Qwen3.6-35B-A3B TPS calculator

Open weights Alibaba 35B parameters April 2026

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

132 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX A4500

20 GB · IQ4_XS · 106 tok/s

Fastest card

B200

538 tok/s · 180 GB

Which GPUs can run Qwen3.6-35B-A3B?

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

132 cards match

Calculating

					Needs	Quantisation	Fit
538 tok/s 323–861 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	36.1 GB	Q8_0	Comfortable
538 tok/s 323–861 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	36.1 GB	Q8_0	Comfortable
429 tok/s 258–687 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	36.1 GB	Q8_0	Comfortable
429 tok/s 258–687 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	36.1 GB	Q8_0	Comfortable
343 tok/s 206–550 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	36.1 GB	Q8_0	Comfortable
329 tok/s 197–526 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	36.1 GB	Q8_0	Comfortable
329 tok/s 197–526 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	36.1 GB	Q8_0	Comfortable
315 tok/s 189–503 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	36.1 GB	Q8_0	Comfortable
279 tok/s 168–447 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	36.1 GB	Q8_0	Comfortable
279 tok/s 168–447 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	36.1 GB	Q8_0	Comfortable
279 tok/s 168–447 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	36.1 GB	Q8_0	Comfortable
265 tok/s 159–424 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	36.1 GB	Q8_0	Comfortable
226 tok/s 136–361 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	36.1 GB	Q8_0	Comfortable
226 tok/s 136–361 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	36.1 GB	Q8_0	Comfortable
226 tok/s 136–361 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	36.1 GB	Q8_0	Comfortable
226 tok/s 136–361 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	36.1 GB	Q8_0	Comfortable
226 tok/s 136–361 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	36.1 GB	Q8_0	Comfortable
208 tok/s 125–333 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	19.8 GB	Q4_K_M	Tight
189 tok/s 114–303 · low confidence	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	19.8 GB	Q4_K_M	Tight
183 tok/s 110–292 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	28.0 GB	Q6_K	Tight
183 tok/s 110–292 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	28.0 GB	Q6_K	Tight
175 tok/s 105–280 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	28.0 GB	Q6_K	Tight
175 tok/s 105–280 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	28.0 GB	Q6_K	Tight
172 tok/s 103–275 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	36.1 GB	Q8_0	Comfortable
172 tok/s 103–275 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	36.1 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: Alibaba
Organisation type: Industry
Country: China
Published: 21 April 2026

What it does

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

Domain: Language
Task: Language modeling/generation

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: 35B
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 (unrestricted)

The extremes

The ten fastest GPUs that run Qwen3.6-35B-A3B

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 Qwen3.6-35B-A3B

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

RTX A4500

Memory needed

17.8 GB

Fastest

538 tok/s

Qwen3.6-35B-A3B reaches a parameter count of 35B. That lands in the range a serious desktop card can handle once the weights are compressed. The number of cards we track that can run it: 132.

The least hardware that works is RTX A4500, with a memory capacity of 20 GB, running it at a compression of IQ4_XS and producing around 106 tokens per second.

The quickest result comes from B200, generating roughly 538 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

Qwen3.6-35B-A3B was published by Alibaba, in the country recorded as China, during April 2026. The category the publisher falls under is industry.

It works in the domain of Language, and is recorded as performing the task of language modeling/generation.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

What decides the speed

Across every card that can run it, the middle of the range sits at 104.9 tokens per second. Exceeding reading speed outright: 132 of them.

This is a mixture-of-experts model, which routes each token through only part of itself. It therefore generates far faster than its total size suggests — while still needing every parameter resident in memory, so it is quick without being cheap to hold.

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 Qwen3.6-35B-A3B

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 Qwen3.6-35B-A3B, needing around 17.8 GB at a compression of IQ4_XS. That figure, not the headline performance of a card, is what decides whether it runs.
02

Decide how long your conversations run

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason a card that seemed fine stops fitting Qwen3.6-35B-A3B.
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold, reaching a compression of IQ4_XS 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

Compare tokens per second, not specifications

Ranking by tokens per second follows memory bandwidth rather than core counts, for Qwen3.6-35B-A3B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 538 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage it from those with room to spare, in the case of Qwen3.6-35B-A3B. 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 Qwen3.6-35B-A3B.

Answers

Qwen3.6-35B-A3B — common questions

Qwen3.6-35B-A3B— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 132. So a second card is rarely the answer here.

Qwen3.6-35B-A3B— 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: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Qwen3.6-35B-A3B— 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: 323–861 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Qwen3.6-35B-A3B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is RTX A4500, with a memory capacity of 20 GB. It runs the model at a compression of IQ4_XS using about 17.8 GB, and produces roughly 106 tokens per second. The number of cards able to run it in total: 132.

Qwen3.6-35B-A3B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 538 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: 132.

Qwen3.6-35B-A3B— how much VRAM does it need?

It needs about 17.8 GB at a compression of IQ4_XS, 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.

Qwen3.6-35B-A3B— 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 Q4_K_M, using about 19.8 GB and generating roughly 208 tokens per second. The fit is tight.

Qwen3.6-35B-A3B— 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.

Qwen3.6-35B-A3B— how many parameters does it have?

It has a parameter count of 35B. 35B total, 3B active MoE. 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.

Qwen3.6-35B-A3B— who created it?

It was published by Alibaba, based in China, an organisation categorised as industry.

Qwen3.6-35B-A3B— when was it released?

It was published in April 2026.

Qwen3.6-35B-A3B— what is it used for?

It works in the domain of Language, and is recorded as handling the task of language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Qwen3.6-35B-A3B— 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.

Qwen3.6-35B-A3B— 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 5.4 GB. Every figure here assumes the whole model is resident on the card.

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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