MoE-1.1T 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 MoE-1.1T?
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
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
No card in our catalogue can run this model with these settings. |
|||||||
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
- Meta AI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 20 December 2021
- Authors
- Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giri Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Ves Stoyanov
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
- 1.1T
- Training data
- 112,000,000,000 tokens
- Epochs
- 2.68
112B tokens, or 84B words at 0.75 English words/token. "We pretrain our models on a union of six Englishlanguage datasets, including the five datasets used to pretrain RoBERTa (Liu et al., 2019) and the English subset of CC100, totalling 112B tokens"
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
- 2.2 × 10²² FLOP
- How it was established
- Reported
Reported directly in paper. Authors calculate FLOPs analytically in appendix G
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 A100
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 (non-commercial)
- Training code
- Unreleased
for research use only https://github.com/facebookresearch/fairseq/tree/main/examples/moe_lm I don't see training code
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 241
Sources
Where this record came from and when it was last checked.
- Reference
- Efficient Large Scale Language Modeling with Mixtures of Experts
- Last updated
- 25 May 2026
What the numbers mean
Hardware requirements in practice
At 1.1T parameters, MoE-1.1T 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.
Where it came from
MoE-1.1T was published by Meta AI, in United States of America, in December 2021. industry is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling/generation, Question answering.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
What went into building it
The training run consumed about 2.2 × 10²² FLOP, on NVIDIA A100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
It was trained on about 112,000,000,000 tokens of text.
Step by step
How to choose a GPU for MoE-1.1T
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Start from the memory column
Every card here has been checked against MoE-1.1T. Capacity is the gate — a card either holds it or it does not.
-
02
Decide how long your conversations run
Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for MoE-1.1T.
-
03
Choose how far you will compress it
Each card runs the least-compressed copy it can hold. Setting a floor drops the cards that only manage MoE-1.1T by squeezing it further than you would want.
-
04
Compare tokens per second, not specifications
Sort by speed to see how cards rank for MoE-1.1T. It will not match a gaming ordering — generation is bound by memory bandwidth.
-
05
Check the fit verdict before buying
Tight means MoE-1.1T 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.
-
06
Check the card from the other side
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond MoE-1.1T.
Answers
MoE-1.1T — common questions
Is MoE-1.1T open source?
Its weights are published, so MoE-1.1T 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.
How many parameters does MoE-1.1T have?
MoE-1.1T has 1.1T 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 MoE-1.1T?
MoE-1.1T was published by Meta AI, based in United States of America, categorised as industry.
When was MoE-1.1T released?
MoE-1.1T was published in December 2021. 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 MoE-1.1T used for?
MoE-1.1T works in Language, and is recorded as handling language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download MoE-1.1T?
The weights for MoE-1.1T are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
How much compute was used to train MoE-1.1T?
Around 2.2 × 10²² FLOP, on NVIDIA A100. 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.
Can I run MoE-1.1T if it does not fit in my GPU?
It can be split between the card and system memory, but MoE-1.1T generates painfully slowly that way — the nearest miss we calculate is short by 407.0 GB. Nothing on this page assumes offloading.
Would two GPUs run MoE-1.1T faster?
Two cards buy memory rather than speed. That matters for MoE-1.1T only if one card cannot hold it — 0 can, so a second adds little.
Why does the quantisation differ between cards for MoE-1.1T?
Because capacity varies, so does how hard MoE-1.1T has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these MoE-1.1T 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.
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.