Stable LM 2 12B TPS calculator

Open weights Stability AI 12.1B parameters April 2024

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

509 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 5110P

8 GB · Q3_K_M · 19.6 tok/s

Fastest card

B200

279 tok/s · 180 GB

Which GPUs can run Stable LM 2 12B?

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.

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Card memory

Also show cards that cannot run it

509 cards match

Calculating

					Needs	Quantisation	Fit
279 tok/s 167–446 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	13.7 GB	Q8_0	Comfortable
279 tok/s 167–446 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	13.7 GB	Q8_0	Comfortable
223 tok/s 134–356 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	13.7 GB	Q8_0	Comfortable
223 tok/s 134–356 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	13.7 GB	Q8_0	Comfortable
178 tok/s 107–285 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	13.7 GB	Q8_0	Comfortable
171 tok/s 102–273 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	13.7 GB	Q8_0	Comfortable
171 tok/s 102–273 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	13.7 GB	Q8_0	Comfortable
163 tok/s 98–261 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	13.7 GB	Q8_0	Comfortable
145 tok/s 87–232 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	13.7 GB	Q8_0	Comfortable
145 tok/s 87–232 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	13.7 GB	Q8_0	Comfortable
145 tok/s 87–232 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	13.7 GB	Q8_0	Comfortable
140 tok/s 84–224 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.6 GB	Q3_K_M	Tight
137 tok/s 82–220 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	13.7 GB	Q8_0	Comfortable
126 tok/s 75–201 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.0 GB	Q4_K_M	Tight
117 tok/s 70–188 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	13.7 GB	Q8_0	Comfortable
117 tok/s 70–188 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	13.7 GB	Q8_0	Comfortable
117 tok/s 70–188 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	13.7 GB	Q8_0	Comfortable
117 tok/s 70–188 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	13.7 GB	Q8_0	Comfortable
117 tok/s 70–188 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	13.7 GB	Q8_0	Comfortable
89.2 tok/s 54–143 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	13.7 GB	Q8_0	Comfortable
89.2 tok/s 54–143 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	13.7 GB	Q8_0	Comfortable
74.4 tok/s 45–119 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	13.7 GB	Q8_0	Comfortable
72.8 tok/s 44–116 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	13.7 GB	Q8_0	Comfortable
72.3 tok/s 43–116 · low confidence	RTX A5000-8Q NVIDIA	8 GB	768 GB/s	Apr 2021	6.6 GB	Q3_K_M	Tight
71.2 tok/s 43–114 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	13.7 GB	Q8_0	Comfortable

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: Stability AI
Organisation type: Industry
Country: United Kingdom of Great Britain and Northern Ireland
Published: 8 April 2024

What it does

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

Domain: Language
Task: Language modeling/generation, Translation
Approach: Self-supervised learning
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: 12.1B
Training data: 2,000,000,000,000 tokens
Epochs: 2

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: 2.9 × 10²³ FLOP
How it was established: Operation counting

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 SXM5 80GB

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 (restricted use)
Training code: Open source

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Likely above 10²³ FLOP: Yes
Record confidence: Confident

Sources

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

Reference: Introducing Stable LM 2 12B
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Stable LM 2 12B

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 Stable LM 2 12B

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

What you need to run it

Minimum card

Xeon Phi 5110P

Memory needed

6.6 GB

Fastest

279 tok/s

Stable LM 2 12B is small enough at 12.1B parameters that hardware is rarely the obstacle — 509 of the cards we track can run it, including cards several years old.

The least hardware that works is a Xeon Phi 5110P. Its 8 GB is enough at Q3_K_M compression, giving roughly 19.6 tokens per second.

At the other end, a B200 generates roughly 279 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

About this model

Stable LM 2 12B was published by Stability AI, in United Kingdom of Great Britain and Northern Ireland, in April 2024. It comes out of industry.

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

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.

How fast it runs, and why

The median result is around 21.0 tokens per second; 455 cards produce text faster than most people read it.

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

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

Producing it required around 2.9 × 10²³ FLOP of arithmetic, on NVIDIA H100 SXM5 80GB, which is a statement about the training budget rather than about inference.

Around 2,000,000,000,000 tokens went into training it.

Step by step

How to choose a GPU for Stable LM 2 12B

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 Stable LM 2 12B actually needs — around 6.6 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

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Stable LM 2 12B.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of Stable LM 2 12B — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Sort by speed

Sort by speed to see how cards rank for Stable LM 2 12B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 279 tok/s.
05

Look at the headroom, not just the fit

Tight means Stable LM 2 12B 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.
06

See what else that card runs

Following a card through to its own page shows every other model it can hold, which is the question that follows once Stable LM 2 12B is settled.

Answers

Stable LM 2 12B — common questions

When was Stable LM 2 12B released?

Stable LM 2 12B was published in April 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

What is Stable LM 2 12B used for?

Stable LM 2 12B works in Language, and is recorded as handling language modeling/generation, Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Stable LM 2 12B?

The weights for Stable LM 2 12B are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

How much compute was used to train Stable LM 2 12B?

Around 2.9 × 10²³ FLOP, on NVIDIA H100 SXM5 80GB. 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 Stable LM 2 12B 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 2.6 GB. Our figures for Stable LM 2 12B assume it is fully resident.

Would two GPUs run Stable LM 2 12B faster?

Two cards buy memory rather than speed. That matters for Stable LM 2 12B only if one card cannot hold it — 509 can, so a second adds little.

Why does the quantisation differ between cards for Stable LM 2 12B?

A larger card holds a more accurate copy. Across the cards that run Stable LM 2 12B, 4 compression levels are used; the floor control above pins it to one.

How accurate are these Stable LM 2 12B 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 167–446 tok/s on the B200 rather than a single number.

What GPU do I need to run Stable LM 2 12B?

The smallest card in our catalogue that holds Stable LM 2 12B is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at Q3_K_M using about 6.6 GB, and produces roughly 19.6 tokens per second. 509 cards in total can run it.

How fast is Stable LM 2 12B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 279 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 455 of the cards that can run Stable LM 2 12B clear that.

How much VRAM does Stable LM 2 12B need?

About 6.6 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 Stable LM 2 12B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q3_K_M, using about 6.6 GB and generating roughly 140 tokens per second — a tight fit.

Can I run Stable LM 2 12B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q5_K_M, using about 9.5 GB and generating roughly 56.8 tokens per second — a tight fit.

Can I run Stable LM 2 12B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 13.7 GB and generating roughly 39.4 tokens per second — a tight fit.

Can I run Stable LM 2 12B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 13.7 GB and generating roughly 46.7 tokens per second — a comfortable fit.

Is Stable LM 2 12B open source?

Its weights are published, so Stable LM 2 12B 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 Stable LM 2 12B have?

Stable LM 2 12B has 12.1B parameters. Precise number given in HF model card. 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 Stable LM 2 12B?

Stable LM 2 12B was published by Stability AI, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.

Source

Original publication

Record last updated 28 November 2025

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