Skip to page content
SAVRN
Search Plan a project

Open-weight model · Text generation

Qwen3-32B

by Qwen Qwen/Qwen3-32B

Qwen3-32B is an open-weight model for text generation from Qwen, released under Apache License 2.0. It has 32.8B parameters and a 40,960-token context. At 16-bit it needs about 78.6 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 3.2M downloads a month.

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

Parameters32.8B
Context40,960
Weights65.5 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads3.2M

Runs On

What it takes to serve Qwen3-32B (32.8B 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 65.5 GB 78.6 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 32.8 GB 39.3 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 16.4 GB 19.7 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 Oct 16, 2026.

Qwen3-32B on every accelerator the SAVRN Index prices, at every precision

SAVRN's Notes on Qwen3-32B

Qwen3 32B is a dense model with a switch: it can think step by step for hard problems or answer directly for simple ones. With about 5 million downloads a month, it is one of the most used large open models in our catalog.

It needs about 79 GB at 16-bit, one large-memory accelerator, and about 20 GB at 4-bit. Apache 2.0 allows commercial use. Its 40,960-token context is shorter than newer Qwen releases; if you need long documents, look at Qwen3.5 27B.

SAVRN Research, 2026-09-18

Model Card

By Qwen, published under apache-2.0, revision 9216db5781bf.

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
64
Hidden size
5,120
Feed-forward size
25,600
Attention heads
64
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/Qwen3-32B
Publisher
Qwen
Task
Text generation
Modality
Text
Library
transformers
Parameters
32.8B parameters
Languages
Not stated by the source
Revision
9216db5781bf21249d130ec9da846c4624c16137
First published
2025-04-27
Last updated
2025-07-26

Files and Weights

27 files, 65.5 GB in total. The weights are 17 files totalling 65.5 GB in safetensors.

Weights17 files · 65.5 GB
Configuration3 files · 59.3 KB
Tokenizer4 files · 15.9 MB
Documentation2 files · 28.0 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00017.safetensorsWeights4.0 GB 52562b2ff97b
model-00002-of-00017.safetensorsWeights3.9 GB e26764b2c687
model-00003-of-00017.safetensorsWeights3.9 GB 6c5ba7bed9c5
model-00004-of-00017.safetensorsWeights3.9 GB f736f6ac4d8c
model-00005-of-00017.safetensorsWeights3.9 GB a52ed375c083
model-00006-of-00017.safetensorsWeights3.9 GB 37fae28990b0
model-00007-of-00017.safetensorsWeights3.9 GB 37776006aeab
model-00008-of-00017.safetensorsWeights3.9 GB 73e74e912967
model-00009-of-00017.safetensorsWeights3.9 GB a044b3602a01
model-00010-of-00017.safetensorsWeights3.9 GB 9966612ba7ec
model-00011-of-00017.safetensorsWeights3.9 GB e2a058a0ac7d
model-00012-of-00017.safetensorsWeights3.9 GB 58a1aa89093f
model-00013-of-00017.safetensorsWeights3.9 GB 35f3381bab31
model-00014-of-00017.safetensorsWeights3.9 GB 8713b062ddc1
model-00015-of-00017.safetensorsWeights3.9 GB bec439d23931
model-00016-of-00017.safetensorsWeights3.9 GB e569139fadd6
model-00017-of-00017.safetensorsWeights3.1 GB 1f47c318fcd7
config.jsonConfiguration728 B —
generation_config.jsonConfiguration239 B —
model.safetensors.index.jsonConfiguration58.3 KB —
LICENSEDocumentation11.3 KB —
README.mdDocumentation16.6 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
65.5 GB
Download from Qwen

Released by Qwen through ModelScope. Read the license.

Built From

Evaluations

Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.

BenchmarkConditionsResultReported byRevisionDate
LEXam-Benchmark/LEXam Task mcq_4_choicesMetric mcq_4_choicesComparison conditions not established 45.3 LEXam Leaderboard
Reported by a third party		 Evaluated revision not stated 2026-06-02
LEXam-Benchmark/LEXam Task open_questionMetric open_questionComparison conditions not established 40 LEXam Leaderboard
Reported by a third party		 Evaluated revision not stated 2026-06-02

Memory Requirements

PrecisionWeights in memory
As published65.5 GB
16-bit65.5 GB
8-bit32.8 GB
4-bit16.4 GB

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

Hosted Prices

HostInput / outputUnitObserved
DeepInfra$0.08 / $0.28input / output, per million tokensOct 16, 2026
Nscale$0.08 / $0.25input / output, per million tokensOct 16, 2026

From the SAVRN Index.

Built on This Model

Compare Qwen3-32B

Questions About Qwen3-32B

How much GPU memory does Qwen3-32B need?

About 78.6 GB at 16-bit and 19.7 GB at 4-bit: the weights (32.8B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Qwen3-32B 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 Qwen3-32B commercially?

Yes. Qwen3-32B 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 Qwen3-32B's context length?

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

Similar Models

Model · Text generation

Qwen3-32B-AWQ

Qwen

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: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and…

Open weights apache-2.0 32.8B parameters 40,960 tokens transformers
View model

Model · Text generation

Qwen3-32B-MXFP4-W4A4

Cezar

MXFP4 W4A4 quantization of (revision 9216db5781bf21249d130ec9da846c4624c16137, BF16). It was made for a controlled NVFP4-vs-MXFP4 decode benchmark on NVIDIA B200 (fp4bench), not as a general-purpose release: the NVFP4 and MXFP4 checkpoints share the model, the tool, the recipe, the calibration data and the layer coverage, and differ only in the format. Recipe: QuantizationModifier(targets="Linear", scheme="MXFP4", ignore=["lmhead"]). The plain preset recipe, with no weight-rounding optimization (GPTQ, AutoRound and the like). anon8231489123/ShareGPTVicunaunfiltered at revision 192ab2185289094fc556ec8ce5ce1e8e587154ca, at most 1024 tokens each (33208 tokens in all, seed 3). MXFP4 has no…

Open weights apache-2.0 32.8B parameters 40,960 tokens
View model