Qwen3-14B TPS calculator

Open weights Alibaba 14.8B parameters April 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

306 of 818 cards that can run it

Smallest card that fits

P102-101

10 GB · Q3_K_M · 21.0 tok/s

Fastest card

B200

229 tok/s · 180 GB

Which GPUs can run Qwen3-14B?

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

306 cards match

Calculating

					Needs	Quantisation	Fit
229 tok/s 195–275	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	16.9 GB	Q8_0	Comfortable
229 tok/s 195–275	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	16.9 GB	Q8_0	Comfortable
183 tok/s 110–293 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	16.9 GB	Q8_0	Comfortable
183 tok/s 110–293 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	16.9 GB	Q8_0	Comfortable
146 tok/s 88–234 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	16.9 GB	Q8_0	Comfortable
140 tok/s 119–168	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	16.9 GB	Q8_0	Comfortable
140 tok/s 119–168	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	16.9 GB	Q8_0	Comfortable
134 tok/s 80–214 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	16.9 GB	Q8_0	Comfortable
120 tok/s 102–145	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.3 GB	Q3_K_M	Tight
119 tok/s 71–190 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	16.9 GB	Q8_0	Comfortable
119 tok/s 71–190 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	16.9 GB	Q8_0	Comfortable
119 tok/s 71–190 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	16.9 GB	Q8_0	Comfortable
113 tok/s 96–135	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
96.2 tok/s 82–115	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
96.2 tok/s 82–115	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	16.9 GB	Q8_0	Comfortable
96.2 tok/s 82–115	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
96.2 tok/s 82–115	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
96.2 tok/s 82–115	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
73.2 tok/s 44–117 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	16.9 GB	Q8_0	Comfortable
73.2 tok/s 44–117 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	16.9 GB	Q8_0	Comfortable
61.0 tok/s 37–98 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	16.9 GB	Q8_0	Comfortable
60.3 tok/s 51–72	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	10.0 GB	Q4_K_M	Tight
60.3 tok/s 51–72	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	10.0 GB	Q4_K_M	Tight
59.7 tok/s 36–96 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	16.9 GB	Q8_0	Comfortable
58.7 tok/s 50–70	CMP 90HX NVIDIA	10 GB	760 GB/s	Jul 2021	8.3 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: 29 April 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: 14.8B
Training data: 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: 3.2 × 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
Record confidence: Confident

Sources

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

Reference: Qwen3: Think Deeper, Act Faster
Last updated: 28 November 2025

The extremes

The ten fastest GPUs for Qwen3-14B

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 Qwen3-14B

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

P102-101

Memory needed

8.3 GB

Fastest

229 tok/s

Qwen3-14B is small enough at 14.8B parameters that hardware is rarely the obstacle — 306 of the cards we track can run it, including cards several years old.

The least hardware that works is a P102-101. Its 10 GB is enough at Q3_K_M compression, giving roughly 21.0 tokens per second.

Top of the range is the B200, at roughly 229 tokens per second thanks to 8,000 GB/s of bandwidth.

About this model

Qwen3-14B was published by Alibaba, in China, in April 2025. It comes out of industry.

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

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.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 21.4 tokens per second, and 266 of them clear the ten tokens per second that roughly matches reading speed.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

Because the architecture is recorded, the memory column is derived rather than estimated.

Training and provenance

Training it took roughly 3.2 × 10²⁴ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.

Step by step

How to choose a GPU for Qwen3-14B

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 Qwen3-14B actually needs — around 8.3 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

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason Qwen3-14B stops fitting a card that seemed fine.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of Qwen3-14B — 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

Ranking by tokens per second for Qwen3-14B follows memory bandwidth, not core counts, which is why the B200 tops it at 229 tok/s.
05

Read the fit column last

Tight means Qwen3-14B loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
06

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once Qwen3-14B is settled.

Answers

Qwen3-14B — common questions

How fast is Qwen3-14B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 229 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 266 of the cards that can run Qwen3-14B clear that.

How much VRAM does Qwen3-14B need?

About 8.3 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 Qwen3-14B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q4_K_M, using about 10.0 GB and generating roughly 60.3 tokens per second — a tight fit.

Can I run Qwen3-14B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q6_K, using about 13.4 GB and generating roughly 47.0 tokens per second — a tight fit.

Can I run Qwen3-14B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 16.9 GB and generating roughly 38.4 tokens per second — a comfortable fit.

Is Qwen3-14B open source?

Its weights are published, so Qwen3-14B 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 Qwen3-14B have?

Qwen3-14B has 14.8B parameters. Number of Parameters: 14.8B Number of Paramaters (Non-Embedding): 13.2B Number of Layers: 40 Number of Attention Heads (GQA): 40 for Q and 8 for KV 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 Qwen3-14B?

Qwen3-14B was published by Alibaba, based in China, categorised as industry.

When was Qwen3-14B released?

Qwen3-14B was published in April 2025.

What is Qwen3-14B used for?

Qwen3-14B works in Language, and is recorded as handling language modeling/generation, Question answering, Mathematical reasoning, Quantitative reasoning, Code generation, Translation. 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 Qwen3-14B?

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 Qwen3-14B?

Around 3.2 × 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 Qwen3-14B if it does not fit in my GPU?

It can be split between the card and system memory, but Qwen3-14B generates painfully slowly that way — the nearest miss we calculate is short by 2.8 GB. Nothing on this page assumes offloading.

Would two GPUs run Qwen3-14B faster?

A second card roughly doubles the memory available but not the generation rate. With 306 cards already able to run Qwen3-14B alone, the case for pairing is weak.

Why does the quantisation differ between cards for Qwen3-14B?

Each card is shown running the least-compressed copy it can hold, and Qwen3-14B appears at 5 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Qwen3-14B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 195–275 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 Qwen3-14B?

The smallest card in our catalogue that holds Qwen3-14B is the P102-101, with 10 GB of memory. It runs the model at Q3_K_M using about 8.3 GB, and produces roughly 21.0 tokens per second. 306 cards in total can run it.

Source

Original publication

Record last updated 28 November 2025

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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