Qwen3-Coder 8B — VRAM, Speed & Local Setup

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

作者： Jakub Rusinowski · 最后更新： 2026年2月5日

Model library → Qwen3-Coder → Qwen3-Coder 8B

Compact coding model that punches well above its weight class. Strong at Python, JavaScript, TypeScript, Go, and Rust.

Qwen3-Coder 8B needs about 6 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

Parameters	8 Billion
Context window	128,000
Architecture	Dense
Provider	Alibaba Cloud
Licence	Apache 2.0
Specified at	Q4_K_M
System RAM	16 GB
Record updated	2026-02-05

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), with no KV cache (this record has no published architecture). 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.

Quant	Weights	VRAM needed	Est. speed	Fit on 24 GB
Q2_K	2.6 GB	3.4 GB	~154 tok/s (est.)	Fits comfortably
Q3_K_M	3.4 GB	4.2 GB	~133 tok/s (est.)	Fits comfortably
Q4_K_M	4.8 GB	5.6 GB	~107 tok/s (est.)	Fits comfortably
Q5_K_M	5.7 GB	6.5 GB	~95 tok/s (est.)	Fits comfortably
Q6_K	6.6 GB	7.4 GB	~86 tok/s (est.)	Fits comfortably
Q8_0	8.5 GB	9.3 GB	~70 tok/s (est.)	Fits comfortably
F16	16.0 GB	16.8 GB	~41 tok/s (est.)	Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the Qwen3-Coder 8B VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board power Deploy 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 Qwen3-Coder 8B 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 Qwen3-Coder 8B

Install Ollama, then run:

ollama run qwen3-coder:8b

Weights on Hugging Face: Qwen/Qwen3-Coder-8B-Instruct.

Best for: coding, debugging, code review.

Can I Run Qwen3-Coder 8B on My GPU?

Other Qwen3-Coder Sizes

Qwen3-Coder 80B-A3B (MoE) — about 49 GB VRAM at Q4_K_M
Qwen3-Coder 480B-A35B (MoE) — about 291 GB VRAM at Q4_K_M

Qwen3-Coder 8B — Frequently Asked Questions

How much VRAM does Qwen3-Coder 8B need?

About 6 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 Qwen3-Coder 8B run on an RTX 4090 (24 GB)?

Yes. Qwen3-Coder 8B needs about 6 GB at Q4_K_M, inside a 24 GB card, at an estimated 107 tokens/sec.

How do I run Qwen3-Coder 8B locally?

Install Ollama and run `ollama run qwen3-coder:8b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Qwen3-Coder come in?

Qwen3-Coder 8B (6 GB), Qwen3-Coder 80B-A3B (MoE) (49 GB), Qwen3-Coder 480B-A35B (MoE) (291 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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