Qwen 2.5 14B Instruct — VRAM, Speed & Local Setup

作者： Jakub Rusinowski · 最后更新： 2024年9月18日

Model library → Qwen 2.5 Family → Qwen 2.5 14B Instruct

The 'Goldilocks' model. Fits on a 12GB GPU (RTX 3060/4070) with good quantization and hits well above its weight class.

Qwen 2.5 14B Instruct needs about 9 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.

Specifications

Parameters14 Billion
Context window128,000
ArchitectureDense Transformer
ProviderAlibaba Cloud
LicenceApache 2.0
Specified atQ4_K_M
System RAM24 GB
Record updated2024-09-18

Licence

Apache-2.0 — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K4.6 GB7.0 GB~106 tok/s (est.)Fits comfortably
Q3_K_M6.0 GB8.4 GB~89 tok/s (est.)Fits comfortably
Q4_K_M8.5 GB10.9 GB~69 tok/s (est.)Fits comfortably
Q5_K_M9.9 GB12.3 GB~61 tok/s (est.)Fits comfortably
Q6_K11.5 GB13.9 GB~54 tok/s (est.)Fits comfortably
Q8_014.9 GB17.3 GB~44 tok/s (est.)Fits comfortably
F1628.0 GB30.4 GB~4 tok/s (est.)Offloads to system RAM (slow)

Want the memory numbers alone, at every quantization level and your own context length? Use the Qwen 2.5 14B Instruct VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

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

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

Recommended GPU

The cheapest catalogued GPU that runs Qwen 2.5 14B Instruct is the Intel Arc B570 (10 GB).

联盟营销声明: 本页部分链接为联盟推广链接——如果你通过它们购买，LLM Configurator 可能会获得佣金，而你无需支付任何额外费用。作为亚马逊联盟成员（Amazon Associate），LLM Configurator 会从符合条件的购买中获得收益。
Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026年价格波动较大——请以当前商品页价格为准。
在亚马逊查看价格

How to Run Qwen 2.5 14B Instruct

Install Ollama, then run:

ollama run qwen2.5:14b

Weights on Hugging Face: Qwen/Qwen2.5-14B-Instruct.

Best for: code, reasoning, rag.

Can I Run Qwen 2.5 14B Instruct on My GPU?

Other Qwen 2.5 Family Sizes

Qwen 2.5 14B Instruct — Frequently Asked Questions

How much VRAM does Qwen 2.5 14B Instruct need?
About 9 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 2.5 14B Instruct run on an RTX 4090 (24 GB)?
Yes. Qwen 2.5 14B Instruct needs about 9 GB at Q4_K_M, inside a 24 GB card, at an estimated 69 tokens/sec.
How do I run Qwen 2.5 14B Instruct locally?
Install Ollama and run `ollama run qwen2.5:14b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Qwen 2.5 Family come in?
Qwen 2.5 Coder 32B (20 GB), Qwen 2.5 14B Instruct (9 GB), Qwen 2.5 7B Instruct (5 GB), Qwen 2.5 72B Instruct (44 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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