Pyramid Flow TPS calculator

Open weights Peking University,Kuaishou Technology,Beijing University of Posts and Telecommunications 2B parameters October 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 · 18.4 tok/s

Fastest card

B200

1,694 tok/s · 180 GB

Which GPUs can run Pyramid Flow?

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
1,694 tok/s 1,016–2,711 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.8 GB	Q8_0	Comfortable
1,694 tok/s 1,016–2,711 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.8 GB	Q8_0	Comfortable
1,353 tok/s 812–2,164 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.8 GB	Q8_0	Comfortable
1,353 tok/s 812–2,164 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.8 GB	Q8_0	Comfortable
1,082 tok/s 649–1,731 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.8 GB	Q8_0	Comfortable
1,036 tok/s 621–1,657 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.8 GB	Q8_0	Comfortable
1,036 tok/s 621–1,657 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.8 GB	Q8_0	Comfortable
991 tok/s 595–1,586 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.8 GB	Q8_0	Comfortable
880 tok/s 528–1,407 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.8 GB	Q8_0	Comfortable
880 tok/s 528–1,407 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.8 GB	Q8_0	Comfortable
880 tok/s 528–1,407 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.8 GB	Q8_0	Comfortable
834 tok/s 501–1,335 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.8 GB	Q8_0	Comfortable
712 tok/s 427–1,138 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.8 GB	Q8_0	Comfortable
712 tok/s 427–1,138 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.8 GB	Q8_0	Comfortable
712 tok/s 427–1,138 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.8 GB	Q8_0	Comfortable
712 tok/s 427–1,138 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.8 GB	Q8_0	Comfortable
712 tok/s 427–1,138 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.8 GB	Q8_0	Comfortable
542 tok/s 325–867 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.8 GB	Q8_0	Comfortable
542 tok/s 325–867 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.8 GB	Q8_0	Comfortable
451 tok/s 271–722 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.8 GB	Q8_0	Comfortable
442 tok/s 265–707 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.8 GB	Q8_0	Comfortable
432 tok/s 259–691 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.8 GB	Q8_0	Comfortable
432 tok/s 259–691 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.8 GB	Q8_0	Comfortable
432 tok/s 259–691 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.8 GB	Q8_0	Comfortable
432 tok/s 259–691 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.8 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: Peking University,Kuaishou Technology,Beijing University of Posts and Telecommunications
Organisation type: Academia,Industry,Academia
Country: China
Published: 8 October 2024
Authors: Yang Jin, Zhicheng Sun, Ningyuan Li, Kun Xu, Kun Xu, Hao Jiang, Nan Zhuang, Quzhe Huang, Yang Song, Yadong Mu, Zhouchen Lin

What it does

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

Domain: Video
Task: Video generation, Text-to-video

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: 2B
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: 7.7 × 10²¹ FLOP

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: 128
Chip-hours: 20,700
Wall-clock time: 162 hours
Power draw: 100.8 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)

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: 294

Sources

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

Reference: Pyramidal Flow Matching for Efficient Video Generative Modeling
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Pyramid Flow

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 Pyramid Flow

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

The hardware side

Minimum card

Tesla C1080

Memory needed

2.8 GB

Fastest

1,694 tok/s

Pyramid Flow reaches a parameter count of 2B. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 818.

At the low end it is handled by Tesla C1080, with a memory capacity of 4 GB, running it at a compression of Q8_0 and producing around 18.4 tokens per second.

Top of the range is B200, generating roughly 1,694 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

Pyramid Flow was published by Peking University,Kuaishou Technology,Beijing University of Posts and Telecommunications, in the country recorded as China, during October 2024. The category the publisher falls under is academia,Industry,Academia.

It works in the domain of Video, and is recorded as performing the task of video generation, Text-to-video.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

What decides the speed

Across every card that can run it, the middle of the range sits at 47.6 tokens per second. Exceeding reading speed outright: 789 of them.

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

Training it took a computation budget of roughly 7.7 × 10²¹ FLOP, on hardware recorded as NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.

Step by step

How to choose a GPU for Pyramid Flow

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

Every card here has been checked against Pyramid Flow, needing around 2.8 GB at a compression of Q8_0. Capacity is the gate — a card either holds it or it does not.
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 a card that seemed fine stops fitting Pyramid Flow.
03

Set a quality floor

Each card runs the least-compressed copy it can hold, reaching a compression of Q8_0 on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Sort by speed

Sort by speed to see how cards rank for Pyramid Flow. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 1,694 tok/s.
05

Check the fit verdict before buying

The fit column separates cards that just manage it from those with room to spare, in the case of Pyramid Flow. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
06

See what else that card runs

Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond Pyramid Flow.

Answers

Pyramid Flow — common questions

Pyramid Flow— where can I download it?

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

Pyramid Flow— how much compute was used to train it?

Training consumed around 7.7 × 10²¹ FLOP, on hardware recorded as 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.

Pyramid Flow— can I run it if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. Every figure here assumes the whole model is resident on the card.

Pyramid Flow— would two GPUs run it faster?

Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 818. So a second card is rarely the answer here.

Pyramid Flow— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Pyramid Flow— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 1,016–2,711 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Pyramid Flow— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Tesla C1080, with a memory capacity of 4 GB. It runs the model at a compression of Q8_0 using about 2.8 GB, and produces roughly 18.4 tokens per second. The number of cards able to run it in total: 818.

Pyramid Flow— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 1,694 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 789.

Pyramid Flow— how much VRAM does it need?

It needs about 2.8 GB at a compression of Q8_0, 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.

Pyramid Flow— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q8_0, using about 2.8 GB and generating roughly 316 tokens per second. The fit is comfortable.

Pyramid Flow— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q8_0, using about 2.8 GB and generating roughly 193 tokens per second. The fit is comfortable.

Pyramid Flow— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 2.8 GB and generating roughly 239 tokens per second. The fit is comfortable.

Pyramid Flow— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 2.8 GB and generating roughly 284 tokens per second. The fit is comfortable.

Pyramid Flow— is it open source?

Its weights are published, so it 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.

Pyramid Flow— how many parameters does it have?

It has a parameter count of 2B. "We utilize the prevailing MM-DiT architecture from SD3 Medium (Esser et al., 2024) as the base model, with 2B parameters in total" (https://arxiv.org/pdf/2410.05954, page 7). 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.

Pyramid Flow— who created it?

It was published by Peking University,Kuaishou Technology,Beijing University of Posts and Telecommunications, based in China, an organisation categorised as academia,Industry,Academia.

Pyramid Flow— when was it released?

It was published in October 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.

Pyramid Flow— what is it used for?

It works in the domain of Video, and is recorded as handling the task of video generation, Text-to-video. These are the areas it was designed around; they describe intent rather than a hard boundary.

Source

Original publication

Record last updated 25 May 2026

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