VAETKI TPS calculator

Open weights NC AI 100B parameters December 2025

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

43 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Radeon Instinct MI200

64 GB · Q4_K_M · 69.5 tok/s

Fastest card

B200

188 tok/s · 180 GB

Which GPUs can run VAETKI?

Set the inputs, read the answer

Context length tokens

A longer conversation needs more memory, which can push this model off smaller cards.

Minimum quality

Hides cards that would only fit the model by compressing it below this point.

Search cards

Card memory

Also show cards that cannot run it

43 cards match

Calculating

					Needs	Quantisation	Fit
188 tok/s 113–301 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	102.3 GB	Q8_0	Comfortable
188 tok/s 113–301 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	102.3 GB	Q8_0	Comfortable
150 tok/s 90–241 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	102.3 GB	Q8_0	Comfortable
150 tok/s 90–241 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	102.3 GB	Q8_0	Comfortable
141 tok/s 85–226 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	67.4 GB	Q5_K_M	Tight
141 tok/s 85–226 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	67.4 GB	Q5_K_M	Tight
135 tok/s 81–216 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	79.1 GB	Q6_K	Tight
120 tok/s 72–192 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	102.3 GB	Q8_0	Tight
115 tok/s 69–184 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	102.3 GB	Q8_0	Comfortable
115 tok/s 69–184 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	102.3 GB	Q8_0	Comfortable
115 tok/s 69–184 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	79.1 GB	Q6_K	Tight
115 tok/s 69–184 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	79.1 GB	Q6_K	Tight
115 tok/s 69–184 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	79.1 GB	Q6_K	Tight
110 tok/s 66–176 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	102.3 GB	Q8_0	Comfortable
110 tok/s 66–176 · low confidence	H100 SXM5 64 GB NVIDIA	64 GB	2,020 GB/s	Mar 2023	55.8 GB	Q4_K_M	Tight
97.7 tok/s 59–156 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	102.3 GB	Q8_0	Tight
97.7 tok/s 59–156 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	102.3 GB	Q8_0	Comfortable
97.7 tok/s 59–156 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	102.3 GB	Q8_0	Comfortable
85.7 tok/s 51–137 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	67.4 GB	Q5_K_M	Tight
85.7 tok/s 51–137 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	67.4 GB	Q5_K_M	Tight
85.7 tok/s 51–137 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	67.4 GB	Q5_K_M	Tight
85.7 tok/s 51–137 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	67.4 GB	Q5_K_M	Tight
85.7 tok/s 51–137 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	67.4 GB	Q5_K_M	Tight
85.7 tok/s 51–137 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	67.4 GB	Q5_K_M	Tight
81.5 tok/s 49–130 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	67.4 GB	Q5_K_M	Tight

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: NC AI
Organisation type: Industry
Country: Korea (Republic of)
Published: 30 December 2025

What it does

The problem areas the model was built for. A model can carry several of each.

Domain: Language
Task: Language modeling/generation
Numerical format: BF16

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: 100B
Training data: tokens
Epochs: 1
Batch size: 8,000

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 H100 80G
Chips used: 1,016
Wall-clock time: 3,238 hours (134.9 days)

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)

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: Training cost
Record confidence: Confident

Sources

Where this record came from and when it was last checked.

Last updated: 3 January 2026

The extremes

The ten fastest GPUs that run VAETKI

Ranked by estimated tokens per second, newest card first where speeds tie. Because generation is bound by memory bandwidth, this ordering follows bandwidth rather than any gaming benchmark.

The smallest GPUs that still run VAETKI

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

What the numbers mean

Hardware requirements in practice

Minimum card

Radeon Instinct MI200

Memory needed

55.8 GB

Fastest

188 tok/s

VAETKI sits at 100B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 43 of the cards we track can hold it.

The entry point is the Radeon Instinct MI200: 64 GB of memory, Q4_K_M compression, roughly 69.5 tokens per second.

The quickest result comes from a B200 at around 188 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

VAETKI was published by NC AI, in Korea (Republic of), in December 2025. The organisation is categorised as industry.

It works in Language, and is recorded as doing language modeling/generation.

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.

Reading the throughput figures

Across every card that can run it, the middle of the range is about 85.7 tokens per second, and 41 of them clear the ten tokens per second that roughly matches reading speed.

Mixture-of-experts routing is why the speeds here look high for the parameter count. Only a fraction is read per token, but the whole thing has to be loaded.

Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.

Training and provenance

It is tracked in the underlying dataset for one reason in particular: training cost.

Step by step

How to choose a GPU for VAETKI

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Read the memory figure first

Look at what VAETKI actually needs — around 55.8 GB at Q4_K_M. No amount of processing power compensates for a card that cannot hold it.
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 VAETKI.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold — Q4_K_M on the smallest card that fits. Setting a floor drops the cards that only manage VAETKI by squeezing it further than you would want.
04

Compare tokens per second, not specifications

The speed ordering for VAETKI is effectively an ordering by memory bandwidth, which is why the B200 tops it at 188 tok/s.
05

Check the fit verdict before buying

A tight fit runs VAETKI but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Check the card from the other side

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for VAETKI alone — a card is usually bought for more than one model.

Answers

VAETKI — common questions

Is VAETKI open source?

Its weights are published, so VAETKI 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 VAETKI have?

VAETKI has 100B parameters. Total parameters: 100B (Sparse), Active parameters: 10B per token. It uses a Mixture-of-Experts architecture with 1 shared expert and 128 total experts(top-8 routing). 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 VAETKI?

VAETKI was published by NC AI, based in Korea (Republic of), categorised as industry.

When was VAETKI released?

VAETKI was published in December 2025.

What is VAETKI used for?

VAETKI 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 VAETKI?

The weights for VAETKI are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run VAETKI 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 VAETKI is rarely worth using — the nearest miss we calculate is short by 12.6 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run VAETKI faster?

Capacity adds across cards; throughput does not. Since 43 of the cards we track already hold VAETKI on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for VAETKI?

Because capacity varies, so does how hard VAETKI has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these VAETKI 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 113–301 tok/s on the B200 rather than a single number.

What GPU do I need to run VAETKI?

The smallest card in our catalogue that holds VAETKI is the Radeon Instinct MI200, with 64 GB of memory. It runs the model at Q4_K_M using about 55.8 GB, and produces roughly 69.5 tokens per second. 43 cards in total can run it.

How fast is VAETKI on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 188 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 41 of the cards that can run VAETKI clear that.

How much VRAM does VAETKI need?

About 55.8 GB at Q4_K_M compression, which is what the smallest card that runs it uses. Less compression needs more: the figures in the memory column above are recalculated for each card, because each one holds the least-compressed version it can.

Source

Original publication

Record last updated 3 January 2026

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.

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