Ling-mini-base-2.0-20T TPS calculator

Open weights Ant Group 16B parameters September 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

306 of 818 cards that can run it

Smallest card that fits

P102-101

10 GB · Q3_K_M · 19.5 tok/s

Fastest card

B200

212 tok/s · 180 GB

Which GPUs can run Ling-mini-base-2.0-20T?

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

306 cards match

Calculating

					Needs	Quantisation	Fit
212 tok/s 127–339 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	17.8 GB	Q8_0	Comfortable
212 tok/s 127–339 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	17.8 GB	Q8_0	Comfortable
169 tok/s 101–271 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	17.8 GB	Q8_0	Comfortable
169 tok/s 101–271 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	17.8 GB	Q8_0	Comfortable
135 tok/s 81–216 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	17.8 GB	Q8_0	Comfortable
129 tok/s 78–207 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	17.8 GB	Q8_0	Comfortable
129 tok/s 78–207 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	17.8 GB	Q8_0	Comfortable
124 tok/s 74–198 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	17.8 GB	Q8_0	Comfortable
111 tok/s 67–178 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.5 GB	Q3_K_M	Tight
110 tok/s 66–176 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	17.8 GB	Q8_0	Comfortable
110 tok/s 66–176 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	17.8 GB	Q8_0	Comfortable
110 tok/s 66–176 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	17.8 GB	Q8_0	Comfortable
104 tok/s 63–167 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	17.8 GB	Q8_0	Comfortable
88.9 tok/s 53–142 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	17.8 GB	Q8_0	Comfortable
88.9 tok/s 53–142 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	17.8 GB	Q8_0	Comfortable
88.9 tok/s 53–142 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	17.8 GB	Q8_0	Comfortable
88.9 tok/s 53–142 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	17.8 GB	Q8_0	Comfortable
88.9 tok/s 53–142 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	17.8 GB	Q8_0	Comfortable
67.7 tok/s 41–108 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	17.8 GB	Q8_0	Comfortable
67.7 tok/s 41–108 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	17.8 GB	Q8_0	Comfortable
56.4 tok/s 34–90 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	17.8 GB	Q8_0	Comfortable
55.8 tok/s 33–89 · low confidence	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	10.4 GB	Q4_K_M	Tight
55.8 tok/s 33–89 · low confidence	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	10.4 GB	Q4_K_M	Tight
55.2 tok/s 33–88 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	17.8 GB	Q8_0	Comfortable
54.3 tok/s 33–87 · low confidence	CMP 90HX NVIDIA	10 GB	760 GB/s	Jul 2021	8.5 GB	Q3_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: Ant Group
Organisation type: Industry
Country: China
Published: 10 September 2025
Authors: Today, we are excited to announce the open-sourcing of Ling 2.0 — a family of MoE-based large language models that combine SOTA performance with high efficiency. The first released version, Ling-mini-2.0, is compact yet powerful. It has 16B total parameters, but only 1.4B are activated per input token (non-embedding 789M). Trained on more than 20T tokens of high-quality data and enhanced through m…

What it does

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

Domain: Language
Task: Language modeling/generation, Question answering
Numerical format: FP8

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: 16B
Training data: 20,000,000,000,000 tokens

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: 1.7 × 10²³ FLOP
How it was established: Operation counting

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)
Training code: Unreleased
Hugging Face: inclusionAI

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: Ling-mini-base-2.0-20T
Last updated: 28 November 2025

The extremes

The ten fastest GPUs for Ling-mini-base-2.0-20T

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 Ling-mini-base-2.0-20T

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

P102-101

Memory needed

8.5 GB

Fastest

212 tok/s

Ling-mini-base-2.0-20T is small enough at 16B parameters that hardware is rarely the obstacle — 306 of the cards we track can run it, including cards several years old.

The entry point is the P102-101: 10 GB of memory, Q3_K_M compression, roughly 19.5 tokens per second.

The quickest result comes from a B200 at around 212 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

Ling-mini-base-2.0-20T was published by Ant Group, in China, in September 2025. industry is the category the publisher falls under.

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

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the inclusionAI organisation on Hugging Face.

Reading the throughput figures

Half the cards that hold it manage more than 19.8 tokens per second, and 259 exceed reading speed outright.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

How it was trained

Producing it required around 1.7 × 10²³ FLOP of arithmetic, which is a statement about the training budget rather than about inference.

Around 20,000,000,000,000 tokens went into training it.

Step by step

How to choose a GPU for Ling-mini-base-2.0-20T

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

01

Check what it needs before anything else

Look at what Ling-mini-base-2.0-20T actually needs — around 8.5 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
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 Ling-mini-base-2.0-20T stops fitting a card that seemed fine.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of Ling-mini-base-2.0-20T — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for Ling-mini-base-2.0-20T follows memory bandwidth, not core counts, which is why the B200 tops it at 212 tok/s.
05

Read the fit column last

A tight fit runs Ling-mini-base-2.0-20T 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

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Ling-mini-base-2.0-20T.

Answers

Ling-mini-base-2.0-20T — common questions

How much compute was used to train Ling-mini-base-2.0-20T?

Around 1.7 × 10²³ FLOP. 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 Ling-mini-base-2.0-20T 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 3.2 GB. Our figures for Ling-mini-base-2.0-20T assume it is fully resident.

Would two GPUs run Ling-mini-base-2.0-20T faster?

Two cards buy memory rather than speed. That matters for Ling-mini-base-2.0-20T only if one card cannot hold it — 306 can, so a second adds little.

Why does the quantisation differ between cards for Ling-mini-base-2.0-20T?

A larger card holds a more accurate copy. Across the cards that run Ling-mini-base-2.0-20T, 5 compression levels are used; the floor control above pins it to one.

How accurate are these Ling-mini-base-2.0-20T speed estimates?

These are estimates with real error bars. The fastest result here, 127–339 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run Ling-mini-base-2.0-20T?

The smallest card in our catalogue that holds Ling-mini-base-2.0-20T is the P102-101, with 10 GB of memory. It runs the model at Q3_K_M using about 8.5 GB, and produces roughly 19.5 tokens per second. 306 cards in total can run it.

How fast is Ling-mini-base-2.0-20T on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 212 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 259 of the cards that can run Ling-mini-base-2.0-20T clear that.

How much VRAM does Ling-mini-base-2.0-20T need?

About 8.5 GB at Q3_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.

Can I run Ling-mini-base-2.0-20T on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q4_K_M, using about 10.4 GB and generating roughly 55.8 tokens per second — a tight fit.

Can I run Ling-mini-base-2.0-20T on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q6_K, using about 14.1 GB and generating roughly 43.5 tokens per second — a tight fit.

Can I run Ling-mini-base-2.0-20T on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 17.8 GB and generating roughly 35.5 tokens per second — a comfortable fit.

Is Ling-mini-base-2.0-20T open source?

Its weights are published, so Ling-mini-base-2.0-20T 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 Ling-mini-base-2.0-20T have?

Ling-mini-base-2.0-20T has 16B parameters. It has 16B total parameters, but only 1.4B are activated per input token (non-embedding 789M). 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 Ling-mini-base-2.0-20T?

Ling-mini-base-2.0-20T was published by Ant Group, based in China, categorised as industry.

When was Ling-mini-base-2.0-20T released?

Ling-mini-base-2.0-20T was published in September 2025.

What is Ling-mini-base-2.0-20T used for?

Ling-mini-base-2.0-20T works in Language, and is recorded as handling language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Ling-mini-base-2.0-20T?

Its weights are published under the inclusionAI organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

Source

Original publication

Record last updated 28 November 2025

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