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
818 cards we hold specifications for
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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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
- 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
Kimi K2.7 Code 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.
About this model
Kimi K2.7 Code was published by Moonshot, in the country recorded as China, during June 2026. The category the publisher falls under is industry.
It works in the domain of Language, Multimodal, Vision, and is recorded as performing the task of language modeling/generation, Code generation.
It builds on Kimi K2.6. Most models at this scale are adapted from an existing base rather than built from nothing.
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. On Hugging Face it is published under the organisation moonshotai.
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. That figure, not the headline performance of a card, is what decides whether it runs.
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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 a card that seemed fine stops fitting Kimi K2.7 Code.
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03
Choose how far you will compress it
Compression is what makes a model fit smaller cards, at some cost in accuracy. 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
Ranking by tokens per second follows memory bandwidth rather than core counts, for Kimi K2.7 Code. It will not match a gaming ordering, because generation is bound by memory bandwidth.
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05
Read the fit column last
Tight means it loads and works with no room to raise the context later, in the case of Kimi K2.7 Code. 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
Check the card from the other side
Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond Kimi K2.7 Code.
Answers
Kimi K2.7 Code — common questions
Kimi K2.7 Code— would two GPUs run it faster?
Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 0. So a second card is rarely the answer here.
Kimi K2.7 Code— why does the quantisation differ between cards?
Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.
Kimi K2.7 Code— 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.
Kimi K2.7 Code— 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.
Kimi K2.7 Code— how many parameters does it have?
It has a parameter count of 1T. 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.
Kimi K2.7 Code— who created it?
It was published by Moonshot, based in China, an organisation categorised as industry.
Kimi K2.7 Code— when was it released?
It was published in June 2026.
Kimi K2.7 Code— what is it used for?
It works in the domain of Language, Multimodal, Vision, and is recorded as handling the task of language modeling/generation, Code generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Kimi K2.7 Code— where can I download it?
Its weights are published on Hugging Face, under the organisation moonshotai. We do not host model files — this site calculates what hardware is needed to run them.
Kimi K2.7 Code— can I run it if it does not fit in my GPU?
Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. The nearest miss we calculate falls short by 370.7 GB. Every figure here assumes the whole model is resident on the card.
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.