Qwen3.5-27B TPS calculator

Open weights Alibaba 27B parameters February 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

241 of 818 cards that can run it

Smallest card that fits

Xeon Phi 7120P

16 GB · Q3_K_M · 9.7 tok/s

Fastest card

B200

125 tok/s · 180 GB

Which GPUs can run Qwen3.5-27B?

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

241 cards match

Calculating

					Needs	Quantisation	Fit
125 tok/s 75–201 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	29.6 GB	Q8_0	Comfortable
125 tok/s 75–201 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	29.6 GB	Q8_0	Comfortable
100 tok/s 60–160 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	29.6 GB	Q8_0	Comfortable
100 tok/s 60–160 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	29.6 GB	Q8_0	Comfortable
80.1 tok/s 48–128 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	29.6 GB	Q8_0	Comfortable
76.7 tok/s 46–123 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	29.6 GB	Q8_0	Comfortable
76.7 tok/s 46–123 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	29.6 GB	Q8_0	Comfortable
73.4 tok/s 44–117 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	29.6 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	29.6 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	29.6 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	29.6 GB	Q8_0	Comfortable
61.8 tok/s 37–99 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	29.6 GB	Q8_0	Comfortable
52.7 tok/s 32–84 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	29.6 GB	Q8_0	Comfortable
52.7 tok/s 32–84 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	29.6 GB	Q8_0	Comfortable
52.7 tok/s 32–84 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	29.6 GB	Q8_0	Comfortable
52.7 tok/s 32–84 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	29.6 GB	Q8_0	Comfortable
52.7 tok/s 32–84 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	29.6 GB	Q8_0	Comfortable
47.8 tok/s 29–77 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	13.9 GB	Q3_K_M	Tight
42.6 tok/s 26–68 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	23.3 GB	Q6_K	Comfortable
42.6 tok/s 26–68 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	23.3 GB	Q6_K	Comfortable
40.8 tok/s 24–65 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	23.3 GB	Q6_K	Comfortable
40.8 tok/s 24–65 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	23.3 GB	Q6_K	Comfortable
40.6 tok/s 24–65 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	13.9 GB	Q3_K_M	Tight
40.1 tok/s 24–64 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	29.6 GB	Q8_0	Comfortable
40.1 tok/s 24–64 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	29.6 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: 24 February 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: 27B
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 (restricted use)
Hugging Face: Qwen

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: Qwen3.5: Towards Native Multimodal Agents
Last updated: 19 June 2026

The extremes

The ten fastest GPUs for Qwen3.5-27B

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 Qwen3.5-27B

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

Xeon Phi 7120P

Memory needed

13.9 GB

Fastest

125 tok/s

With 27B parameters, Qwen3.5-27B lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.

The entry point is the Xeon Phi 7120P: 16 GB of memory, Q3_K_M compression, roughly 9.7 tokens per second.

The quickest result comes from a B200 at around 125 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

What this model is

Qwen3.5-27B was published by Alibaba, in China, in February 2026. industry is the category the publisher falls under.

It works in Language, and is recorded as doing language modeling/generation.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. It is published under the Qwen organisation on Hugging Face.

What decides the speed

Half the cards that hold it manage more than 19.0 tokens per second, and 196 exceed reading speed outright.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

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.

Step by step

How to choose a GPU for Qwen3.5-27B

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

Look at what Qwen3.5-27B actually needs — around 13.9 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
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 Qwen3.5-27B.
03

Set a quality floor

Each card runs the least-compressed copy it can hold — Q3_K_M on the smallest card that fits. Setting a floor drops the cards that only manage Qwen3.5-27B by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for Qwen3.5-27B follows memory bandwidth, not core counts, which is why the B200 tops it at 125 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs Qwen3.5-27B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

See what else that card runs

Following a card through to its own page shows every other model it can hold, which is the question that follows once Qwen3.5-27B is settled.

Answers

Qwen3.5-27B — common questions

How many parameters does Qwen3.5-27B have?

Qwen3.5-27B has 27B 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 Qwen3.5-27B?

Qwen3.5-27B was published by Alibaba, based in China, categorised as industry.

When was Qwen3.5-27B released?

Qwen3.5-27B was published in February 2026.

What is Qwen3.5-27B used for?

Qwen3.5-27B works in Language, and is recorded as handling language modeling/generation. 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 Qwen3.5-27B?

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.

Can I run Qwen3.5-27B if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down — the nearest miss we calculate is short by 6.2 GB. Our figures for Qwen3.5-27B assume it is fully resident.

Would two GPUs run Qwen3.5-27B faster?

Capacity adds across cards; throughput does not. Since 241 of the cards we track already hold Qwen3.5-27B on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Qwen3.5-27B?

Because capacity varies, so does how hard Qwen3.5-27B has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Qwen3.5-27B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 75–201 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 Qwen3.5-27B?

The smallest card in our catalogue that holds Qwen3.5-27B is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at Q3_K_M using about 13.9 GB, and produces roughly 9.7 tokens per second. 241 cards in total can run it.

How fast is Qwen3.5-27B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 125 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 196 of the cards that can run Qwen3.5-27B clear that.

How much VRAM does Qwen3.5-27B need?

About 13.9 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.

Can I run Qwen3.5-27B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q3_K_M, using about 13.9 GB and generating roughly 47.8 tokens per second — a tight fit.

Can I run Qwen3.5-27B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q5_K_M, using about 20.2 GB and generating roughly 37.5 tokens per second — a tight fit.

Is Qwen3.5-27B open source?

Its weights are published, so Qwen3.5-27B 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.

Source

Original publication

Record last updated 19 June 2026

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