OpenVLA TPS calculator

Open weights Stanford University,University of California (UC) Berkeley,Toyota Research Institute,Google DeepMind,Massachusetts Institute of Technology (MIT),Physical Intelligence 7.2B parameters June 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

589 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla K20c

5 GB · Q3_K_M · 28.1 tok/s

Fastest card

B200

471 tok/s · 180 GB

Which GPUs can run OpenVLA?

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

589 cards match

Calculating

					Needs	Quantisation	Fit
471 tok/s 283–754 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	8.4 GB	Q8_0	Comfortable
471 tok/s 283–754 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	8.4 GB	Q8_0	Comfortable
376 tok/s 226–602 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	8.4 GB	Q8_0	Comfortable
376 tok/s 226–602 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	8.4 GB	Q8_0	Comfortable
301 tok/s 181–482 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	8.4 GB	Q8_0	Comfortable
288 tok/s 173–461 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	8.4 GB	Q8_0	Comfortable
288 tok/s 173–461 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	8.4 GB	Q8_0	Comfortable
276 tok/s 165–441 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	8.4 GB	Q8_0	Comfortable
245 tok/s 147–392 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	8.4 GB	Q8_0	Comfortable
245 tok/s 147–392 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	8.4 GB	Q8_0	Comfortable
245 tok/s 147–392 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	8.4 GB	Q8_0	Comfortable
232 tok/s 139–371 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	8.4 GB	Q8_0	Comfortable
198 tok/s 119–317 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	8.4 GB	Q8_0	Comfortable
198 tok/s 119–317 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	8.4 GB	Q8_0	Comfortable
198 tok/s 119–317 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	8.4 GB	Q8_0	Comfortable
198 tok/s 119–317 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	8.4 GB	Q8_0	Comfortable
198 tok/s 119–317 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	8.4 GB	Q8_0	Comfortable
151 tok/s 90–241 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	8.4 GB	Q8_0	Comfortable
151 tok/s 90–241 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	8.4 GB	Q8_0	Comfortable
128 tok/s 77–204 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.7 GB	Q6_K	Tight
126 tok/s 75–201 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	8.4 GB	Q8_0	Comfortable
123 tok/s 74–197 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	8.4 GB	Q8_0	Comfortable
120 tok/s 72–192 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	8.4 GB	Q8_0	Comfortable
120 tok/s 72–192 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	8.4 GB	Q8_0	Comfortable
120 tok/s 72–192 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	8.4 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: Stanford University,University of California (UC) Berkeley,Toyota Research Institute,Google DeepMind,Massachusetts Institute of Technology (MIT),Physical Intelligence
Organisation type: Academia,Academia,Industry,Industry,Academia,Industry
Country: United States of America
Published: 13 June 2024
Authors: Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, Chelsea Finn

What it does

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

Domain: Robotics, Vision, Language
Task: Robotic manipulation
Approach: Supervised
Base model: Llama 2-7B
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: 7.2B
Training data: tokens
Epochs: 27
Batch size: 2,048

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: 1.1 × 10²³ FLOP
How it was established: Hardware
Fine-tuning compute: 9.7 × 10²¹ 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: 64
Wall-clock time: 336 hours (14 days)
Power draw: 50.5 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

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Likely above 10²³ FLOP: Yes
Why it is tracked: SOTA improvement
Record confidence: Confident
Citations: 2,123

Sources

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

Reference: OpenVLA: An Open-Source Vision-Language-Action Mode
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run OpenVLA

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 OpenVLA

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

The hardware side

Minimum card

Tesla K20c

Memory needed

4.2 GB

Fastest

471 tok/s

OpenVLA reaches a parameter count of 7.2B. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 589.

At the low end it is handled by Tesla K20c, with a memory capacity of 5 GB, running it at a compression of Q3_K_M and producing around 28.1 tokens per second.

Top of the range is B200, generating roughly 471 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

OpenVLA was published by Stanford University,University of California (UC) Berkeley,Toyota Research Institute,Google DeepMind,Massachusetts Institute of Technology (MIT),Physical Intelligence, in the country recorded as United States of America, during June 2024. It comes out of an organisation categorised as academia,Academia,Industry,Industry,Academia,Industry.

It works in the domain of Robotics, Vision, Language, and is recorded as performing the task of robotic manipulation.

Its starting point was an existing base model, Llama 2-7B. Most models at this scale are adapted from an existing base rather than built from nothing.

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.

What decides the speed

Across every card that can run it, the middle of the range sits at 25.5 tokens per second. Producing text faster than most people read it: 559 of them.

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.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

How it was trained

Producing it required arithmetic totalling around 1.1 × 10²³ FLOP, on hardware recorded as NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.

It is tracked in the underlying dataset for one reason in particular: sOTA improvement.

Step by step

How to choose a GPU for OpenVLA

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 able to hold OpenVLA, needing around 4.2 GB at a compression of Q3_K_M. That figure, not the headline performance of a card, is what decides whether it runs.
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, because at long context a card that handles short questions easily can be dropped by OpenVLA.
03

Set a quality floor

Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of Q3_K_M on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second follows memory bandwidth rather than core counts, for OpenVLA. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 471 tok/s.
05

Look at the headroom, not just the fit

Tight means it loads and works with no room to raise the context later, in the case of OpenVLA. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
06

Open the card you have settled on

Following a card through to its own page shows every other model it can hold, which is the question that follows once you have settled on OpenVLA.

Answers

OpenVLA — common questions

OpenVLA— why does the quantisation differ between cards?

A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

OpenVLA— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 283–754 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

OpenVLA— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Tesla K20c, with a memory capacity of 5 GB. It runs the model at a compression of Q3_K_M using about 4.2 GB, and produces roughly 28.1 tokens per second. The number of cards able to run it in total: 589.

OpenVLA— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 471 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 559.

OpenVLA— how much VRAM does it need?

It needs about 4.2 GB at a compression of Q3_K_M, 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.

OpenVLA— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q6_K, using about 6.7 GB and generating roughly 128 tokens per second. The fit is tight.

OpenVLA— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q8_0, using about 8.4 GB and generating roughly 53.8 tokens per second. The fit is comfortable.

OpenVLA— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 8.4 GB and generating roughly 66.6 tokens per second. The fit is comfortable.

OpenVLA— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 8.4 GB and generating roughly 79.0 tokens per second. The fit is comfortable.

OpenVLA— is it open source?

Its weights are published, so it 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.

OpenVLA— how many parameters does it have?

It has a parameter count of 7.2B. Based on a Prismatic-7B VLM backbone, which itself is comprised of 600M parameter vision encoder (DinoV2 + SigLIP) plus Llama-2 7B. Table 1 indicates 7.1881 billion trainable 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.

OpenVLA— who created it?

It was published by Stanford University,University of California (UC) Berkeley,Toyota Research Institute,Google DeepMind,Massachusetts Institute of Technology (MIT),Physical Intelligence, based in United States of America, an organisation categorised as academia,Academia,Industry,Industry,Academia,Industry.

OpenVLA— when was it released?

It was published in June 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.

OpenVLA— what is it used for?

It works in the domain of Robotics, Vision, Language, and is recorded as handling the task of robotic manipulation. 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.

OpenVLA— where can I download it?

The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

OpenVLA— how much compute was used to train it?

Training consumed around 1.1 × 10²³ FLOP, on hardware recorded as NVIDIA A100. 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.

OpenVLA— can I run it 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 model is rarely worth using. The nearest miss we calculate falls short by 1.4 GB. Every figure here assumes the whole model is resident on the card.

OpenVLA— would two GPUs run it faster?

Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 589. So a second card is rarely the answer here.

Source

Original publication

Record last updated 25 May 2026

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.

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