InternVL1.5 TPS calculator

Open weights Shanghai AI Lab,SenseTime,Tsinghua University,Nanjing University,Fudan University,Chinese University of Hong Kong (CUHK) 25.5B parameters April 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

241 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 7120P

16 GB · Q3_K_M · 10.3 tok/s

Fastest card

B200

133 tok/s · 180 GB

Which GPUs can run InternVL1.5?

Set the inputs, read the answer

A longer conversation needs more memory, which can push this model off smaller cards.

Hides cards that would only fit the model by compressing it below this point.

241 cards match

Calculating
Needs Quantisation Fit
133 tok/s

80–213 · low confidence

B200 NVIDIA 180 GB 8,000 GB/s Jan 2024 28.0 GB Q8_0 Comfortable
133 tok/s

80–213 · low confidence

B300 NVIDIA 288 GB 8,000 GB/s Sep 2025 28.0 GB Q8_0 Comfortable
106 tok/s

64–170 · low confidence

Radeon Instinct MI350X AMD 288 GB 8,190 GB/s Jan 2025 28.0 GB Q8_0 Comfortable
106 tok/s

64–170 · low confidence

Radeon Instinct MI355X AMD 288 GB 8,190 GB/s Jan 2025 28.0 GB Q8_0 Comfortable
84.9 tok/s

51–136 · low confidence

Radeon Instinct MI300 AMD 128 GB 6,550 GB/s Jan 2023 28.0 GB Q8_0 Comfortable
81.2 tok/s

49–130 · low confidence

H200 NVL NVIDIA 141 GB 4,890 GB/s Nov 2024 28.0 GB Q8_0 Comfortable
81.2 tok/s

49–130 · low confidence

H200 SXM 141 GB NVIDIA 141 GB 4,890 GB/s Nov 2024 28.0 GB Q8_0 Comfortable
77.7 tok/s

47–124 · low confidence

Radeon Instinct MI325X AMD 256 GB 6,000 GB/s Oct 2024 28.0 GB Q8_0 Comfortable
69.0 tok/s

41–110 · low confidence

Radeon Instinct MI300A AMD 128 GB 5,325 GB/s Dec 2023 28.0 GB Q8_0 Comfortable
69.0 tok/s

41–110 · low confidence

Radeon Instinct MI300X AMD 192 GB 5,325 GB/s Dec 2023 28.0 GB Q8_0 Comfortable
69.0 tok/s

41–110 · low confidence

Radeon Instinct MI308X AMD 192 GB 5,325 GB/s Dec 2023 28.0 GB Q8_0 Comfortable
65.4 tok/s

39–105 · low confidence

H100 NVL 94 GB NVIDIA 94 GB 3,940 GB/s Mar 2023 28.0 GB Q8_0 Comfortable
55.8 tok/s

33–89 · low confidence

H100 PCIe 96 GB NVIDIA 96 GB 3,360 GB/s Mar 2023 28.0 GB Q8_0 Comfortable
55.8 tok/s

33–89 · low confidence

H100 SXM5 80 GB NVIDIA 80 GB 3,360 GB/s Oct 2022 28.0 GB Q8_0 Comfortable
55.8 tok/s

33–89 · low confidence

H100 SXM5 94 GB NVIDIA 94 GB 3,360 GB/s Mar 2023 28.0 GB Q8_0 Comfortable
55.8 tok/s

33–89 · low confidence

H100 SXM5 96 GB NVIDIA 96 GB 3,360 GB/s Mar 2023 28.0 GB Q8_0 Comfortable
55.8 tok/s

33–89 · low confidence

H800 SXM5 NVIDIA 80 GB 3,360 GB/s Mar 2023 28.0 GB Q8_0 Comfortable
50.6 tok/s

30–81 · low confidence

Tesla V100 SXM2 16 GB NVIDIA 16 GB 1,130 GB/s Nov 2019 13.2 GB Q3_K_M Tight
43.0 tok/s

26–69 · low confidence

GeForce RTX 5080 NVIDIA 16 GB 960 GB/s Jan 2025 13.2 GB Q3_K_M Tight
42.5 tok/s

26–68 · low confidence

Radeon Instinct MI250 AMD 128 GB 3,280 GB/s Nov 2021 28.0 GB Q8_0 Comfortable
42.5 tok/s

26–68 · low confidence

Radeon Instinct MI250X AMD 128 GB 3,280 GB/s Nov 2021 28.0 GB Q8_0 Comfortable
40.2 tok/s

24–64 · low confidence

Tesla V100 DGXS 16 GB NVIDIA 16 GB 897 GB/s Mar 2018 13.2 GB Q3_K_M Tight
40.2 tok/s

24–64 · low confidence

Tesla V100 PCIe 16 GB NVIDIA 16 GB 897 GB/s Jun 2017 13.2 GB Q3_K_M Tight
40.2 tok/s

24–64 · low confidence

Tesla V100 SXM2 16 GB NVIDIA 16 GB 897 GB/s Jun 2017 13.2 GB Q3_K_M Tight
40.2 tok/s

24–64 · low confidence

GeForce RTX 5070 Ti NVIDIA 16 GB 896 GB/s Feb 2025 13.2 GB Q3_K_M Tight

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
Shanghai AI Lab,SenseTime,Tsinghua University,Nanjing University,Fudan University,Chinese University of Hong Kong (CUHK)
Organisation type
Academia,Industry,Academia,Academia,Academia,Academia
Country
China, Hong Kong
Published
29 April 2024
Authors
Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, Ji Ma, Jiaqi Wang, Xiaoyi Dong, Hang Yan, Hewei Guo, Conghui He, Botian Shi, Zhenjiang Jin, Chao Xu, Bin Wang, Xingjian Wei, Wei Li, Wenjian Zhang, Bo Zhang, Pinlong Cai, Licheng Wen, Xiangchao Yan, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, Wenhai Wa…

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, Image captioning, Object detection, Character recognition (OCR), Language modeling/generation, Translation
Base model
InternViT-6B,InternLM2-20B

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
25.5B

25.5B

Training data
tokens

"in our model, a 448×448 image is represented by 256 visual 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

MIT license https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5

Hugging Face
OpenGVLab

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
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Last updated
28 November 2025

The extremes

The ten fastest GPUs that run InternVL1.5

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.

  1. 01 B300 288 GB · 8,000 GB/s · Q8_0 133 tok/s
  2. 02 B200 180 GB · 8,000 GB/s · Q8_0 133 tok/s
  3. 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 106 tok/s
  4. 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 106 tok/s
  5. 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 84.9 tok/s
  6. 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 81.2 tok/s
  7. 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 81.2 tok/s
  8. 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 77.7 tok/s
  9. 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 69.0 tok/s
  10. 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 69.0 tok/s

The smallest GPUs that still run InternVL1.5

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

  1. 01 Ryzen AI Z2 Extreme GPU 16 GB · needs 13.2 GB · Q3_K_M · tight 9.0 tok/s
  2. 02 Radeon RX 7700 16 GB · needs 13.2 GB · Q3_K_M · tight 21.8 tok/s
  3. 03 Arc Pro B50 16 GB · needs 13.2 GB · Q3_K_M · tight 6.5 tok/s
  4. 04 RTX PRO 2000 Blackwell 16 GB · needs 13.2 GB · Q3_K_M · tight 12.9 tok/s
  5. 05 Ryzen Z2 Extreme GPU 16 GB · needs 13.2 GB · Q3_K_M · tight 4.5 tok/s
  6. 06 Radeon RX 9060 XT 16 GB 16 GB · needs 13.2 GB · Q3_K_M · tight 11.3 tok/s
  7. 07 GeForce RTX 5060 Ti 16 GB 16 GB · needs 13.2 GB · Q3_K_M · tight 20.1 tok/s
  8. 08 GeForce RTX 5080 Mobile 16 GB · needs 13.2 GB · Q3_K_M · tight 40.2 tok/s
  9. 09 Radeon RX 9070 16 GB · needs 13.2 GB · Q3_K_M · tight 22.5 tok/s
  10. 10 Radeon RX 9070 XT 16 GB · needs 13.2 GB · Q3_K_M · tight 22.5 tok/s

What the numbers mean

The hardware side

Minimum card

Xeon Phi 7120P

Memory needed

13.2 GB

Fastest

133 tok/s

With 25.5B parameters, InternVL1.5 lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.

At the low end, a Xeon Phi 7120P handles it — 16 GB, at Q3_K_M, for about 10.3 tokens per second.

Top of the range is the B200, at roughly 133 tokens per second thanks to 8,000 GB/s of bandwidth.

Background

InternVL1.5 was published by Shanghai AI Lab,SenseTime,Tsinghua University,Nanjing University,Fudan University,Chinese University of Hong Kong (CUHK), in China, in April 2024. academia,Industry,Academia,Academia,Academia,Academia is the category the publisher falls under.

It works in Multimodal, Vision, Language, and is recorded as doing visual question answering, Image captioning, Object detection, Character recognition (OCR), Language modeling/generation, Translation.

It is derived from InternViT-6B,InternLM2-20B rather than trained from scratch, which is the usual way a specialised model is produced.

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 OpenGVLab organisation on Hugging Face.

Reading the throughput figures

Across every card that can run it, the middle of the range is about 19.9 tokens per second, and 195 of them clear the ten tokens per second that roughly matches reading speed.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

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 InternVL1.5

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

  1. 01

    Check what it needs before anything else

    The table lists every card that can hold InternVL1.5 — around 13.2 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.

  2. 02

    Decide how long your conversations run

    The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context InternVL1.5 can slip off a card that handles short questions easily.

  3. 03

    Set a quality floor

    The quantisation column varies by card, because a bigger card holds a more accurate copy of InternVL1.5 — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.

  4. 04

    Rank by throughput rather than spec sheet

    Sort by speed to see how cards rank for InternVL1.5. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 133 tok/s.

  5. 05

    Read the fit column last

    Tight means InternVL1.5 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.

  6. 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 InternVL1.5 alone — a card is usually bought for more than one model.

Answers

InternVL1.5 — common questions

01

Can I run InternVL1.5 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q5_K_M, using about 19.1 GB and generating roughly 39.8 tokens per second — a tight fit.

02

Is InternVL1.5 open source?

Its weights are published, so InternVL1.5 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.

03

How many parameters does InternVL1.5 have?

InternVL1.5 has 25.5B parameters. 25.5B. 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.

04

Who created InternVL1.5?

InternVL1.5 was published by Shanghai AI Lab,SenseTime,Tsinghua University,Nanjing University,Fudan University,Chinese University of Hong Kong (CUHK), based in China, categorised as academia,Industry,Academia,Academia,Academia,Academia.

05

When was InternVL1.5 released?

InternVL1.5 was published in April 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.

06

What is InternVL1.5 used for?

InternVL1.5 works in Multimodal, Vision, Language, and is recorded as handling visual question answering, Image captioning, Object detection, Character recognition (OCR), Language modeling/generation, Translation. These are the areas it was designed around; they describe intent rather than a hard boundary.

07

Where can I download InternVL1.5?

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

08

Can I run InternVL1.5 if it does not fit in my GPU?

It can be split between the card and system memory, but InternVL1.5 generates painfully slowly that way — the nearest miss we calculate is short by 5.3 GB. Nothing on this page assumes offloading.

09

Would two GPUs run InternVL1.5 faster?

A second card roughly doubles the memory available but not the generation rate. With 241 cards already able to run InternVL1.5 alone, the case for pairing is weak.

10

Why does the quantisation differ between cards for InternVL1.5?

A larger card holds a more accurate copy. Across the cards that run InternVL1.5, 5 compression levels are used; the floor control above pins it to one.

11

How accurate are these InternVL1.5 speed estimates?

These are estimates with real error bars. The fastest result here, 80–213 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

12

What GPU do I need to run InternVL1.5?

The smallest card in our catalogue that holds InternVL1.5 is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at Q3_K_M using about 13.2 GB, and produces roughly 10.3 tokens per second. 241 cards in total can run it.

13

How fast is InternVL1.5 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 133 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 195 of the cards that can run InternVL1.5 clear that.

14

How much VRAM does InternVL1.5 need?

About 13.2 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.

15

Can I run InternVL1.5 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q3_K_M, using about 13.2 GB and generating roughly 50.6 tokens per second — a tight fit.

Source

Original publication

Record last updated 28 November 2025

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Looking at it from the other side?

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