Molmo 72B 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 Molmo 72B?
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
- Allen Institute for AI,University of Washington
- Organisation type
- Research collective,Academia
- Country
- United States of America
- Published
- 25 September 2024
- Authors
- Matt Deitke, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, Jae Sung Park, Mohammadreza Salehi, Niklas Muennighoff, Kyle Lo, Luca Soldaini, Jiasen Lu, Taira Anderson, Erin Bransom, Kiana Ehsani, Huong Ngo, YenSung Chen, Ajay Patel, Mark Yatskar, Chris Callison-Burch, Andrew Head, Rose Hendrix, Favyen Bastani, Eli VanderBilt, Nathan Lambert, Yvonne Chou, Arnavi Chheda, Jenna Sparks, Sam S…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Vision, Multimodal
- Task
- Language modeling/generation, Visual question answering, Question answering
- Base model
- Qwen2-72B,CLIP (ViT L/14@336px)
- Numerical format
- BF16
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
"In total, we trained on 712k distinct images with ∼1.3M captions (including the augmentation)."
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.
- Training compute
- 1.3 × 10²² FLOP
- How it was established
- Hardware,Operation counting
from Table 8: h100 gpus [assuming NVIDIA H100 SXM5 80GB] pre-train 128 gpus 33.3 hours 4.2k gpu-hours fine-tune 256 gpus 32.4 hours 8.3k gpu-hours 989500000000000 FLOP/sec [bf16 precision] * 3600 s/hour * 0.3 [assumed utilization] * (4200 + 8300) = 1.33583×10^22 FLOP "We set a maximum sequence length of 2304 for both pre-training and fine-tuning" from Table 6: 6 FLOP/parameter/token * 2304 max tokens / sequence * 72B paramters [Pre-train]: ((128 sequences [batch size] * 22300 steps) + [Fine-t…
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 H100 SXM5 80GB
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
Apache 2 This checkpoint is a preview of the Molmo release. All artifacts used in creating Molmo (PixMo dataset, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility. https://huggingface.co/allenai/Molmo-72B-0924 https://github.com/allenai/molmo
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
- Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run Molmo 72B
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 Molmo 72B
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
Molmo 72B 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.
Top of the range is the B200, at roughly 47.1 tokens per second thanks to 8,000 GB/s of bandwidth.
Where it came from
Molmo 72B was published by Allen Institute for AI,University of Washington, in United States of America, in September 2024. It comes out of research collective,Academia.
It works in Language, Vision, Multimodal, and is recorded as doing language modeling/generation, Visual question answering, Question answering.
It builds on Qwen2-72B,CLIP (ViT L/14@336px), which is why it shares that model's general shape and size.
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.
Understanding the speeds
The median result is around 16.6 tokens per second; 51 cards produce text faster than most people read it.
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.
Training and provenance
Producing it required around 1.3 × 10²² FLOP of arithmetic, on NVIDIA H100 SXM5 80GB, which is a statement about the training budget rather than about inference.
Step by step
How to choose a GPU for Molmo 72B
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Read the memory figure first
The table lists every card that can hold Molmo 72B — 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
Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Molmo 72B.
-
03
Decide how much compression you will accept
The quantisation column varies by card, because a bigger card holds a more accurate copy of Molmo 72B — 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 Molmo 72B. 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
The fit column separates cards that just manage Molmo 72B from those with room to spare. Buy for the second if the context might grow.
-
06
Check the card from the other side
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Molmo 72B.
Answers
Molmo 72B — common questions
Would two GPUs run Molmo 72B faster?
Two cards buy memory rather than speed. That matters for Molmo 72B only if one card cannot hold it — 61 can, so a second adds little.
Why does the quantisation differ between cards for Molmo 72B?
Each card is shown running the least-compressed copy it can hold, and Molmo 72B appears at 5 different compression levels across the cards that fit it. Bigger cards get the more accurate version.
How accurate are these Molmo 72B speed estimates?
They are calculated from specifications rather than measured, and each carries a range — 28–75 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
What GPU do I need to run Molmo 72B?
The smallest card in our catalogue that holds Molmo 72B 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 Molmo 72B 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 Molmo 72B clear that.
How much VRAM does Molmo 72B 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 Molmo 72B open source?
Its weights are published, so Molmo 72B 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 Molmo 72B have?
Molmo 72B 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 Molmo 72B?
Molmo 72B was published by Allen Institute for AI,University of Washington, based in United States of America, categorised as research collective,Academia.
When was Molmo 72B released?
Molmo 72B was published in September 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 Molmo 72B used for?
Molmo 72B works in Language, Vision, Multimodal, and is recorded as handling language modeling/generation, Visual question answering, Question answering. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Where can I download Molmo 72B?
The weights for Molmo 72B are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
How much compute was used to train Molmo 72B?
Around 1.3 × 10²² FLOP, on NVIDIA H100 SXM5 80GB. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Can I run Molmo 72B if it does not fit in my GPU?
It can be split between the card and system memory, but Molmo 72B generates painfully slowly that way — the nearest miss we calculate is short by 15.5 GB. Nothing on this page assumes offloading.
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.