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Qwen3 14B

Qwen3 14B needs roughly 11.6 GB VRAM at Q4_K_M quantization (34.7 GB at FP16). 104 GPUs we track can run it fully in VRAM at 8k context.

104 GPUs run this natively · 12 with CPU offload

Alibaba14.8B params128k contextApache 2.0Commercial use ok

Qwen3 14B is a 14.8B parameter dense model developed by Alibaba. April 2025 dense model from the Qwen3 line, released under Apache 2.0 with a unified thinking/non-thinking mode toggled via the chat template or an enable_thinking flag.

To run Qwen3 14B locally: Q5_K_M needs roughly 10-11GB and fits on a 12GB GPU with some headroom, or comfortably on 16GB. Thinking mode generates longer outputs, so budget extra KV cache for reasoning-heavy prompts.

MMLU-Pro 61.03 puts it well ahead of Qwen2.5-14B on general reasoning, reflecting Qwen3's larger 36-trillion-token pretraining corpus.

VRAM at each quantization

Qwen3 14B natively supports a longer context window, but the table below is capped at 8k for comparability; KV cache grows linearly with context length.

QuantWeightsKV cacheTotal
FP3259.2 GB1.34 GB67.8 GB
BF1629.6 GB1.34 GB34.7 GB
FP1629.6 GB1.34 GB34.7 GB
Q8_015.7 GB1.34 GB19.1 GB
Q6_K12.2 GB1.34 GB15.1 GB
Q5_K_Mrec10.5 GB1.34 GB13.3 GB
Q4_K_M9.0 GB1.34 GB11.6 GB
Q3_K_M7.1 GB1.34 GB9.5 GB
Q2_K5.6 GB1.34 GB7.8 GB
NVFP4cuda7.4 GB1.34 GB9.8 GB

KV cache figures assume 8k context at FP16. NVFP4 quantization requires a CUDA-capable GPU. Enable TurboQuant in the calculator to see reduced KV cache estimates.

Benchmarks

MMLU-Pro
61.0

GPUs that run Qwen3 14B natively (104)

Show 99 more
Plus 12 GPUs that run it with CPU offload (slower)

Notes

Supports thinking and non-thinking modes.

Hugging Face ↗Ollama ↗Released 2025-04-29

Continue reading

vram-guides9 min
Best LLMs for 16 GB VRAM GPUs (2026)
vram-guides8 min
Best LLMs for 12 GB VRAM (2026)
vram-guides10 min
Qwen Family Guide: 2.5 to 3.6
hardware9 min
Best GPUs Under $500 for LLMs (2026)

Frequently asked questions

What are the VRAM requirements for Qwen3 14B?
Qwen3 14B requires approximately 11.6 GB of VRAM at Q4_K_M quantization, 19.1 GB at Q8, and 34.7 GB at FP16. These numbers assume 8k context window; VRAM scales linearly with context length due to the KV cache.
How many parameters does Qwen3 14B have?
Qwen3 14B has 14.8 billion parameters.
How capable is Qwen3 14B?
With an MMLU-Pro score of 61.03, Qwen3 14B delivers solid general-purpose performance suitable for most everyday tasks and professional use.
Can Qwen3 14B run on a 16 GB GPU?
Yes. Qwen3 14B needs 11.6 GB at Q4_K_M, which fits in a 16 GB GPU like the RTX 4080 or RTX 5070 Ti.
What is the smallest quantization for Qwen3 14B that fits in 24 GB of VRAM?
At NVFP4, Qwen3 14B needs 9.8 GB, the highest-quality quantization that fits in 24 GB of VRAM.
What GPU do I need to run Qwen3 14B locally?
A 16 GB GPU is enough. At Q4_K_M, Qwen3 14B needs 11.6 GB VRAM. Good options: RTX 4080 (16 GB), RTX 5070 Ti (16 GB).