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

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 5 lutego 2026

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

Parameters8 Billion
Context window128,000
ArchitectureDense
ProviderAlibaba Cloud
LicenceApache 2.0
Specified atQ4_K_M
System RAM16 GB
Record updated2026-02-05

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). Weights plus framework overhead only — this model publishes no architecture we can read, so no KV cache is included. A real session needs more; the figure is a floor, not a target. 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.

QuantBits/weightWeightsVRAM neededEst. speedFit on 24 GB
Q2_K2.632.6 GB3.4 GB~154 tok/s (est.)Fits comfortably
Q3_K_M3.413.4 GB4.2 GB~133 tok/s (est.)Fits comfortably
Q4_K_M4.834.8 GB5.6 GB~107 tok/s (est.)Fits comfortably
Q5_K_M5.675.7 GB6.5 GB~95 tok/s (est.)Fits comfortably
Q6_K6.566.6 GB7.4 GB~86 tok/s (est.)Fits comfortably
Q8_08.508.5 GB9.3 GB~70 tok/s (est.)Fits comfortably
F1616.0016 GB16.8 GB~41 tok/s (est.)Fits comfortably

Want to set your own context length and KV-cache quantization? Use the interactive 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 Qwen3-Coder 8B is the Intel Arc B570 (10 GB).

Ujawnienie afiliacyjne: Niektóre odnośniki na tej stronie to linki afiliacyjne — jeśli dokonasz zakupu za ich pośrednictwem, LLM Configurator może otrzymać prowizję bez dodatkowych kosztów dla Ciebie. Jako uczestnik programu Amazon Associates, LLM Configurator zarabia na kwalifikujących się zakupach.
Intel Arc B570 10GB
10 GB VRAM · 150 W board power
Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.
Sprawdź cenę na Amazon

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 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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