push this model off smaller cards.
Hides cards that would only fit the model by compressing it below this point.
0 cards match
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Speeds are estimates for a single request — one conversation at a time — calculated from memory bandwidth, model size and quantisation. Real throughput varies with the inference runtime and its version. Figures published by hardware vendors measure many simultaneous requests and are much higher.
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
- Ant Group
- Organisation type
- Industry
- Country
- China
- Published
- 10 October 2025
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, Quantitative reasoning, Code generation
- Base model
- Ling-1T
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
- 1T
- Training data
- tokens
1 trillion total parameters with 50 billion activated parameters
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Unreleased
- Hugging Face
- inclusionAI
MIT license https://huggingface.co/inclusionAI/Ring-1T
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
- Ring-1T, flow state leads to sudden enlightenment
- Last updated
- 28 November 2025
What the numbers mean
What you need to run it
At 1T parameters, Ring-1T is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job — 0 of the cards we track can hold it on their own, and all of them are datacentre parts.
About this model
Ring-1T was published by Ant Group, in China, in October 2025. It comes out of industry.
It works in Language, and is recorded as doing language modeling/generation, Question answering, Quantitative reasoning, Code generation.
Its starting point was Ling-1T — most models at this scale are adapted from an existing base rather than built from nothing.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the inclusionAI organisation on Hugging Face.
Step by step
How to choose a GPU for Ring-1T
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
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01
Start from the memory column
The table lists every card that can hold Ring-1T. That figure, not the card's headline performance, is what decides whether it runs.
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02
Match the context to your actual use
Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason Ring-1T stops fitting a card that seemed fine.
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03
Decide how much compression you will accept
Compression is what makes Ring-1T fit smaller cards, at some cost in accuracy. A minimum quality removes the ones that go too far.
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04
Rank by throughput rather than spec sheet
Ranking by tokens per second for Ring-1T follows memory bandwidth, not core counts.
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05
Read the fit column last
Tight means Ring-1T loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
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06
See what else that card runs
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Ring-1T.
Answers
Ring-1T — common questions
How many parameters does Ring-1T have?
Ring-1T has 1T parameters. 1 trillion total parameters with 50 billion activated parameters. 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.
Who created Ring-1T?
Ring-1T was published by Ant Group, based in China, categorised as industry.
When was Ring-1T released?
Ring-1T was published in October 2025.
What is Ring-1T used for?
Ring-1T works in Language, and is recorded as handling language modeling/generation, Question answering, Quantitative reasoning, Code generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download Ring-1T?
Its weights are published under the inclusionAI organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
Can I run Ring-1T if it does not fit in my GPU?
Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded Ring-1T is rarely worth using — the nearest miss we calculate is short by 346.5 GB. Every figure here assumes the whole model is on the card.
Would two GPUs run Ring-1T faster?
A second card roughly doubles the memory available but not the generation rate. With 0 cards already able to run Ring-1T alone, the case for pairing is weak.
Why does the quantisation differ between cards for Ring-1T?
Because capacity varies, so does how hard Ring-1T has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these Ring-1T speed estimates?
They are calculated from specifications rather than measured, and each carries a range — the range beneath each figure, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
Is Ring-1T open source?
Its weights are published, so Ring-1T can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
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.