SANA 1.5 4.8B TPS calculator

Open weights NVIDIA,Massachusetts Institute of Technology (MIT),Tsinghua University,Playground,Peking University,The University of Hong Kong 4.8B parameters May 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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · IQ4_XS · 18.9 tok/s

Fastest card

B200

706 tok/s · 180 GB

Which GPUs can run SANA 1.5 4.8B?

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
706 tok/s 424–1,129 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	5.8 GB	Q8_0	Comfortable
706 tok/s 424–1,129 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	5.8 GB	Q8_0	Comfortable
564 tok/s 338–902 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	5.8 GB	Q8_0	Comfortable
564 tok/s 338–902 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	5.8 GB	Q8_0	Comfortable
451 tok/s 270–721 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	5.8 GB	Q8_0	Comfortable
431 tok/s 259–690 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	5.8 GB	Q8_0	Comfortable
431 tok/s 259–690 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	5.8 GB	Q8_0	Comfortable
413 tok/s 248–661 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	5.8 GB	Q8_0	Comfortable
366 tok/s 220–586 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	5.8 GB	Q8_0	Comfortable
366 tok/s 220–586 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	5.8 GB	Q8_0	Comfortable
366 tok/s 220–586 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	5.8 GB	Q8_0	Comfortable
348 tok/s 209–556 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	5.8 GB	Q8_0	Comfortable
296 tok/s 178–474 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	5.8 GB	Q8_0	Comfortable
296 tok/s 178–474 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	5.8 GB	Q8_0	Comfortable
296 tok/s 178–474 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	5.8 GB	Q8_0	Comfortable
296 tok/s 178–474 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	5.8 GB	Q8_0	Comfortable
296 tok/s 178–474 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	5.8 GB	Q8_0	Comfortable
226 tok/s 135–361 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	5.8 GB	Q8_0	Comfortable
226 tok/s 135–361 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	5.8 GB	Q8_0	Comfortable
188 tok/s 113–301 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	5.8 GB	Q8_0	Comfortable
184 tok/s 110–295 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	5.8 GB	Q8_0	Comfortable
180 tok/s 108–288 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	5.8 GB	Q8_0	Comfortable
180 tok/s 108–288 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	5.8 GB	Q8_0	Comfortable
180 tok/s 108–288 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	5.8 GB	Q8_0	Comfortable
180 tok/s 108–288 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	5.8 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: NVIDIA,Massachusetts Institute of Technology (MIT),Tsinghua University,Playground,Peking University,The University of Hong Kong
Organisation type: Industry,Academia,Academia,Industry,Academia,Academia
Country: United States of America, China, Hong Kong
Published: 17 May 2025
Authors: Enze Xie, Junsong Chen, Yuyang Zhao, Jincheng Yu, Ligeng Zhu, Chengyue Wu, Yujun Lin, Zhekai Zhang, Muyang Li, Junyu Chen, Han Cai, Bingchen Liu, Daquan Zhou, Song Han

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
Base model: SANA 1.6B
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: 4.8B
Training data: tokens

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 A100
Chips used: 64
Power draw: 50.2 kW

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 (non-commercial)
Training code: Open source
Hugging Face: Efficient-Large-Model

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: SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run SANA 1.5 4.8B

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 SANA 1.5 4.8B

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

Tesla C1080

Memory needed

3.3 GB

Fastest

706 tok/s

SANA 1.5 4.8B is small enough at 4.8B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The entry point is the Tesla C1080: 4 GB of memory, IQ4_XS compression, roughly 18.9 tokens per second.

The quickest result comes from a B200 at around 706 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

SANA 1.5 4.8B was published by NVIDIA,Massachusetts Institute of Technology (MIT),Tsinghua University,Playground,Peking University,The University of Hong Kong, in United States of America, in May 2025. The organisation is categorised as industry,Academia,Academia,Industry,Academia,Academia.

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

It builds on SANA 1.6B, which is why it shares that model's general shape and size.

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 Efficient-Large-Model organisation on Hugging Face.

Reading the throughput figures

Across every card that can run it, the middle of the range is about 28.2 tokens per second, and 771 of them clear the ten tokens per second that roughly matches reading speed.

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.

Step by step

How to choose a GPU for SANA 1.5 4.8B

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

Every card here has been checked against SANA 1.5 4.8B — around 3.3 GB at IQ4_XS. Capacity is the gate — a card either holds it or it does not.
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 SANA 1.5 4.8B.
03

Decide how much compression you will accept

Compression is what makes SANA 1.5 4.8B fit smaller cards, at some cost in accuracy — IQ4_XS on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for SANA 1.5 4.8B follows memory bandwidth, not core counts, which is why the B200 tops it at 706 tok/s.
05

Look at the headroom, not just the fit

Tight means SANA 1.5 4.8B 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 SANA 1.5 4.8B is settled.

Answers

SANA 1.5 4.8B — common questions

Can I run SANA 1.5 4.8B if it does not fit in my GPU?

It can be split between the card and system memory, but SANA 1.5 4.8B generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run SANA 1.5 4.8B faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run SANA 1.5 4.8B alone, the case for pairing is weak.

Why does the quantisation differ between cards for SANA 1.5 4.8B?

Each card is shown running the least-compressed copy it can hold, and SANA 1.5 4.8B appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these SANA 1.5 4.8B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 424–1,129 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 SANA 1.5 4.8B?

The smallest card in our catalogue that holds SANA 1.5 4.8B is the Tesla C1080, with 4 GB of memory. It runs the model at IQ4_XS using about 3.3 GB, and produces roughly 18.9 tokens per second. 818 cards in total can run it.

How fast is SANA 1.5 4.8B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 706 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 771 of the cards that can run SANA 1.5 4.8B clear that.

How much VRAM does SANA 1.5 4.8B need?

About 3.3 GB at IQ4_XS 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 SANA 1.5 4.8B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 5.8 GB and generating roughly 131 tokens per second — a comfortable fit.

Can I run SANA 1.5 4.8B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 5.8 GB and generating roughly 80.5 tokens per second — a comfortable fit.

Can I run SANA 1.5 4.8B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 5.8 GB and generating roughly 99.7 tokens per second — a comfortable fit.

Can I run SANA 1.5 4.8B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 5.8 GB and generating roughly 118 tokens per second — a comfortable fit.

Is SANA 1.5 4.8B open source?

Its weights are published, so SANA 1.5 4.8B 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 SANA 1.5 4.8B have?

SANA 1.5 4.8B has 4.8B parameters. 4.8B Our final model (SANA-4.8B) scales to 60 layers while maintaining the same channel dimension (2240 per layer) and FFN dimension (5600) as SANA-1.6B. The architecture, training data, and other hyperparameters remain consistent with SANA-1.6B. 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 SANA 1.5 4.8B?

SANA 1.5 4.8B was published by NVIDIA,Massachusetts Institute of Technology (MIT),Tsinghua University,Playground,Peking University,The University of Hong Kong, based in United States of America, categorised as industry,Academia,Academia,Industry,Academia,Academia.

When was SANA 1.5 4.8B released?

SANA 1.5 4.8B was published in May 2025.

What is SANA 1.5 4.8B used for?

SANA 1.5 4.8B works in Image generation, and is recorded as handling text-to-image, Image generation. 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 SANA 1.5 4.8B?

Its weights are published under the Efficient-Large-Model 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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