Qwen3.8-27B TPS calculator

Open weights Alibaba 27.8B parameters August 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 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 7120P

16 GB · Q3_K_M · 9.4 tok/s

Fastest card

B200

122 tok/s · 180 GB

Which GPUs can run Qwen3.8-27B?

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
122 tok/s 104–146	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	29.2 GB	Q8_0	Comfortable
122 tok/s 104–146	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	29.2 GB	Q8_0	Comfortable
97.4 tok/s 58–156 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	29.2 GB	Q8_0	Comfortable
97.4 tok/s 58–156 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	29.2 GB	Q8_0	Comfortable
77.9 tok/s 47–125 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	29.2 GB	Q8_0	Comfortable
74.6 tok/s 63–89	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	29.2 GB	Q8_0	Comfortable
74.6 tok/s 63–89	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	29.2 GB	Q8_0	Comfortable
71.4 tok/s 43–114 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	29.2 GB	Q8_0	Comfortable
63.3 tok/s 38–101 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	29.2 GB	Q8_0	Comfortable
63.3 tok/s 38–101 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	29.2 GB	Q8_0	Comfortable
63.3 tok/s 38–101 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	29.2 GB	Q8_0	Comfortable
60.1 tok/s 51–72	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	29.2 GB	Q8_0	Comfortable
51.2 tok/s 44–61	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	29.2 GB	Q8_0	Comfortable
51.2 tok/s 44–61	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	29.2 GB	Q8_0	Comfortable
51.2 tok/s 44–61	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	29.2 GB	Q8_0	Comfortable
51.2 tok/s 44–61	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	29.2 GB	Q8_0	Comfortable
51.2 tok/s 44–61	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	29.2 GB	Q8_0	Comfortable
46.5 tok/s 40–56	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	13.0 GB	Q3_K_M	Tight
41.4 tok/s 35–50	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	22.7 GB	Q6_K	Comfortable
41.4 tok/s 35–50	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	22.7 GB	Q6_K	Comfortable
39.7 tok/s 34–48	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	22.7 GB	Q6_K	Comfortable
39.7 tok/s 34–48	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	22.7 GB	Q6_K	Comfortable
39.5 tok/s 34–47	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	13.0 GB	Q3_K_M	Tight
39.0 tok/s 23–62 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	29.2 GB	Q8_0	Comfortable
39.0 tok/s 23–62 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	29.2 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: 14 August 2026

What it does

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

Domain: Language, Vision, Multimodal
Task: Language modeling/generation, Question answering, Code generation
Approach: Hybrid linear/full attention transformer with a vision encoder
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: 27.8B
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)
Hugging Face: Qwen

How it is classified

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

Why it is tracked: Highly cited or historically significant
Record confidence: Confident

Sources

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

Reference: Qwen3.8-27B model card

The extremes

The ten fastest GPUs that run Qwen3.8-27B

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 Qwen3.8-27B

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

What it takes to run this model

Minimum card

Xeon Phi 7120P

Memory needed

13.0 GB

Fastest

122 tok/s

Qwen3.8-27B reaches a parameter count of 27.8B. 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: 241.

The smallest card that holds it is Xeon Phi 7120P, with a memory capacity of 16 GB, running it at a compression of Q3_K_M and producing around 9.4 tokens per second.

The fastest we calculate for it is B200, generating roughly 122 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

Qwen3.8-27B was published by Alibaba, in the country recorded as China, during August 2026. It comes out of an organisation categorised as industry.

It works in the domain of Language, Vision, Multimodal, and is recorded as performing the task of language modeling/generation, Question answering, Code generation.

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. On Hugging Face it is published under the organisation Qwen.

How fast it runs, and why

Half the cards that hold it manage more than 18.5 tokens per second. Exceeding reading speed outright: 193 of them.

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

Its attention layout is on file, so the memory figures are computed exactly rather than approximated.

What went into building it

Its inclusion criterion: highly cited or historically significant.

Step by step

How to choose a GPU for Qwen3.8-27B

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

Start from what it actually needs, which is the requirement of Qwen3.8-27B, needing around 13.0 GB at a compression of Q3_K_M. Capacity is the gate — a card either holds it or it does not.
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, because at long context a card that handles short questions easily can be dropped by Qwen3.8-27B.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy, 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

Sort by speed to see how cards rank for Qwen3.8-27B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 122 tok/s.
05

Check the fit verdict before buying

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of Qwen3.8-27B. 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

See what else that card runs

Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond Qwen3.8-27B.

Answers

Qwen3.8-27B — common questions

Qwen3.8-27B— when was it released?

It was published in August 2026.

Qwen3.8-27B— what is it used for?

It works in the domain of Language, Vision, Multimodal, and is recorded as handling the task of language modeling/generation, Question answering, Code 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.

Qwen3.8-27B— where can I download it?

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

Qwen3.8-27B— 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 5.5 GB. Every figure here assumes the whole model is resident on the card.

Qwen3.8-27B— would two GPUs run it faster?

A second card roughly doubles the memory available but not the generation rate. The number already able to run it alone: 241. So a second card is rarely the answer here.

Qwen3.8-27B— 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.

Qwen3.8-27B— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 104–146 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Qwen3.8-27B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Xeon Phi 7120P, with a memory capacity of 16 GB. It runs the model at a compression of Q3_K_M using about 13.0 GB, and produces roughly 9.4 tokens per second. The number of cards able to run it in total: 241.

Qwen3.8-27B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 122 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: 193.

Qwen3.8-27B— how much VRAM does it need?

It needs about 13.0 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.

Qwen3.8-27B— 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 Q3_K_M, using about 13.0 GB and generating roughly 46.5 tokens per second. The fit is tight.

Qwen3.8-27B— 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 Q5_K_M, using about 19.5 GB and generating roughly 36.5 tokens per second. The fit is tight.

Qwen3.8-27B— 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.

Qwen3.8-27B— how many parameters does it have?

It has a parameter count of 27.8B. Dense 27.78B. Hybrid attention: 16 x (3 x Gated DeltaNet -> 1 x Gated Attention), so only 16 of 64 layers hold a KV cache. 24 Q heads, 4 KV heads, head_dim 256. Native context 262,144, extensible to 1,000,000. 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.

Qwen3.8-27B— who created it?

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

Source

Original publication

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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