Inkling 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 Inkling?
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
- Thinking Machines
- Organisation type
- Industry
- Country
- United States of America
- Published
- 15 July 2026
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Multimodal, Audio, Video, Vision
- Task
- Language modeling/generation, Chat
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
- 975B
- Training data
- 45,000,000,000,000 tokens
975B total, 41B active
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
- 1.8 × 10²⁴ FLOP
6ND = 41e9 active parameters * 45e12 tokens = 1.845e+24 FLOP
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 GB300 (Blackwell Ultra)
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
- thinkingmachines
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Discretionary
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Inkling: Our open-weights model
- Last updated
- 18 July 2026
What the numbers mean
What you need to run it
At 975B parameters, Inkling 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.
What this model is
Inkling was published by Thinking Machines, in United States of America, in July 2026. It comes out of industry.
It works in Language, Multimodal, Audio, Video, Vision, and is recorded as doing language modeling/generation, Chat.
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 thinkingmachines organisation on Hugging Face.
Training and provenance
Producing it required around 1.8 × 10²⁴ FLOP of arithmetic, on NVIDIA GB300 (Blackwell Ultra), which is a statement about the training budget rather than about inference.
It was trained on about 45,000,000,000,000 tokens of text.
Its inclusion criterion is discretionary.
Step by step
How to choose a GPU for Inkling
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 Inkling actually needs. No amount of processing power compensates for a card that cannot hold it.
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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 Inkling.
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03
Set a quality floor
The quantisation column varies by card, because a bigger card holds a more accurate copy of Inkling. Set a floor to hold the comparison at one level.
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04
Compare tokens per second, not specifications
Sort by speed to see how cards rank for Inkling. It will not match a gaming ordering — generation is bound by memory bandwidth.
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05
Read the fit column last
The fit column separates cards that just manage Inkling from those with room to spare. Buy for the second if the context might grow.
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06
See what else that card runs
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Inkling.
Answers
Inkling — common questions
Would two GPUs run Inkling faster?
Capacity adds across cards; throughput does not. Since 0 of the cards we track already hold Inkling on their own, a second card is rarely the answer here.
Why does the quantisation differ between cards for Inkling?
Because capacity varies, so does how hard Inkling has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these Inkling 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 Inkling open source?
Its weights are published, so Inkling 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 Inkling have?
Inkling has 975B parameters. 975B total, 41B 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.
Who created Inkling?
Inkling was published by Thinking Machines, based in United States of America, categorised as industry.
When was Inkling released?
Inkling was published in July 2026.
What is Inkling used for?
Inkling works in Language, Multimodal, Audio, Video, Vision, and is recorded as handling language modeling/generation, Chat. 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.
Where can I download Inkling?
Its weights are published under the thinkingmachines organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
How much compute was used to train Inkling?
Around 1.8 × 10²⁴ FLOP, on NVIDIA GB300 (Blackwell Ultra). 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 Inkling if it does not fit in my GPU?
Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded Inkling is rarely worth using — the nearest miss we calculate is short by 263.3 GB. Every figure here assumes the whole model is 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.