InternVL2 26B TPS calculator
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 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 InternVL2 26B?
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
- Organisation type
- Academia
- Country
- China
- Published
- 4 July 2024
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision, Video
- Task
- Visual question answering, Language modeling/generation, Question answering, Video description, Character recognition (OCR)
- 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
- Training data
- tokens
25.5B
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
- OpenGVLab
MIT license https://huggingface.co/OpenGVLab/InternVL2-26B MIT license https://github.com/OpenGVLab/InternVL "Release training / evaluation code for InternVL2 series" is checked off
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
- InternVL2: Better than the Best—Expanding Performance Boundaries of Open-Source Multimodal Models with the Progressive Scaling Strategy
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run InternVL2 26B
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.
- 01 B300 288 GB · 8,000 GB/s · Q8_0 133 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 133 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 106 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 106 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 84.9 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 81.2 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 81.2 tok/s
- 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 77.7 tok/s
- 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 69.0 tok/s
- 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 69.0 tok/s
The smallest GPUs that still run InternVL2 26B
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 Ryzen AI Z2 Extreme GPU 16 GB · needs 13.2 GB · Q3_K_M · tight 9.0 tok/s
- 02 Radeon RX 7700 16 GB · needs 13.2 GB · Q3_K_M · tight 21.8 tok/s
- 03 Arc Pro B50 16 GB · needs 13.2 GB · Q3_K_M · tight 6.5 tok/s
- 04 RTX PRO 2000 Blackwell 16 GB · needs 13.2 GB · Q3_K_M · tight 12.9 tok/s
- 05 Ryzen Z2 Extreme GPU 16 GB · needs 13.2 GB · Q3_K_M · tight 4.5 tok/s
- 06 Radeon RX 9060 XT 16 GB 16 GB · needs 13.2 GB · Q3_K_M · tight 11.3 tok/s
- 07 GeForce RTX 5060 Ti 16 GB 16 GB · needs 13.2 GB · Q3_K_M · tight 20.1 tok/s
- 08 GeForce RTX 5080 Mobile 16 GB · needs 13.2 GB · Q3_K_M · tight 40.2 tok/s
- 09 Radeon RX 9070 16 GB · needs 13.2 GB · Q3_K_M · tight 22.5 tok/s
- 10 Radeon RX 9070 XT 16 GB · needs 13.2 GB · Q3_K_M · tight 22.5 tok/s
What the numbers mean
Hardware requirements in practice
Minimum card
Xeon Phi 7120P
Memory needed
13.2 GB
Fastest
133 tok/s
With 25.5B parameters, InternVL2 26B lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.
The entry point is the Xeon Phi 7120P: 16 GB of memory, Q3_K_M compression, roughly 10.3 tokens per second.
A B200 is the fastest we calculate for it: about 133 tokens per second, from 8,000 GB/s of memory bandwidth.
About this model
InternVL2 26B was published by Shanghai AI Lab, in China, in July 2024. academia is the category the publisher falls under.
It works in Multimodal, Language, Vision, Video, and is recorded as doing visual question answering, Language modeling/generation, Question answering, Video description, Character recognition (OCR).
Its starting point was InternViT-6B,InternLM2-20B — most models at this scale are adapted from an existing base rather than built from nothing.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all. It is published under the OpenGVLab organisation on Hugging Face.
How fast it runs, and why
The median result is around 19.9 tokens per second; 195 cards produce text faster than most people read it.
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.
Step by step
How to choose a GPU for InternVL2 26B
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
Look at what InternVL2 26B actually needs — around 13.2 GB at Q3_K_M. 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 InternVL2 26B.
-
03
Choose how far you will compress it
Each card runs the least-compressed copy it can hold — Q3_K_M on the smallest card that fits. Setting a floor drops the cards that only manage InternVL2 26B by squeezing it further than you would want.
-
04
Sort by speed
The speed ordering for InternVL2 26B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 133 tok/s.
-
05
Read the fit column last
The fit column separates cards that just manage InternVL2 26B 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 InternVL2 26B alone — a card is usually bought for more than one model.
Answers
InternVL2 26B — common questions
When was InternVL2 26B released?
InternVL2 26B was published in July 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 InternVL2 26B used for?
InternVL2 26B works in Multimodal, Language, Vision, Video, and is recorded as handling visual question answering, Language modeling/generation, Question answering, Video description, Character recognition (OCR). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download InternVL2 26B?
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.
Can I run InternVL2 26B if it does not fit in my GPU?
It can be split between the card and system memory, but InternVL2 26B generates painfully slowly that way — the nearest miss we calculate is short by 5.3 GB. Nothing on this page assumes offloading.
Would two GPUs run InternVL2 26B faster?
Capacity adds across cards; throughput does not. Since 241 of the cards we track already hold InternVL2 26B on their own, a second card is rarely the answer here.
Why does the quantisation differ between cards for InternVL2 26B?
Each card is shown running the least-compressed copy it can hold, and InternVL2 26B appears at 5 different compression levels across the cards that fit it. Bigger cards get the more accurate version.
How accurate are these InternVL2 26B 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.
What GPU do I need to run InternVL2 26B?
The smallest card in our catalogue that holds InternVL2 26B 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.
How fast is InternVL2 26B 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 InternVL2 26B clear that.
How much VRAM does InternVL2 26B 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.
Can I run InternVL2 26B 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.
Can I run InternVL2 26B 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.
Is InternVL2 26B open source?
Its weights are published, so InternVL2 26B 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 InternVL2 26B have?
InternVL2 26B 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.
Who created InternVL2 26B?
InternVL2 26B was published by Shanghai AI Lab, based in China, categorised as academia.
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.