Mochi 1 TPS calculator

Open weights Genmo 10B 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

509 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 5110P

8 GB · Q4_K_M · 20.3 tok/s

Fastest card

B200

339 tok/s · 180 GB

Which GPUs can run Mochi 1?

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

509 cards match

Calculating

					Needs	Quantisation	Fit
339 tok/s 203–542 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	11.4 GB	Q8_0	Comfortable
339 tok/s 203–542 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	11.4 GB	Q8_0	Comfortable
271 tok/s 162–433 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	11.4 GB	Q8_0	Comfortable
271 tok/s 162–433 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	11.4 GB	Q8_0	Comfortable
216 tok/s 130–346 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	11.4 GB	Q8_0	Comfortable
207 tok/s 124–331 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	11.4 GB	Q8_0	Comfortable
207 tok/s 124–331 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	11.4 GB	Q8_0	Comfortable
198 tok/s 119–317 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	11.4 GB	Q8_0	Comfortable
176 tok/s 106–281 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	11.4 GB	Q8_0	Comfortable
176 tok/s 106–281 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	11.4 GB	Q8_0	Comfortable
176 tok/s 106–281 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	11.4 GB	Q8_0	Comfortable
167 tok/s 100–267 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
146 tok/s 87–233 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.7 GB	Q4_K_M	Tight
142 tok/s 85–228 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
118 tok/s 71–189 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	7.9 GB	Q5_K_M	Tight
108 tok/s 65–173 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	11.4 GB	Q8_0	Comfortable
108 tok/s 65–173 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	11.4 GB	Q8_0	Comfortable
90.3 tok/s 54–144 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	11.4 GB	Q8_0	Comfortable
88.4 tok/s 53–141 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	11.4 GB	Q8_0	Comfortable
86.4 tok/s 52–138 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	11.4 GB	Q8_0	Comfortable
86.4 tok/s 52–138 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	11.4 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: Genmo
Organisation type: Industry
Country: United States of America
Published: 22 October 2024

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: 10B
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 (unrestricted)
Training code: Unreleased

How it is classified

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

Record confidence: Likely

Sources

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

Reference: Mochi 1: A new SOTA in open-source video generation models
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Mochi 1

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 Mochi 1

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

Xeon Phi 5110P

Memory needed

6.7 GB

Fastest

339 tok/s

Mochi 1 is small enough at 10B parameters that hardware is rarely the obstacle — 509 of the cards we track can run it, including cards several years old.

The entry point is the Xeon Phi 5110P: 8 GB of memory, Q4_K_M compression, roughly 20.3 tokens per second.

Top of the range is the B200, at roughly 339 tokens per second thanks to 8,000 GB/s of bandwidth.

What this model is

Mochi 1 was published by Genmo, in United States of America, in October 2024. It comes out of industry.

It works in Video, and is recorded as doing video generation, Text-to-video.

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.

What decides the speed

The median result is around 22.0 tokens per second; 469 cards produce text faster than most people read it.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

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 Mochi 1

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 Mochi 1 — around 6.7 GB at Q4_K_M. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Mochi 1 can slip off a card that handles short questions easily.
03

Set a quality floor

Each card runs the least-compressed copy it can hold — Q4_K_M on the smallest card that fits. Setting a floor drops the cards that only manage Mochi 1 by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

The speed ordering for Mochi 1 is effectively an ordering by memory bandwidth, which is why the B200 tops it at 339 tok/s.
05

Read the fit column last

Tight means Mochi 1 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

Open the card you have settled on

Following a card through to its own page shows every other model it can hold, which is the question that follows once Mochi 1 is settled.

Answers

Mochi 1 — common questions

What GPU do I need to run Mochi 1?

The smallest card in our catalogue that holds Mochi 1 is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at Q4_K_M using about 6.7 GB, and produces roughly 20.3 tokens per second. 509 cards in total can run it.

How fast is Mochi 1 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 339 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 469 of the cards that can run Mochi 1 clear that.

How much VRAM does Mochi 1 need?

About 6.7 GB at Q4_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 Mochi 1 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q4_K_M, using about 6.7 GB and generating roughly 146 tokens per second — a tight fit.

Can I run Mochi 1 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q6_K, using about 9.1 GB and generating roughly 56.2 tokens per second — a tight fit.

Can I run Mochi 1 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 11.4 GB and generating roughly 47.9 tokens per second — a comfortable fit.

Can I run Mochi 1 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 11.4 GB and generating roughly 56.8 tokens per second — a comfortable fit.

Is Mochi 1 open source?

Its weights are published, so Mochi 1 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 Mochi 1 have?

Mochi 1 has 10B parameters. featuring a 10 billion parameter diffusion model built on our novel Asymmetric Diffusion Transformer (AsymmDiT) architecture. 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 Mochi 1?

Mochi 1 was published by Genmo, based in United States of America, categorised as industry.

When was Mochi 1 released?

Mochi 1 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.

What is Mochi 1 used for?

Mochi 1 works in Video, and is recorded as handling video generation, Text-to-video. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Mochi 1?

The weights for Mochi 1 are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run Mochi 1 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 1.3 GB. Our figures for Mochi 1 assume it is fully resident.

Would two GPUs run Mochi 1 faster?

Capacity adds across cards; throughput does not. Since 509 of the cards we track already hold Mochi 1 on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Mochi 1?

A larger card holds a more accurate copy. Across the cards that run Mochi 1, 4 compression levels are used; the floor control above pins it to one.

How accurate are these Mochi 1 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 203–542 tok/s on the B200 rather than a single number.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

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