Playground v2.5 TPS calculator

Open weights Playground 3.5B parameters February 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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q5_K_M · 18.8 tok/s

Fastest card

B200

968 tok/s · 180 GB

Which GPUs can run Playground v2.5?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
968 tok/s 581–1,549 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	4.4 GB	Q8_0	Comfortable
968 tok/s 581–1,549 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	4.4 GB	Q8_0	Comfortable
773 tok/s 464–1,237 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	4.4 GB	Q8_0	Comfortable
773 tok/s 464–1,237 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	4.4 GB	Q8_0	Comfortable
618 tok/s 371–989 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	4.4 GB	Q8_0	Comfortable
592 tok/s 355–947 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	4.4 GB	Q8_0	Comfortable
592 tok/s 355–947 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	4.4 GB	Q8_0	Comfortable
566 tok/s 340–906 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	4.4 GB	Q8_0	Comfortable
503 tok/s 302–804 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	4.4 GB	Q8_0	Comfortable
503 tok/s 302–804 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	4.4 GB	Q8_0	Comfortable
503 tok/s 302–804 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	4.4 GB	Q8_0	Comfortable
477 tok/s 286–763 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	4.4 GB	Q8_0	Comfortable
407 tok/s 244–651 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	4.4 GB	Q8_0	Comfortable
407 tok/s 244–651 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	4.4 GB	Q8_0	Comfortable
407 tok/s 244–651 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	4.4 GB	Q8_0	Comfortable
407 tok/s 244–651 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	4.4 GB	Q8_0	Comfortable
407 tok/s 244–651 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	4.4 GB	Q8_0	Comfortable
310 tok/s 186–495 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	4.4 GB	Q8_0	Comfortable
310 tok/s 186–495 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	4.4 GB	Q8_0	Comfortable
258 tok/s 155–413 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	4.4 GB	Q8_0	Comfortable
252 tok/s 151–404 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	4.4 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	4.4 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	4.4 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	4.4 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	4.4 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: Playground
Organisation type: Industry
Country: United States of America
Published: 27 February 2024
Authors: Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, Suhail Doshi

What it does

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

Domain: Image generation
Task: Text-to-image, Image generation

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: 3.5B
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 (restricted use)
Training code: Unreleased
Hugging Face: playgroundai

How it is classified

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

Record confidence: Unknown
Citations: 234

Sources

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

Reference: Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Playground v2.5

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 Playground v2.5

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

Tesla C1080

Memory needed

3.2 GB

Fastest

968 tok/s

Playground v2.5 is small enough at 3.5B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The least hardware that works is a Tesla C1080. Its 4 GB is enough at Q5_K_M compression, giving roughly 18.8 tokens per second.

A B200 is the fastest we calculate for it: about 968 tokens per second, from 8,000 GB/s of memory bandwidth.

About this model

Playground v2.5 was published by Playground, in United States of America, in February 2024. The organisation is categorised as industry.

It works in Image generation, and is recorded as doing text-to-image, Image generation.

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. It is published under the playgroundai organisation on Hugging Face.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 32.5 tokens per second, and 778 of them clear the ten tokens per second that roughly matches reading speed.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

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.

Step by step

How to choose a GPU for Playground v2.5

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

01

Start from the memory column

The table lists every card that can hold Playground v2.5 — around 3.2 GB at Q5_K_M. That figure, not the card's headline performance, is what decides whether it runs.
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 Playground v2.5 stops fitting a card that seemed fine.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold — Q5_K_M on the smallest card that fits. Setting a floor drops the cards that only manage Playground v2.5 by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for Playground v2.5 follows memory bandwidth, not core counts, which is why the B200 tops it at 968 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage Playground v2.5 from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Playground v2.5.

Answers

Playground v2.5 — common questions

Can I run Playground v2.5 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. Our figures for Playground v2.5 assume it is fully resident.

Would two GPUs run Playground v2.5 faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold Playground v2.5 on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Playground v2.5?

A larger card holds a more accurate copy. Across the cards that run Playground v2.5, 2 compression levels are used; the floor control above pins it to one.

How accurate are these Playground v2.5 speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 581–1,549 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

What GPU do I need to run Playground v2.5?

The smallest card in our catalogue that holds Playground v2.5 is the Tesla C1080, with 4 GB of memory. It runs the model at Q5_K_M using about 3.2 GB, and produces roughly 18.8 tokens per second. 818 cards in total can run it.

How fast is Playground v2.5 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 968 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 778 of the cards that can run Playground v2.5 clear that.

How much VRAM does Playground v2.5 need?

About 3.2 GB at Q5_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 Playground v2.5 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 4.4 GB and generating roughly 180 tokens per second — a comfortable fit.

Can I run Playground v2.5 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 4.4 GB and generating roughly 110 tokens per second — a comfortable fit.

Can I run Playground v2.5 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 4.4 GB and generating roughly 137 tokens per second — a comfortable fit.

Can I run Playground v2.5 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 4.4 GB and generating roughly 162 tokens per second — a comfortable fit.

Is Playground v2.5 open source?

Its weights are published, so Playground v2.5 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 Playground v2.5 have?

Playground v2.5 has 3.5B parameters. The Model Description section of [1] says that the model “follows the same architecture as Stable Diffusion XL.” It also says that the model is a latent diffusion model that uses OpenCLIP-ViT/G and CLIP-ViT/L as text encoders, which seemingly would make it identical to Stable Diffusion XL [2]. Therefore, I will assume that Playground v2.5 has the same number of parameters as the Stable Diffusion XL 1.0 base model, which has 3.5B model parameters [3]. 1. https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic 2. https://arxiv.org/pdf/2307.01952 3. https://stability.ai/news/stable-diffusion-sdxl-1-announcement. 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 Playground v2.5?

Playground v2.5 was published by Playground, based in United States of America, categorised as industry.

When was Playground v2.5 released?

Playground v2.5 was published in February 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 Playground v2.5 used for?

Playground v2.5 works in Image generation, and is recorded as handling text-to-image, Image generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Playground v2.5?

Its weights are published under the playgroundai 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 25 May 2026

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