GLM-5.2 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 GLM-5.2?
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
- Z.ai (Zhipu AI)
- Organisation type
- Industry
- Country
- China
- Published
- 16 June 2026
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
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
- 744B
- Training data
- tokens
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
- zai-org
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
- GLM-5.2: Built for Long-Horizon Tasks
- Last updated
- 10 July 2026
What the numbers mean
What it takes to run this model
At 744B parameters, GLM-5.2 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
GLM-5.2 was published by Z.ai (Zhipu AI), in China, in June 2026. industry is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling/generation.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. It is published under the zai-org organisation on Hugging Face.
Step by step
How to choose a GPU for GLM-5.2
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
Read the memory figure first
Look at what GLM-5.2 actually needs. No amount of processing power compensates for a card that cannot hold it.
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02
Set the context length you will work at
Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason GLM-5.2 stops fitting a card that seemed fine.
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03
Decide how much compression you will accept
The quantisation column varies by card, because a bigger card holds a more accurate copy of GLM-5.2. Set a floor to hold the comparison at one level.
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04
Compare tokens per second, not specifications
The speed ordering for GLM-5.2 is effectively an ordering by memory bandwidth.
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05
Check the fit verdict before buying
The fit column separates cards that just manage GLM-5.2 from those with room to spare. Buy for the second if the context might grow.
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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. Worth a look before buying for GLM-5.2 alone — a card is usually bought for more than one model.
Answers
GLM-5.2 — common questions
Can I run GLM-5.2 if it does not fit in my GPU?
Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down — the nearest miss we calculate is short by 191.6 GB. Our figures for GLM-5.2 assume it is fully resident.
Would two GPUs run GLM-5.2 faster?
Two cards buy memory rather than speed. That matters for GLM-5.2 only if one card cannot hold it — 0 can, so a second adds little.
Why does the quantisation differ between cards for GLM-5.2?
A larger card holds a more accurate copy. Across the cards that run GLM-5.2, 1 compression levels are used; the floor control above pins it to one.
How accurate are these GLM-5.2 speed estimates?
Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as the range beneath each figure rather than a single number.
Is GLM-5.2 open source?
Its weights are published, so GLM-5.2 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 GLM-5.2 have?
GLM-5.2 has 744B 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 GLM-5.2?
GLM-5.2 was published by Z.ai (Zhipu AI), based in China, categorised as industry.
When was GLM-5.2 released?
GLM-5.2 was published in June 2026.
What is GLM-5.2 used for?
GLM-5.2 works in Language, and is recorded as handling language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download GLM-5.2?
Its weights are published under the zai-org organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
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.