Kimi K2.7 Code 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 Kimi K2.7 Code?
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
- Moonshot
- Organisation type
- Industry
- Country
- China
- Published
- 12 June 2026
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Multimodal, Vision
- Task
- Language modeling/generation, Code generation
- Base model
- Kimi K2.6
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
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
- moonshotai
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
- Kimi K2.7 Code
- Last updated
- 8 July 2026
What the numbers mean
The hardware side
At 1T parameters, Kimi K2.7 Code 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
Kimi K2.7 Code was published by Moonshot, in China, in June 2026. industry is the category the publisher falls under.
It works in Language, Multimodal, Vision, and is recorded as doing language modeling/generation, Code generation.
It builds on Kimi K2.6, which is why it shares that model's general shape and size.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold. It is published under the moonshotai organisation on Hugging Face.
Step by step
How to choose a GPU for Kimi K2.7 Code
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
Check what it needs before anything else
Every card here has been checked against Kimi K2.7 Code. Capacity is the gate — a card either holds it or it does not.
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02
Decide how long your conversations run
Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason Kimi K2.7 Code stops fitting a card that seemed fine.
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03
Choose how far you will compress it
Compression is what makes Kimi K2.7 Code 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 Kimi K2.7 Code follows memory bandwidth, not core counts.
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05
Read the fit column last
Tight means Kimi K2.7 Code 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
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 Kimi K2.7 Code.
Answers
Kimi K2.7 Code — common questions
Would two GPUs run Kimi K2.7 Code faster?
Two cards buy memory rather than speed. That matters for Kimi K2.7 Code only if one card cannot hold it — 0 can, so a second adds little.
Why does the quantisation differ between cards for Kimi K2.7 Code?
Because capacity varies, so does how hard Kimi K2.7 Code has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these Kimi K2.7 Code 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 Kimi K2.7 Code open source?
Its weights are published, so Kimi K2.7 Code 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 Kimi K2.7 Code have?
Kimi K2.7 Code has 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 Kimi K2.7 Code?
Kimi K2.7 Code was published by Moonshot, based in China, categorised as industry.
When was Kimi K2.7 Code released?
Kimi K2.7 Code was published in June 2026.
What is Kimi K2.7 Code used for?
Kimi K2.7 Code works in Language, Multimodal, Vision, and is recorded as handling language modeling/generation, Code generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download Kimi K2.7 Code?
Its weights are published under the moonshotai organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
Can I run Kimi K2.7 Code 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 370.7 GB. Our figures for Kimi K2.7 Code assume it is fully resident.
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.