MultiVerse 70B 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
A100 PCIe 40 GB
40 GB · Q3_K_M · 24.8 tok/s
Fastest card
B200
47.1 tok/s · 180 GB
Which GPUs can run MultiVerse 70B?
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.
61 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
47.1
tok/s
28–75 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 77.8 GB | Q8_0 | Comfortable |
|
47.1
tok/s
28–75 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 77.8 GB | Q8_0 | Comfortable |
|
37.6
tok/s
23–60 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 77.8 GB | Q8_0 | Comfortable |
|
37.6
tok/s
23–60 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 77.8 GB | Q8_0 | Comfortable |
|
30.1
tok/s
18–48 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 77.8 GB | Q8_0 | Comfortable |
|
28.8
tok/s
17–46 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 77.8 GB | Q8_0 | Comfortable |
|
28.8
tok/s
17–46 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 77.8 GB | Q8_0 | Comfortable |
|
28.7
tok/s
17–46 · low confidence |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 61.0 GB | Q6_K | Tight |
|
28.7
tok/s
17–46 · low confidence |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 61.0 GB | Q6_K | Tight |
|
27.5
tok/s
17–44 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 77.8 GB | Q8_0 | Comfortable |
|
27.0
tok/s
16–43 · low confidence |
GRID A100B NVIDIA | 48 GB | 1,870 GB/s | May 2020 | 40.1 GB | IQ4_XS | Tight |
|
24.8
tok/s
15–40 · low confidence |
A100 PCIe 40 GB NVIDIA | 40 GB | 1,560 GB/s | Jun 2020 | 35.9 GB | Q3_K_M | Tight |
|
24.8
tok/s
15–40 · low confidence |
A100 SXM4 40 GB NVIDIA | 40 GB | 1,560 GB/s | May 2020 | 35.9 GB | Q3_K_M | Tight |
|
24.8
tok/s
15–40 · low confidence |
A800 PCIe 40 GB NVIDIA | 40 GB | 1,560 GB/s | Nov 2022 | 35.9 GB | Q3_K_M | Tight |
|
24.4
tok/s
15–39 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 77.8 GB | Q8_0 | Comfortable |
|
24.4
tok/s
15–39 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 77.8 GB | Q8_0 | Comfortable |
|
24.4
tok/s
15–39 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 77.8 GB | Q8_0 | Comfortable |
|
23.2
tok/s
14–37 · low confidence |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 77.8 GB | Q8_0 | Tight |
|
21.2
tok/s
13–34 · low confidence |
H100 SXM5 64 GB NVIDIA | 64 GB | 2,020 GB/s | Mar 2023 | 52.6 GB | Q5_K_M | Tight |
|
19.8
tok/s
12–32 · low confidence |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 77.8 GB | Q8_0 | Tight |
|
19.8
tok/s
12–32 · low confidence |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 77.8 GB | Q8_0 | Tight |
|
19.8
tok/s
12–32 · low confidence |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 77.8 GB | Q8_0 | Tight |
|
19.4
tok/s
12–31 · low confidence |
RTX PRO 5000 Blackwell NVIDIA | 48 GB | 1,340 GB/s | Mar 2025 | 40.1 GB | IQ4_XS | Tight |
|
17.4
tok/s
10–28 · low confidence |
A100 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Nov 2020 | 61.0 GB | Q6_K | Tight |
|
17.4
tok/s
10–28 · low confidence |
A100X NVIDIA | 80 GB | 2,040 GB/s | Jun 2021 | 61.0 GB | Q6_K | 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
- MTS
- Organisation type
- Industry
- Country
- Russia
- Published
- 25 March 2024
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering
- Base model
- Qwen-72B
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
- 72B
- 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 (restricted use)
- Training code
- Unreleased
- Hugging Face
- MTSAIR
Qwen license (restriction on >100m monthly users) https://huggingface.co/MTSAIR/MultiVerse_70B
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
- MultiVerse 70B
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run MultiVerse 70B
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 47.1 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 47.1 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 37.6 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 37.6 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 30.1 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 28.8 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 28.8 tok/s
- 08 H800 SXM5 80 GB · 3,360 GB/s · Q6_K 28.7 tok/s
- 09 H100 SXM5 80 GB 80 GB · 3,360 GB/s · Q6_K 28.7 tok/s
- 10 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 27.5 tok/s
The smallest GPUs that still run MultiVerse 70B
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 A800 PCIe 40 GB 40 GB · needs 35.9 GB · Q3_K_M · tight 24.8 tok/s
- 02 A100 PCIe 40 GB 40 GB · needs 35.9 GB · Q3_K_M · tight 24.8 tok/s
- 03 A100 SXM4 40 GB 40 GB · needs 35.9 GB · Q3_K_M · tight 24.8 tok/s
- 04 Radeon PRO W7900D 48 GB · needs 40.1 GB · IQ4_XS · tight 9.7 tok/s
- 05 RTX PRO 5000 Blackwell 48 GB · needs 40.1 GB · IQ4_XS · tight 19.4 tok/s
- 06 RTX 5880 Ada Generation 48 GB · needs 40.1 GB · IQ4_XS · tight 12.5 tok/s
- 07 L20 48 GB · needs 40.1 GB · IQ4_XS · tight 12.5 tok/s
- 08 Radeon PRO W7800 48 GB 48 GB · needs 40.1 GB · IQ4_XS · tight 9.7 tok/s
- 09 Radeon PRO W7900 48 GB · needs 40.1 GB · IQ4_XS · tight 9.7 tok/s
- 10 Data Center GPU Max 1100 48 GB · needs 40.1 GB · IQ4_XS · tight 11.6 tok/s
What the numbers mean
What it takes to run this model
Minimum card
A100 PCIe 40 GB
Memory needed
35.9 GB
Fastest
47.1 tok/s
MultiVerse 70B sits at 72B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 61 of the cards we track can hold it.
The smallest card that holds it is the A100 PCIe 40 GB with 40 GB, running it at Q3_K_M and producing around 24.8 tokens per second.
A B200 is the fastest we calculate for it: about 47.1 tokens per second, from 8,000 GB/s of memory bandwidth.
What this model is
MultiVerse 70B was published by MTS, in Russia, in March 2024. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling/generation, Question answering.
It builds on Qwen-72B, which is why it shares that model's general shape and size.
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 MTSAIR organisation on Hugging Face.
What decides the speed
Half the cards that hold it manage more than 16.6 tokens per second, and 51 exceed reading speed outright.
Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.
Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.
Step by step
How to choose a GPU for MultiVerse 70B
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Check what it needs before anything else
The table lists every card that can hold MultiVerse 70B — around 35.9 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
-
02
Set the context length you will work at
The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context MultiVerse 70B can slip off a card that handles short questions easily.
-
03
Decide how much compression you will accept
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 MultiVerse 70B by squeezing it further than you would want.
-
04
Compare tokens per second, not specifications
Sort by speed to see how cards rank for MultiVerse 70B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 47.1 tok/s.
-
05
Read the fit column last
Tight means MultiVerse 70B 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
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond MultiVerse 70B.
Answers
MultiVerse 70B — common questions
Can I run MultiVerse 70B if it does not fit in my GPU?
It can be split between the card and system memory, but MultiVerse 70B generates painfully slowly that way — the nearest miss we calculate is short by 15.5 GB. Nothing on this page assumes offloading.
Would two GPUs run MultiVerse 70B faster?
A second card roughly doubles the memory available but not the generation rate. With 61 cards already able to run MultiVerse 70B alone, the case for pairing is weak.
Why does the quantisation differ between cards for MultiVerse 70B?
A larger card holds a more accurate copy. Across the cards that run MultiVerse 70B, 5 compression levels are used; the floor control above pins it to one.
How accurate are these MultiVerse 70B 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 28–75 tok/s on the B200 rather than a single number.
What GPU do I need to run MultiVerse 70B?
The smallest card in our catalogue that holds MultiVerse 70B is the A100 PCIe 40 GB, with 40 GB of memory. It runs the model at Q3_K_M using about 35.9 GB, and produces roughly 24.8 tokens per second. 61 cards in total can run it.
How fast is MultiVerse 70B on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 47.1 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 51 of the cards that can run MultiVerse 70B clear that.
How much VRAM does MultiVerse 70B need?
About 35.9 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.
Is MultiVerse 70B open source?
Its weights are published, so MultiVerse 70B 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 MultiVerse 70B have?
MultiVerse 70B has 72B parameters. 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 MultiVerse 70B?
MultiVerse 70B was published by MTS, based in Russia, categorised as industry.
When was MultiVerse 70B released?
MultiVerse 70B was published in March 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 MultiVerse 70B used for?
MultiVerse 70B works in Language, and is recorded as handling language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download MultiVerse 70B?
Its weights are published under the MTSAIR organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
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.