OmniFusion-7B (InternViT-6B-448px V1-2) TPS calculator

Open weights AIRI Artificial Intelligence Research Institute,Sber,Skolkovo Institute of Science and Technology 12.5B parameters November 2023

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 · Q3_K_M · 19.0 tok/s

Fastest card

B200

270 tok/s · 180 GB

Which GPUs can run OmniFusion-7B (InternViT-6B-448px V1-2)?

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
270 tok/s 162–432 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	14.1 GB	Q8_0	Comfortable
270 tok/s 162–432 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	14.1 GB	Q8_0	Comfortable
216 tok/s 129–345 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	14.1 GB	Q8_0	Comfortable
216 tok/s 129–345 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	14.1 GB	Q8_0	Comfortable
173 tok/s 104–276 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	14.1 GB	Q8_0	Comfortable
165 tok/s 99–264 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	14.1 GB	Q8_0	Comfortable
165 tok/s 99–264 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	14.1 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	14.1 GB	Q8_0	Comfortable
140 tok/s 84–224 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	14.1 GB	Q8_0	Comfortable
140 tok/s 84–224 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	14.1 GB	Q8_0	Comfortable
140 tok/s 84–224 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	14.1 GB	Q8_0	Comfortable
136 tok/s 81–217 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.8 GB	Q3_K_M	Tight
133 tok/s 80–213 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	14.1 GB	Q8_0	Comfortable
122 tok/s 73–195 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.3 GB	Q4_K_M	Tight
113 tok/s 68–182 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	14.1 GB	Q8_0	Comfortable
113 tok/s 68–182 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	14.1 GB	Q8_0	Comfortable
113 tok/s 68–182 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	14.1 GB	Q8_0	Comfortable
113 tok/s 68–182 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	14.1 GB	Q8_0	Comfortable
113 tok/s 68–182 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	14.1 GB	Q8_0	Comfortable
86.4 tok/s 52–138 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	14.1 GB	Q8_0	Comfortable
86.4 tok/s 52–138 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	14.1 GB	Q8_0	Comfortable
72.0 tok/s 43–115 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	14.1 GB	Q8_0	Comfortable
70.5 tok/s 42–113 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	14.1 GB	Q8_0	Comfortable
70.0 tok/s 42–112 · low confidence	RTX A5000-8Q NVIDIA	8 GB	768 GB/s	Apr 2021	6.8 GB	Q3_K_M	Tight
68.9 tok/s 41–110 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	14.1 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: AIRI Artificial Intelligence Research Institute,Sber,Skolkovo Institute of Science and Technology
Organisation type: Research collective,Industry,Government,Academia
Country: Russia
Published: 22 November 2023
Authors: Elizaveta Goncharova, Anton Razzhigaev, Matvey Mikhalchuk, Maxim Kurkin, Irina Abdullaeva, Matvey Skripkin, Ivan Oseledets, Denis Dimitrov, Andrey Kuznetsov

What it does

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

Domain: Multimodal, Vision, Language
Task: Visual question answering, Language modeling/generation, Question answering
Base model: InternViT-6B,GigaChat-7B

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: 12.5B
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.

Fine-tuning compute: 4 × 10¹⁶ 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: 8
Power draw: 6.3 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: Open source
Hugging Face: AIRI-Institute

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: 7

Sources

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

Reference: OmniFusion Technical Report
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run OmniFusion-7B (InternViT-6B-448px V1-2)

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 OmniFusion-7B (InternViT-6B-448px V1-2)

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

6.8 GB

Fastest

270 tok/s

OmniFusion-7B (InternViT-6B-448px V1-2) is small enough at 12.5B 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, Q3_K_M compression, roughly 19.0 tokens per second.

A B200 is the fastest we calculate for it: about 270 tokens per second, from 8,000 GB/s of memory bandwidth.

What this model is

OmniFusion-7B (InternViT-6B-448px V1-2) was published by AIRI Artificial Intelligence Research Institute,Sber,Skolkovo Institute of Science and Technology, in Russia, in November 2023. The organisation is categorised as research collective,Industry,Government,Academia.

It works in Multimodal, Vision, Language, and is recorded as doing visual question answering, Language modeling/generation, Question answering.

Its starting point was InternViT-6B,GigaChat-7B — most models at this scale are adapted from an existing base rather than built from nothing.

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 AIRI-Institute organisation on Hugging Face.

What decides the speed

Across every card that can run it, the middle of the range is about 20.3 tokens per second, and 451 of them clear the ten tokens per second that roughly matches reading speed.

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 OmniFusion-7B (InternViT-6B-448px V1-2)

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 OmniFusion-7B (InternViT-6B-448px V1-2) — around 6.8 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for OmniFusion-7B (InternViT-6B-448px V1-2).
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy of OmniFusion-7B (InternViT-6B-448px V1-2) — Q3_K_M 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 OmniFusion-7B (InternViT-6B-448px V1-2). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 270 tok/s.
05

Read the fit column last

The fit column separates cards that just manage OmniFusion-7B (InternViT-6B-448px V1-2) from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for OmniFusion-7B (InternViT-6B-448px V1-2) alone — a card is usually bought for more than one model.

Answers

OmniFusion-7B (InternViT-6B-448px V1-2) — common questions

How accurate are these OmniFusion-7B (InternViT-6B-448px V1-2) 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 162–432 tok/s on the B200 rather than a single number.

What GPU do I need to run OmniFusion-7B (InternViT-6B-448px V1-2)?

The smallest card in our catalogue that holds OmniFusion-7B (InternViT-6B-448px V1-2) is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at Q3_K_M using about 6.8 GB, and produces roughly 19.0 tokens per second. 509 cards in total can run it.

How fast is OmniFusion-7B (InternViT-6B-448px V1-2) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 270 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 451 of the cards that can run OmniFusion-7B (InternViT-6B-448px V1-2) clear that.

How much VRAM does OmniFusion-7B (InternViT-6B-448px V1-2) need?

About 6.8 GB at Q3_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 OmniFusion-7B (InternViT-6B-448px V1-2) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q3_K_M, using about 6.8 GB and generating roughly 136 tokens per second — a tight fit.

Can I run OmniFusion-7B (InternViT-6B-448px V1-2) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q5_K_M, using about 9.7 GB and generating roughly 55.0 tokens per second — a tight fit.

Can I run OmniFusion-7B (InternViT-6B-448px V1-2) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 14.1 GB and generating roughly 38.2 tokens per second — a tight fit.

Can I run OmniFusion-7B (InternViT-6B-448px V1-2) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 14.1 GB and generating roughly 45.3 tokens per second — a comfortable fit.

Is OmniFusion-7B (InternViT-6B-448px V1-2) open source?

Its weights are published, so OmniFusion-7B (InternViT-6B-448px V1-2) 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 OmniFusion-7B (InternViT-6B-448px V1-2) have?

OmniFusion-7B (InternViT-6B-448px V1-2) has 12.5B parameters. The model uses InternViT-6B-448px V1-2 as the visual encoder, which undergoes pre-training, and GigaChat-7B as the LLM, which undergoes fine-tuning [1]. InternViT-6B-448px V1-2 has 5.54B parameters [2], and GigaChat-7B, being closed-source is assumed to have 7B parameters. 1. https://arxiv.org/pdf/2404.06212 2. https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2. 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.

Who created OmniFusion-7B (InternViT-6B-448px V1-2)?

OmniFusion-7B (InternViT-6B-448px V1-2) was published by AIRI Artificial Intelligence Research Institute,Sber,Skolkovo Institute of Science and Technology, based in Russia, categorised as research collective,Industry,Government,Academia.

When was OmniFusion-7B (InternViT-6B-448px V1-2) released?

OmniFusion-7B (InternViT-6B-448px V1-2) was published in November 2023. 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 OmniFusion-7B (InternViT-6B-448px V1-2) used for?

OmniFusion-7B (InternViT-6B-448px V1-2) works in Multimodal, Vision, Language, and is recorded as handling visual question answering, Language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download OmniFusion-7B (InternViT-6B-448px V1-2)?

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

Can I run OmniFusion-7B (InternViT-6B-448px V1-2) 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 OmniFusion-7B (InternViT-6B-448px V1-2) is rarely worth using — the nearest miss we calculate is short by 2.9 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run OmniFusion-7B (InternViT-6B-448px V1-2) faster?

Two cards buy memory rather than speed. That matters for OmniFusion-7B (InternViT-6B-448px V1-2) only if one card cannot hold it — 509 can, so a second adds little.

Why does the quantisation differ between cards for OmniFusion-7B (InternViT-6B-448px V1-2)?

A larger card holds a more accurate copy. Across the cards that run OmniFusion-7B (InternViT-6B-448px V1-2), 4 compression levels are used; the floor control above pins it to one.

Source

Original publication

Record last updated 25 May 2026

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