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Open-weight model · Text generation

Qwen3-8B

by Qwen Qwen/Qwen3-8B

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

Parameters8.2B
Context40,960
Weights16.4 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads13M

Runs On

What it takes to serve Qwen3-8B (8.2B 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 16.4 GB 19.7 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 8.2 GB 9.8 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 4.1 GB 4.9 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 Qwen3-8B

At 16-bit the weights are 16.4 GB and the model needs 19.7 GB, which fits one MI300X with 192 GB, the Index's cheapest setup at $1.85 an hour on-demand. At 8-bit the need drops to 9.8 GB and at 4-bit to 4.9 GB, so the question is never which card but how many copies to stack on one. With 8.2 billion parameters, a 40,960-token context and a switch between thinking and non-thinking modes, this is a text model for everyday work.

The license is the easy part: Apache 2.0 permits commercial use, modification and redistribution, with notices and any NOTICE file kept and significant changes stated, plus an express patent grant. Two things to weigh: it derives from Qwen3-8B-Base, so decide whether you want this checkpoint or the base, and compare the hourly card against the one Index host price, Nscale at $0.07 in and $0.18 out per million tokens.

Model Card

By Qwen, published under apache-2.0, revision b968826d9c46.

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
36
Hidden size
4,096
Feed-forward size
12,288
Attention heads
32
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/Qwen3-8B
Publisher
Qwen
Task
Text generation
Modality
Text
Library
transformers
Parameters
8.2B parameters
Languages
Not stated by the source
Revision
b968826d9c46dd6066d109eabc6255188de91218
First published
2025-04-27
Last updated
2025-07-26

Files and Weights

15 files, 16.4 GB in total. The weights are 5 files totalling 16.4 GB in safetensors.

Weights5 files · 16.4 GB
Configuration3 files · 33.8 KB
Tokenizer4 files · 15.9 MB
Documentation2 files · 28.0 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00005.safetensorsWeights4.0 GB 31d6a825ae35
model-00002-of-00005.safetensorsWeights4.0 GB 5991236cea6f
model-00003-of-00005.safetensorsWeights4.0 GB c5185c4794be
model-00004-of-00005.safetensorsWeights3.2 GB b5ee7de71fbf
model-00005-of-00005.safetensorsWeights1.2 GB 20c2d6366ab8
config.jsonConfiguration728 B
generation_config.jsonConfiguration239 B
model.safetensors.index.jsonConfiguration32.9 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
16.4 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
LiquidAI/ifstruct-v1.0 Task ifstruct_v1Metric ifstruct_v1Comparison conditions not established 79.75 Liquid AI — IFStruct v1.0 blog (Qwen3-8B)
Reported by a third party
Evaluated revision not stated 2026-06-30

Memory Requirements

PrecisionWeights in memory
As published16.4 GB
16-bit16.4 GB
8-bit8.2 GB
4-bit4.1 GB

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

Hosted Prices

HostInput / outputUnitObserved
Nscale$0.07 / $0.18input / output, per million tokensSep 18, 2026

From the SAVRN Index.

Built on This Model

Compare Qwen3-8B

Questions About Qwen3-8B

How much GPU memory does Qwen3-8B need?

About 19.7 GB at 16-bit and 4.9 GB at 4-bit: the weights (8.2B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Qwen3-8B 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 Qwen3-8B commercially?

Yes. Qwen3-8B 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 Qwen3-8B's context length?

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

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