Alibaba Qwen · Released April 2025
Qwen3 14B
What it takes to run Qwen3 14B locally: memory by quantization, the GPUs that handle it, and how fast it runs.
Updated
- Parameters
- 14.8B
- Architecture
- Dense
- Context
- 32K tokens
- License
- Apache 2.0 license
- Memory at Q4_K_M
- ~11GB
What do you need to run Qwen3 14B?
Qwen3 14B needs about 11GB of VRAM at Q4_K_M with 8K context (9.0GB for the weights plus KV cache and overhead). The smallest GPU that can hold it is a 12GB card like the RTX 3060 12GB, with little memory to spare. For a good balance of price and speed we recommend the RTX 4060 Ti 16GB (~21 tokens/sec).
What to buy
GPUs for Qwen3 14B
Speeds are estimates at the quantization shown with 8K context. We may earn a commission when you buy through these links. Learn more.
Memory
Qwen3 14B VRAM requirements by quantization
Lower-bit quantizations shrink the model at some cost to quality. Q4_K_M is the usual sweet spot. Totals include the KV cache for the context length shown plus runtime overhead.
| Quantization | Quality | Weights | Total @ 8K ctx | Total @ 32K ctx |
|---|---|---|---|---|
| Q2_K | Rough | 5.6 GB | 7.7 GB | 12 GB |
| Q3_K_M | Usable | 7.2 GB | 9.4 GB | 13 GB |
| Q4_K_M | RecommendedDefault | 9.0 GB | 11 GB | 15 GB |
| Q5_K_M | Very good | 11 GB | 13 GB | 17 GB |
| Q6_K | Near-lossless | 12 GB | 15 GB | 19 GB |
| Q8_0 | Practically lossless | 16 GB | 18 GB | 22 GB |
| BF16 | Full precision | 30 GB | 32 GB | 36 GB |
KV cache note: 2 x 40 layers x 8 KV heads x 128 dims x 2 bytes = 163,840 bytes (160 KiB) per token: about 1.3GB at 8K and 5.4GB at the native 32K context.
Performance
How fast Qwen3 14B runs
Single-user generation speed on popular GPUs that fit the model entirely in memory, at the best quantization each can hold.
RTX 5090
Q8_0 · 32GB
~84tok/s
RTX PRO 6000
Q8_0 · 96GB
~84tok/s
RTX 5080
Q5_K_M · 16GB
~68tok/s
RTX PRO 5000 72GB
Q8_0 · 72GB
~64tok/s
RTX 5070 Ti
Q5_K_M · 16GB
~63tok/s
RTX 5070
Q4_K_M · 12GB
~56tok/s
RTX 4080 Super
Q5_K_M · 16GB
~53tok/s
RTX 4090
Q8_0 · 24GB
~49tok/s
RTX 4070 Ti Super
Q5_K_M · 16GB
~48tok/s
RTX 6000 Ada
Q8_0 · 48GB
~47tok/s
Measured
Qwen3 14B benchmarks
- RTX PRO 5000 72GBQ4_K_XL · llama.cpp99 tok/s Hardware Corner: RTX Pro 5000 Blackwell LLM Performance
- RTX 4060 Ti 16GBQ4_K_XL · llama.cpp27 tok/s Hardware Corner: RTX 4060 Ti 16GB LLM Performance
Overview
About Qwen3 14B
Qwen3 14B was the sweet spot of the Qwen3 dense lineup for 12GB and 16GB GPUs, with the same switchable thinking mode as its siblings. The Qwen3.5 generation dropped the 14B size: Qwen3.5 9B now does as well or better in less memory, and Gemma 4 26B A4B is the stronger option on 16GB cards. At Q4_K_M it needs about 11GB with 8K context, which is tight on a 12GB card and comfortable on 16GB. Native context is 32K, extendable to 128K with YaRN.
Strengths
- +Toggleable thinking mode with solid math and coding for its size
- +Fits 12GB GPUs at Q4_K_M and 16GB GPUs with room for longer context
- +Apache 2.0 license
Weaknesses
- −Superseded: there is no Qwen3.5 14B, and Qwen3.5 9B is as capable in less memory
- −Native context is only 32K tokens
- −Text only
Setup
How to run Qwen3 14B
$ ollama run qwen3:14b$ llama-server -hf Qwen/Qwen3-14B-GGUF:Q4_K_M -c 8192 -ngl 99$ mlx_lm.chat --model mlx-community/Qwen3-14B-4bitWeights: Qwen/Qwen3-14B · GGUF: Qwen/Qwen3-14B-GGUF
Alternatives
Models like Qwen3 14B
Alibaba Qwen
Qwen3.5 9B
9B · 256K ctx
Gemma 4 26B A4B
26B (3.8B active) · 256K ctx
Microsoft
Phi-4
14.7B · 16K ctx
OpenAI
gpt-oss-20b
20.9B (3.6B active) · 128K ctx
Frequently asked questions
How much VRAM do you need to run Qwen3 14B?
About 11GB at Q4_K_M with 8K tokens of context: 9.0GB of weights, 1.3GB of KV cache, and 0.9GB of runtime overhead. Higher-precision quants and longer contexts need more; see the table above.
What is the cheapest GPU that can run Qwen3 14B?
The smallest card in our database that can hold it is the RTX 3060 12GB (12GB), at Q4_K_M with little memory to spare, generating ~28 tokens/sec.
Is Qwen3 14B free for commercial use?
Qwen3 14B is released under the Apache 2.0 license. That is a permissive license, so commercial use is allowed.
How long a context does Qwen3 14B support?
Up to 32,768 tokens (32K). Long contexts need a lot of extra memory for the KV cache, so most local setups run 8K to 32K.
Sources: Qwen3 14B model card (Hugging Face), modelfit.io GGUF file sizes for Qwen3 14B. Capability score basis: Placed between the Llama 3.1 8B and Qwen3 32B anchors from its Artificial Analysis Intelligence Index v3 standing in reasoning mode (October 2025).