BlenderBot 3 TPS calculator

Open weights McGill University,Meta AI,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms) 175B parameters August 2022

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

24 cards that can run it

818 cards we hold specifications for

Smallest card that fits

H100 PCIe 96 GB

96 GB · Q3_K_M · 21.9 tok/s

Fastest card

Radeon Instinct MI300

28.6 tok/s · 128 GB

Which GPUs can run BlenderBot 3?

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

24 cards match

Calculating

					Needs	Quantisation	Fit
28.6 tok/s 17–46 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	106.6 GB	Q4_K_M	Tight
28.1 tok/s 17–45 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	147.3 GB	Q6_K	Tight
27.3 tok/s 16–44 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	106.6 GB	Q4_K_M	Tight
27.3 tok/s 16–44 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	106.6 GB	Q4_K_M	Tight
23.2 tok/s 14–37 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	106.6 GB	Q4_K_M	Tight
21.9 tok/s 13–35 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	86.2 GB	Q3_K_M	Tight
21.9 tok/s 13–35 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	86.2 GB	Q3_K_M	Tight
19.4 tok/s 12–31 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	188.1 GB	Q8_0	Comfortable
15.5 tok/s 9–25 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	188.1 GB	Q8_0	Comfortable
15.5 tok/s 9–25 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	188.1 GB	Q8_0	Comfortable
14.6 tok/s 9–23 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	147.3 GB	Q6_K	Tight
14.6 tok/s 9–23 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	147.3 GB	Q6_K	Tight
14.3 tok/s 9–23 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	106.6 GB	Q4_K_M	Tight
14.3 tok/s 9–23 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	106.6 GB	Q4_K_M	Tight
11.9 tok/s 7–19 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	106.6 GB	Q4_K_M	Tight
11.7 tok/s 7–19 · low confidence	RTX PRO 6000 Blackwell NVIDIA	96 GB	1,790 GB/s	Mar 2025	86.2 GB	Q3_K_M	Tight
11.7 tok/s 7–19 · low confidence	RTX PRO 6000 Blackwell Max-Q NVIDIA	96 GB	1,790 GB/s	Mar 2025	86.2 GB	Q3_K_M	Tight
11.7 tok/s 7–19 · low confidence	RTX PRO 6000 Blackwell Server NVIDIA	96 GB	1,790 GB/s	Mar 2025	86.2 GB	Q3_K_M	Tight
11.7 tok/s 7–19 · low confidence	RTX PRO 6000D Blackwell Max-Q NVIDIA	96 GB	1,790 GB/s	Mar 2025	86.2 GB	Q3_K_M	Tight
11.7 tok/s 7–19 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	106.6 GB	Q4_K_M	Tight
11.3 tok/s 7–18 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	188.1 GB	Q8_0	Comfortable
10.4 tok/s 6–17 · low confidence	Data Center GPU Max 1350 Intel	96 GB	2,460 GB/s	Jan 2023	86.2 GB	Q3_K_M	Tight
1.5 tok/s 1–2 · low confidence	GB10 NVIDIA	128 GB	273 GB/s	Oct 2025	106.6 GB	Q4_K_M	Tight
1.5 tok/s 1–2 · low confidence	Jetson T5000 NVIDIA	128 GB	273 GB/s	Aug 2025	106.6 GB	Q4_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: McGill University,Meta AI,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms)
Organisation type: Academia,Industry,Academia
Country: Canada, United States of America
Published: 10 August 2022
Authors: Kurt Shuster, Jing Xu, Mojtaba Komeili, Da Ju, Eric Michael Smith, Stephen Roller, Megan Ung, Moya Chen, Kushal Arora, Joshua Lane, Morteza Behrooz, William Ngan, Spencer Poff, Naman Goyal, Arthur Szlam, Y-Lan Boureau, Melanie Kambadur, Jason Weston

What it does

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

Domain: Language
Task: Chat
Base model: OPT-175B

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: 175B
Training data: 1,300,000,000 tokens
Batch size: 262,144

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: 4.3 × 10²³ FLOP
How it was established: Operation counting
Fine-tuning compute: 1.5 × 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 SXM4 40 GB
Chips used: 128
Power draw: 102.6 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 (non-commercial)
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: Likely
Citations: 290

Sources

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

Reference: BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run BlenderBot 3

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 BlenderBot 3

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

What you need to run it

Minimum card

H100 PCIe 96 GB

Memory needed

86.2 GB

Fastest

28.6 tok/s

BlenderBot 3 sits at 175B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 24 of the cards we track can hold it.

The least hardware that works is a H100 PCIe 96 GB. Its 96 GB is enough at Q3_K_M compression, giving roughly 21.9 tokens per second.

The quickest result comes from a Radeon Instinct MI300 at around 28.6 tokens per second — its 6,550 GB/s of bandwidth is what buys that.

Background

BlenderBot 3 was published by McGill University,Meta AI,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms), in Canada, in August 2022. academia,Industry,Academia is the category the publisher falls under.

It works in Language, and is recorded as doing chat.

It builds on OPT-175B, 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.

Reading the throughput figures

The median result is around 14.5 tokens per second; 22 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.

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.

What went into building it

Producing it required around 4.3 × 10²³ FLOP of arithmetic, on NVIDIA A100 SXM4 40 GB, which is a statement about the training budget rather than about inference.

The training set ran to roughly 1,300,000,000 tokens.

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for BlenderBot 3

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

The table lists every card that can hold BlenderBot 3 — around 86.2 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for BlenderBot 3.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of BlenderBot 3 — 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

The speed ordering for BlenderBot 3 is effectively an ordering by memory bandwidth, which is why the Radeon Instinct MI300 tops it at 28.6 tok/s.
05

Read the fit column last

The fit column separates cards that just manage BlenderBot 3 from those with room to spare. Buy for the second if the context might grow.
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 BlenderBot 3 alone — a card is usually bought for more than one model.

Answers

BlenderBot 3 — common questions

How accurate are these BlenderBot 3 speed estimates?

These are estimates with real error bars. The fastest result here, 17–46 tok/s on the Radeon Instinct MI300, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run BlenderBot 3?

The smallest card in our catalogue that holds BlenderBot 3 is the H100 PCIe 96 GB, with 96 GB of memory. It runs the model at Q3_K_M using about 86.2 GB, and produces roughly 21.9 tokens per second. 24 cards in total can run it.

How fast is BlenderBot 3 on a GPU?

It depends on the card. The quickest we calculate is a Radeon Instinct MI300 at about 28.6 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 22 of the cards that can run BlenderBot 3 clear that.

How much VRAM does BlenderBot 3 need?

About 86.2 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.

Is BlenderBot 3 open source?

Its weights are published, so BlenderBot 3 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 BlenderBot 3 have?

BlenderBot 3 has 175B 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 BlenderBot 3?

BlenderBot 3 was published by McGill University,Meta AI,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms), based in Canada, categorised as academia,Industry,Academia.

When was BlenderBot 3 released?

BlenderBot 3 was published in August 2022. 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 BlenderBot 3 used for?

BlenderBot 3 works in Language, and is recorded as handling chat. 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 BlenderBot 3?

The weights for BlenderBot 3 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 BlenderBot 3?

Around 4.3 × 10²³ FLOP, on NVIDIA A100 SXM4 40 GB. 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 BlenderBot 3 if it does not fit in my GPU?

It can be split between the card and system memory, but BlenderBot 3 generates painfully slowly that way — the nearest miss we calculate is short by 22.0 GB. Nothing on this page assumes offloading.

Would two GPUs run BlenderBot 3 faster?

A second card roughly doubles the memory available but not the generation rate. With 24 cards already able to run BlenderBot 3 alone, the case for pairing is weak.

Why does the quantisation differ between cards for BlenderBot 3?

Because capacity varies, so does how hard BlenderBot 3 has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

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.

All AI models