Qwen3-14B

Updated

Tools

Thinking

Reasoning

Code

Multilingual

Run

Run Qwen3-14B locally with Atomic Chat — strong reasoning and coding, fully offline and private. No API keys, no limits. Download free.

huggingface-cli download Qwen/Qwen3-14B

from transformers import AutoModel
model = AutoModel.from_pretrained("Qwen/Qwen3-14B")

More models

View all

Name	Size / Usage	Context	Input
Qwen3.6-35B-A3B		256K	Text, Image
Qwen3.6-27B		256K	Text, Image
Qwen3-8B	Reasoning, coding, agentic chat	128K	Text
Qwen2.5-7B-Instruct	General chat, coding, reasoning	128K	Text
Qwen2.5-32B-Instruct	General chat, coding, reasoning	128K	Text
Qwen2.5-14B-Instruct	General chat, coding, reasoning	128K	Text
Qwen2.5-Coder-7B-Instruct	Code generation, code reasoning	128K	Text
Qwen2.5-Coder-32B-Instruct	Code generation, fixing, agents	128K	Text
Qwen3-235B-A22B	Reasoning, coding, agentic tasks	128K	Text
Qwen3-30B-A3B-Instruct-2507	General chat, reasoning, agents	256K	Text
Qwen2.5-72B-Instruct	General chat, coding, reasoning	128K	Text
Qwen3-4B-Thinking-2507	Reasoning, math, coding	256K	Text
WebWorld-8B	Web agents, multimodal reasoning	40K	Text, Image
MiniCPM-V 4.6	5213 GB	421K	Text, Image
anima	0.4B	31K	Text
Qwen3-Coder-30B-A3B-Instruct		256K	Text
Qwen3-30B-A3B		128K	Text
Qwen3-32B		128K	Text

At a glance

License: Apache 2.0
Context length: 128K tokens
Languages: Multilingual
Minimum hardware: ~30 GB VRAM
Strengths: reasoning, coding and multilingual chat

Overview

Qwen3-14B is a dense large language model from Qwen, the AI team at Alibaba.

License: Apache 2.0
Context length: 128K tokens
Languages: Multilingual
Minimum hardware: ~30 GB VRAM
Strengths: reasoning, coding and multilingual chat

Highlights

Seamless thinking switch — one model toggles between thinking and fast non-thinking modes.
Stronger reasoning — surpasses QwQ (thinking) and Qwen2.5-instruct (non-thinking) on math, code, and logic.
Human-preference alignment — excels at creative writing, role-play, and multi-turn dialogue.
Agentic + multilingual — precise tool integration and support for 100+ languages.

What it is good at

Qwen3-14B can switch between a thinking mode for harder problems and a faster direct-answer mode for ordinary chat. That split shapes where it does well.

Reasoning and math — thinking mode produces step-by-step chains for logic puzzles, multi-step math, and problems where a single-pass answer tends to slip.
Code — it writes and debugs across common languages and follows multi-file instructions, useful for local coding help that never leaves your laptop.
Tool use and multilingual work — it can call external tools and functions for agent-style tasks, and it handles over 100 languages for translation and instruction following.

Running it locally

At 14.8B parameters, Qwen3-14B fits on a single mid-range GPU once quantized. A Q4_K_M build needs around 10 to 11 GB of VRAM with an 8K context, which lands it on 12 GB cards like the RTX 4070; a Q8 build sits near 18 GB for quality closer to full precision. The 128K context window costs more memory the larger the prompt you feed it, so KV-cache headroom matters for long documents.

huggingface-cli download Qwen/Qwen3-14B

From there you can load the weights with Transformers or serve them through vLLM, or skip the setup and open the model directly in Atomic Chat with one click.

License

Qwen3-14B ships under the Apache-2.0 license. That allows commercial use, modification, and redistribution without a fee, including bundling the model into your own products, as long as you keep the license and attribution notices.

Desktop

macOS

(M1 or better)

Download

Windows

(x64)

Download

Linux

(x86_64)

Download

Frequently asked questions

Qwen3-14B is a 14.8B-parameter dense language model from Qwen (Alibaba's AI team), part of the Qwen3 series released in 2025. It handles reasoning, math, code, tool calling, and over 100 languages, and it can toggle between a thinking mode for hard problems and a faster mode for plain chat. In Atomic Chat it runs locally on your own hardware.

A Q4_K_M quantized build needs roughly 10 to 11 GB of VRAM at an 8K context, so a 12 GB GPU like the RTX 4070 handles it comfortably. A Q8 build sits near 18 GB for quality close to full precision, while unquantized FP16 needs about 35 GB. Longer contexts up to the 128K limit raise memory use, so leave headroom for the KV cache.

Yes. Qwen3-14B is released under the Apache-2.0 license, so the weights are free to download and use with no licensing fee. The license also permits modification, redistribution, and commercial use, provided you keep the original license and attribution notices.

Yes. Once the weights are downloaded through Atomic Chat, the model runs fully on-device and needs no internet connection. Every prompt is processed locally, so your text stays on your machine and nothing is sent to an external server.

It is a strong fit for local reasoning and math through its thinking mode, for writing and debugging code, and for agent-style tasks that call external tools and functions. Its multilingual coverage of more than 100 languages also makes it useful for private translation and instruction following without a cloud service.