Prithvi WxC TPS calculator

Open weights IBM Research,University of Alabama,Stanford University,Colorado State University,Oak Ridge National Laboratory,NASA 2.3B parameters September 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 · Q8_0 · 16.0 tok/s

Fastest card

B200

1,473 tok/s · 180 GB

Which GPUs can run Prithvi WxC?

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
1,473 tok/s 884–2,357 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.2 GB	Q8_0	Comfortable
1,473 tok/s 884–2,357 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.2 GB	Q8_0	Comfortable
1,176 tok/s 706–1,882 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.2 GB	Q8_0	Comfortable
1,176 tok/s 706–1,882 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.2 GB	Q8_0	Comfortable
941 tok/s 564–1,505 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.2 GB	Q8_0	Comfortable
900 tok/s 540–1,441 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.2 GB	Q8_0	Comfortable
900 tok/s 540–1,441 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.2 GB	Q8_0	Comfortable
862 tok/s 517–1,379 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.2 GB	Q8_0	Comfortable
765 tok/s 459–1,224 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.2 GB	Q8_0	Comfortable
765 tok/s 459–1,224 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.2 GB	Q8_0	Comfortable
765 tok/s 459–1,224 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.2 GB	Q8_0	Comfortable
726 tok/s 435–1,161 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.2 GB	Q8_0	Comfortable
619 tok/s 371–990 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.2 GB	Q8_0	Comfortable
619 tok/s 371–990 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.2 GB	Q8_0	Comfortable
619 tok/s 371–990 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.2 GB	Q8_0	Comfortable
619 tok/s 371–990 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.2 GB	Q8_0	Comfortable
619 tok/s 371–990 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.2 GB	Q8_0	Comfortable
471 tok/s 283–754 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.2 GB	Q8_0	Comfortable
471 tok/s 283–754 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.2 GB	Q8_0	Comfortable
393 tok/s 236–628 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.2 GB	Q8_0	Comfortable
384 tok/s 231–615 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.2 GB	Q8_0	Comfortable
376 tok/s 225–601 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.2 GB	Q8_0	Comfortable
376 tok/s 225–601 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.2 GB	Q8_0	Comfortable
376 tok/s 225–601 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.2 GB	Q8_0	Comfortable
376 tok/s 225–601 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.2 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: IBM Research,University of Alabama,Stanford University,Colorado State University,Oak Ridge National Laboratory,NASA
Organisation type: Industry,Academia,Academia,Academia,Government,Government
Country: United States of America
Published: 20 September 2024
Authors: Johannes Schmude, Sujit Roy, Will Trojak, Johannes Jakubik, Daniel Salles Civitarese, Shraddha Singh, Julian Kuehnert, Kumar Ankur, Aman Gupta, Christopher E Phillips, Romeo Kienzler, Daniela Szwarcman, Vishal Gaur, Rajat Shinde, Rohit Lal, Arlindo Da Silva, Jorge Luis Guevara Diaz, Anne Jones, Simon Pfreundschuh, Amy Lin, Aditi Sheshadri, Udaysankar Nair, Valentine Anantharaj, Hendrik Hamann, Cam…

What it does

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

Domain: Earth science
Task: Weather forecasting

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: 2.3B
Training data: 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: 7.2 × 10¹⁹ 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 A100
Chips used: 64
Power draw: 50.4 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 (unrestricted)
Training code: Unreleased
Hugging Face: ibm-nasa-geospatial

How it is classified

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

Record confidence: Confident
Citations: 38

Sources

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

Reference: Prithvi WxC: Foundation Model for Weather and Climate
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Prithvi WxC

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 Prithvi WxC

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

3.2 GB

Fastest

1,473 tok/s

Prithvi WxC is small enough at 2.3B 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, Q8_0 compression, roughly 16.0 tokens per second.

At the other end, a B200 generates roughly 1,473 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

What this model is

Prithvi WxC was published by IBM Research,University of Alabama,Stanford University,Colorado State University,Oak Ridge National Laboratory,NASA, in United States of America, in September 2024. The organisation is categorised as industry,Academia,Academia,Academia,Government,Government.

It works in Earth science, and is recorded as doing weather forecasting.

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 ibm-nasa-geospatial organisation on Hugging Face.

What decides the speed

Half the cards that hold it manage more than 41.4 tokens per second, and 785 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.

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.

How it was trained

The training run consumed about 7.2 × 10¹⁹ FLOP, on NVIDIA A100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Step by step

How to choose a GPU for Prithvi WxC

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 Prithvi WxC actually needs — around 3.2 GB at Q8_0. 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 Prithvi WxC.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of Prithvi WxC — Q8_0 on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for Prithvi WxC. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 1,473 tok/s.
05

Check the fit verdict before buying

Tight means Prithvi WxC 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

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 Prithvi WxC alone — a card is usually bought for more than one model.

Answers

Prithvi WxC — common questions

Who created Prithvi WxC?

Prithvi WxC was published by IBM Research,University of Alabama,Stanford University,Colorado State University,Oak Ridge National Laboratory,NASA, based in United States of America, categorised as industry,Academia,Academia,Academia,Government,Government.

When was Prithvi WxC released?

Prithvi WxC was published in September 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 Prithvi WxC used for?

Prithvi WxC works in Earth science, and is recorded as handling weather forecasting. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Prithvi WxC?

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

How much compute was used to train Prithvi WxC?

Around 7.2 × 10¹⁹ FLOP, on NVIDIA A100. 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 Prithvi WxC 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 Prithvi WxC is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run Prithvi WxC faster?

Two cards buy memory rather than speed. That matters for Prithvi WxC only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for Prithvi WxC?

Each card is shown running the least-compressed copy it can hold, and Prithvi WxC appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Prithvi WxC 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 884–2,357 tok/s on the B200 rather than a single number.

What GPU do I need to run Prithvi WxC?

The smallest card in our catalogue that holds Prithvi WxC is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 3.2 GB, and produces roughly 16.0 tokens per second. 818 cards in total can run it.

How fast is Prithvi WxC on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,473 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 785 of the cards that can run Prithvi WxC clear that.

How much VRAM does Prithvi WxC need?

About 3.2 GB at Q8_0 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 Prithvi WxC on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.2 GB and generating roughly 274 tokens per second — a comfortable fit.

Can I run Prithvi WxC on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.2 GB and generating roughly 168 tokens per second — a comfortable fit.

Can I run Prithvi WxC on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.2 GB and generating roughly 208 tokens per second — a comfortable fit.

Can I run Prithvi WxC on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.2 GB and generating roughly 247 tokens per second — a comfortable fit.

Is Prithvi WxC open source?

Its weights are published, so Prithvi WxC 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 Prithvi WxC have?

Prithvi WxC has 2.3B parameters. a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). 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.

Source

Original publication

Record last updated 25 May 2026

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