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Open-weight model · Text generation

Qwen3-14B

by Qwen Qwen/Qwen3-14B

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.

Parameters14.8B
Context40,960
Weights29.5 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads1.7M

Runs On

What it takes to serve Qwen3-14B (14.8B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 29.5 GB 35.4 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 14.8 GB 17.7 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 7.4 GB 8.9 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

SAVRN's Notes on Qwen3-14B

Two hosts on the SAVRN Index sell Qwen3-14B by the token: Nscale at $0.07 in and $0.20 out per million, DeepInfra at $0.12 and $0.24. Running it yourself starts with memory. At 4-bit the weights are 7.4 GB with 8.9 GB needed; at 16-bit, 29.5 GB and 35.4 GB. Both fit the cheapest setup the Index prices, one MI300X with 192 GB at $1.85 an hour on-demand. The 14.8B parameters switch between thinking and non-thinking modes in one set of weights.

Apache 2.0 permits commercial use, modification and redistribution with notices kept, so fine-tuned copies can ship. It is derived from Qwen/Qwen3-14B-Base, the starting point for further training. Hold designs to the 40,960-token context; the YaRN paper, arXiv:2309.00071, sits among the papers describing it, so ask what length was validated. No evaluations are reported, so run your own before weighing the $1.85 card against the per-token hosts.

Model Card

By Qwen, published under apache-2.0, revision 40c069824f42.

Qwen3 Highlights

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:

Read the full model card (1,884 words)

Configuration

Architecture
Qwen3ForCausalLM
Context length (tokens)
40,960
Layers
40
Hidden size
5,120
Feed-forward size
17,408
Attention heads
40
Key/value heads
8
Head dimension
128
Vocabulary size
151,936
RoPE base
1,000,000
Stored precision
bfloat16
Model type
qwen3

Identity and Version

Repository
Qwen/Qwen3-14B
Publisher
Qwen
Task
Text generation
Modality
Text
Library
transformers
Parameters
14.8B parameters
Languages
Not stated by the source
Revision
40c069824f4251a91eefaf281ebe4c544efd3e18
First published
2025-04-27
Last updated
2025-07-26

Files and Weights

18 files, 29.6 GB in total. The weights are 8 files totalling 29.5 GB in safetensors.

Weights8 files · 29.5 GB
Configuration3 files · 37.5 KB
Tokenizer4 files · 15.9 MB
Documentation2 files · 28.0 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00008.safetensorsWeights3.8 GB e942bdbdf088
model-00002-of-00008.safetensorsWeights4.0 GB f7c9c6eee628
model-00003-of-00008.safetensorsWeights4.0 GB dfb8c5df9404
model-00004-of-00008.safetensorsWeights4.0 GB eab286fec759
model-00005-of-00008.safetensorsWeights4.0 GB 97f0dc2992e5
model-00006-of-00008.safetensorsWeights4.0 GB 9e8e76a013cd
model-00007-of-00008.safetensorsWeights4.0 GB 0aee70ee6e91
model-00008-of-00008.safetensorsWeights1.9 GB 0d6b92296e32
config.jsonConfiguration728 B
generation_config.jsonConfiguration239 B
model.safetensors.index.jsonConfiguration36.5 KB
LICENSEDocumentation11.3 KB
README.mdDocumentation16.7 KB
.gitattributesRepository1.6 KB
merges.txtTokenizer1.7 MB
tokenizer.jsonTokenizer11.4 MB aeb13307a71a
tokenizer_config.jsonTokenizer9.7 KB
vocab.jsonTokenizer2.8 MB

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
29.5 GB
Download from Qwen

Released by Qwen through ModelScope. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published29.5 GB
16-bit29.5 GB
8-bit14.8 GB
4-bit7.4 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Hosted Prices

HostInput / outputUnitObserved
DeepInfra$0.12 / $0.24input / output, per million tokensSep 18, 2026
Nscale$0.07 / $0.20input / output, per million tokensSep 18, 2026

From the SAVRN Index.

Built on This Model

Compare Qwen3-14B

Questions About Qwen3-14B

How much GPU memory does Qwen3-14B need?

About 35.4 GB at 16-bit and 8.9 GB at 4-bit: the weights (14.8B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Qwen3-14B on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use Qwen3-14B commercially?

Yes. Qwen3-14B is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

What is Qwen3-14B's context length?

40,960 tokens, from the maximum position embeddings in its published configuration.

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