Alibaba Cloud14B~10 GB VRAM at Q4_K_M

Qwen 3 14B — VRAM, speed & local setup

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Written by Jakub Rusinowski · Last updated April 28, 2025

A powerhouse 14B model with hybrid thinking. Beats Qwen 2.5 72B in reasoning benchmarks despite being 5x smaller. Perfect for RTX 4070/4080 users who want frontier-level reasoning locally.

Qwen 3 14B needs about 10 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Logic94

Creative84

Coding93

VRAM and speed by quantization

Quoted against NVIDIA RTX 4090 (24 GB). Includes the KV cache at 8K context, so it reads higher than the headline figure.

Quant	VRAM	Speed (est.)	Fit
Q2_K 2.63 bpw	7 GB	~104 tok/s	Fits
Q3_K_M 3.41 bpw	8.5 GB	~87 tok/s	Fits
Q4_K_M 4.83 bpw	11.1 GB	~67 tok/s	Fits
Q5_K_M 5.67 bpw	12.6 GB	~59 tok/s	Fits
Q6_K 6.56 bpw	14.3 GB	~52 tok/s	Fits
Q8_0 8.50 bpw	17.9 GB	~42 tok/s	Fits
F16 16.00 bpw	31.7 GB	~4 tok/s	Offload

Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. Qwen 3 14B VRAM calculator →

Get Qwen 3 14B running

The cheapest catalogued GPU that runs Qwen 3 14B is the Intel Arc B580 (12 GB).

Buy this hardware Intel Arc B580 12GB — 12 GB VRAM · 190 W board power Deploy in the cloud now RTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Intel Arc B580 12GB

12 GB VRAM · 190 W board power

2026 prices are volatile — check the current listing.

Check price on Amazon

How to run Qwen 3 14B

Install Ollama, then run:

$ollama run qwen3:14b

Weights on Hugging Face: Qwen/Qwen3-14B ↗

Specifications

Curated — A hand-written entry from before this catalogue recorded its sources. The figures are long-standing but their provenance is not on file.

Parameters: 14 Billion
Context window: 128,000
Architecture: Dense
Provider: Alibaba Cloud
Licence: Apache 2.0
Practical deployment: Laptop
Specified at: Q4_K_M
System RAM: 24 GB
Record updated: 2025-04-28

LicenceApache-2.0Commercial use permitted

Commercial use permitted. No usage restrictions beyond attribution.

Quality and use cases

Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.

Best forreasoningcodingmathrag

Can I run Qwen 3 14B on my GPU?

Other Qwen 3 sizes

Qwen 3 14B — frequently asked questions

How much VRAM does Qwen 3 14B need?

About 10 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does Qwen 3 14B run on an RTX 4090 (24 GB)?

Yes. Qwen 3 14B needs about 10 GB at Q4_K_M, inside a 24 GB card, at an estimated 67 tokens/sec.

How do I run Qwen 3 14B locally?

Install Ollama and run `ollama run qwen3:14b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Qwen 3 come in?

Qwen 3 8B (6 GB), Qwen 3 14B (10 GB), Qwen 3 32B (21 GB), Qwen 3 30B-A3B (MoE) (19 GB), Qwen 3 235B-A22B (MoE) (143 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.