Ring-2.6-1T 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
818 cards we hold specifications for
Which GPUs can run Ring-2.6-1T?
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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No card in our catalogue can run this model with these settings. |
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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
- 14 May 2026
- Authors
- Ang Li, Ben Liu, Bin Han, Bin Hu, Bin Jing, Binbin Hu, Bing Li, Cai Chen, Caizhi Tang, Changxin Tian, Chao Huang, Chao Zhang, Chen Liang, Chen Qian, Chengfu Tang, Chengyao Wen, Chilin Fu, Chunwei Wu, Cong Zhang, Cunyin Peng, Daixin Wang, Dalong Zhang, Deng Zhao, Dingnan Jin, Dingyuan Zhu, Donghao Zhang, Fan Yuan, Fangzheng Zhao, Fanzhuang Meng, Feifan Wu, Feng Xu, Fengbin Fang, Gangshan Wang, Guod…
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
- 1T
- Training data
- 9,600,000,000,000 tokens
1B total, 63B active
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)
- Hugging Face
- inclusionAI
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
- Last updated
- 11 August 2026
What the numbers mean
What it takes to run this model
Ring-2.6-1T reaches a parameter count of 1T. That is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job, and every card able to hold it alone is a datacentre part. The number that can: 0.
What this model is
Ring-2.6-1T was published by Ant Group, in the country recorded as China, during May 2026. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Question answering.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. On Hugging Face it is published under the organisation inclusionAI.
How it was trained
Training consumed a corpus of around 9,600,000,000,000 tokens of text.
Step by step
How to choose a GPU for Ring-2.6-1T
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
Start from what it actually needs, which is the requirement of Ring-2.6-1T. Capacity is the gate — a card either holds it or it does not.
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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 a card that seemed fine stops fitting Ring-2.6-1T.
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03
Set a quality floor
Each card runs the least-compressed copy it can hold. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
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04
Rank by throughput rather than spec sheet
Sort by speed to see how cards rank for Ring-2.6-1T. It will not match a gaming ordering, because generation is bound by memory bandwidth.
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05
Look at the headroom, not just the fit
Tight means it loads and works with no room to raise the context later, in the case of Ring-2.6-1T. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
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06
Open the card you have settled on
Every card name links to its own page, which runs the same calculation across the whole model catalogue. A card is usually bought for more than one model, so it is worth a look before buying for Ring-2.6-1T.
Answers
Ring-2.6-1T — common questions
Ring-2.6-1T— why does the quantisation differ between cards?
A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.
Ring-2.6-1T— how accurate are these speed estimates?
They are calculated from specifications rather than measured, and each carries a range. One example: the range beneath each figure. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
Ring-2.6-1T— is it open source?
Its weights are published, so it 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.
Ring-2.6-1T— how many parameters does it have?
It has a parameter count of 1T. 1B total, 63B active. 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.
Ring-2.6-1T— who created it?
It was published by Ant Group, based in China, an organisation categorised as industry.
Ring-2.6-1T— when was it released?
It was published in May 2026.
Ring-2.6-1T— 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. 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.
Ring-2.6-1T— where can I download it?
Its weights are published on Hugging Face, under the organisation inclusionAI. We do not host model files — this site calculates what hardware is needed to run them.
Ring-2.6-1T— can I run it if it does not fit in my GPU?
It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 282.3 GB. Every figure here assumes the whole model is resident on the card.
Ring-2.6-1T— would two GPUs run it faster?
Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 0. So a second card is rarely the answer here.
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.