Qwen3-235B-A22B (Jul 2025) TPS calculator

Open weights Alibaba 235B parameters July 2025

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

17 of 818 cards that can run it

Smallest card that fits

Radeon Instinct MI250

128 GB · Q3_K_M · 69.1 tok/s

Fastest card

B200

143 tok/s · 180 GB

Which GPUs can run Qwen3-235B-A22B (Jul 2025)?

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.

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Card memory

Also show cards that cannot run it

17 cards match

Calculating

					Needs	Quantisation	Fit
143 tok/s 86–229 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	157.1 GB	Q5_K_M	Tight
138 tok/s 83–221 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	102.4 GB	Q3_K_M	Tight
120 tok/s 72–192 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	116.0 GB	IQ4_XS	Tight
120 tok/s 72–192 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	116.0 GB	IQ4_XS	Tight
112 tok/s 67–180 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	102.4 GB	Q3_K_M	Tight
80.1 tok/s 48–128 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	239.1 GB	Q8_0	Tight
74.3 tok/s 45–119 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	157.1 GB	Q5_K_M	Tight
74.3 tok/s 45–119 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	157.1 GB	Q5_K_M	Tight
69.1 tok/s 41–111 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	102.4 GB	Q3_K_M	Tight
69.1 tok/s 41–111 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	102.4 GB	Q3_K_M	Tight
68.1 tok/s 41–109 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	184.4 GB	Q6_K	Comfortable
64.0 tok/s 38–102 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	239.1 GB	Q8_0	Tight
64.0 tok/s 38–102 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	239.1 GB	Q8_0	Tight
57.6 tok/s 35–92 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	102.4 GB	Q3_K_M	Tight
56.4 tok/s 34–90 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	102.4 GB	Q3_K_M	Tight
7.4 tok/s 4–12 · low confidence	GB10 NVIDIA	128 GB	273 GB/s	Oct 2025	102.4 GB	Q3_K_M	Tight
7.4 tok/s 4–12 · low confidence	Jetson T5000 NVIDIA	128 GB	273 GB/s	Aug 2025	102.4 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: Alibaba
Organisation type: Industry
Country: China
Published: 25 July 2025

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, Mathematical reasoning, Quantitative reasoning, Code generation, Translation

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: 235B
Training data: 36,000,000,000,000 tokens
Epochs: 1

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: 4.8 × 10²⁴ FLOP
How it was established: Operation counting

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: Unreleased
Hugging Face: Qwen

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: Discretionary
Record confidence: Likely

Sources

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

Reference: Qwen3: Think Deeper, Act Faster
Last updated: 12 February 2026

The extremes

The ten fastest GPUs for Qwen3-235B-A22B (Jul 2025)

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 Qwen3-235B-A22B (Jul 2025)

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

Hardware requirements in practice

Minimum card

Radeon Instinct MI250

Memory needed

102.4 GB

Fastest

143 tok/s

At 235B parameters, Qwen3-235B-A22B (Jul 2025) is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job — 17 of the cards we track can hold it on their own, and all of them are datacentre parts.

The entry point is the Radeon Instinct MI250: 128 GB of memory, Q3_K_M compression, roughly 69.1 tokens per second.

The quickest result comes from a B200 at around 143 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

Qwen3-235B-A22B (Jul 2025) was published by Alibaba, in China, in July 2025. The organisation is categorised as industry.

It works in Language, and is recorded as doing language modeling/generation, Question answering, Mathematical reasoning, Quantitative reasoning, Code generation, Translation.

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 Qwen organisation on Hugging Face.

Reading the throughput figures

The median result is around 69.1 tokens per second; 15 cards produce text faster than most people read it.

Mixture-of-experts routing is why the speeds here look high for the parameter count. Only a fraction is read per token, but the whole thing has to be loaded.

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

The training run consumed about 4.8 × 10²⁴ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Around 36,000,000,000,000 tokens went into training it.

Its inclusion criterion is discretionary.

Step by step

How to choose a GPU for Qwen3-235B-A22B (Jul 2025)

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

Look at what Qwen3-235B-A22B (Jul 2025) actually needs — around 102.4 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

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Qwen3-235B-A22B (Jul 2025) can slip off a card that handles short questions easily.
03

Choose how far you will compress it

Compression is what makes Qwen3-235B-A22B (Jul 2025) fit smaller cards, at some cost in accuracy — Q3_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

The speed ordering for Qwen3-235B-A22B (Jul 2025) is effectively an ordering by memory bandwidth, which is why the B200 tops it at 143 tok/s.
05

Check the fit verdict before buying

The fit column separates cards that just manage Qwen3-235B-A22B (Jul 2025) from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Qwen3-235B-A22B (Jul 2025).

Answers

Qwen3-235B-A22B (Jul 2025) — common questions

Why does the quantisation differ between cards for Qwen3-235B-A22B (Jul 2025)?

Each card is shown running the least-compressed copy it can hold, and Qwen3-235B-A22B (Jul 2025) appears at 5 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Qwen3-235B-A22B (Jul 2025) 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 86–229 tok/s on the B200 rather than a single number.

What GPU do I need to run Qwen3-235B-A22B (Jul 2025)?

The smallest card in our catalogue that holds Qwen3-235B-A22B (Jul 2025) is the Radeon Instinct MI250, with 128 GB of memory. It runs the model at Q3_K_M using about 102.4 GB, and produces roughly 69.1 tokens per second. 17 cards in total can run it.

How fast is Qwen3-235B-A22B (Jul 2025) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 143 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 15 of the cards that can run Qwen3-235B-A22B (Jul 2025) clear that.

How much VRAM does Qwen3-235B-A22B (Jul 2025) need?

About 102.4 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 Qwen3-235B-A22B (Jul 2025) open source?

Its weights are published, so Qwen3-235B-A22B (Jul 2025) 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 Qwen3-235B-A22B (Jul 2025) have?

Qwen3-235B-A22B (Jul 2025) has 235B parameters. 235 billion total parameters and 22 billion activated parameters Number of Layers: 94 Number of Attention Heads (GQA): 64 for Q and 4 for KV Number of Experts: 128 Number of Activated Experts: 8 Context Length: 32,768 natively and 131,072 tokens with YaRN. 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 Qwen3-235B-A22B (Jul 2025)?

Qwen3-235B-A22B (Jul 2025) was published by Alibaba, based in China, categorised as industry.

When was Qwen3-235B-A22B (Jul 2025) released?

Qwen3-235B-A22B (Jul 2025) was published in July 2025.

What is Qwen3-235B-A22B (Jul 2025) used for?

Qwen3-235B-A22B (Jul 2025) works in Language, and is recorded as handling language modeling/generation, Question answering, Mathematical reasoning, Quantitative reasoning, Code generation, Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Qwen3-235B-A22B (Jul 2025)?

Its weights are published under the Qwen organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

How much compute was used to train Qwen3-235B-A22B (Jul 2025)?

Around 4.8 × 10²⁴ FLOP. 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 Qwen3-235B-A22B (Jul 2025) 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 Qwen3-235B-A22B (Jul 2025) is rarely worth using — the nearest miss we calculate is short by 43.3 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run Qwen3-235B-A22B (Jul 2025) faster?

Capacity adds across cards; throughput does not. Since 17 of the cards we track already hold Qwen3-235B-A22B (Jul 2025) on their own, a second card is rarely the answer here.

Source

Original publication

Record last updated 12 February 2026

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