Qwen 3.6 27B — VRAM, Speed & Local Setup

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

作者： Jakub Rusinowski · 最后更新： 2026年4月22日

Dense successor to Qwen 3.5 27B with the same hybrid Gated DeltaNet/attention architecture (64 layers) but improved coding benchmarks. Fits a single RTX 3090/4090 at Q4_K_M (~16.8 GB). Apache 2.0, ~256K context extensible to ~1M.

Qwen 3.6 27B needs about 18 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	27.8 Billion
Context window	262,144
Architecture	Dense Transformer (Gated DeltaNet hybrid)
Provider	Alibaba Cloud
Licence	Apache 2.0
Specified at	Q4_K_M
System RAM	32 GB
Record updated	2026-04-22

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	9.1 GB	9.9 GB	~65 tok/s (est.)	Fits comfortably
Q3_K_M	11.8 GB	12.6 GB	~52 tok/s (est.)	Fits comfortably
Q4_K_M	16.8 GB	17.6 GB	~39 tok/s (est.)	Fits comfortably
Q5_K_M	19.7 GB	20.5 GB	~34 tok/s (est.)	Fits comfortably
Q6_K	22.8 GB	23.6 GB	~30 tok/s (est.)	Tight fit
Q8_0	29.5 GB	30.3 GB	~4 tok/s (est.)	Offloads to system RAM (slow)
F16	55.6 GB	56.4 GB	—	Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Qwen 3.6 27B VRAM calculator.

Buy This HardwareAMD Radeon RX 7900 XT 20GB — 20 GB VRAM · 315 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)

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Recommended GPU

The cheapest catalogued GPU that runs Qwen 3.6 27B is the AMD Radeon RX 7900 XT (20 GB).

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AMD Radeon RX 7900 XT 20GB

20 GB VRAM · 315 W board power

2026年价格波动较大——请以当前商品页价格为准。

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How to Run Qwen 3.6 27B

Install Ollama, then run:

ollama run qwen3.6:27b

Weights on Hugging Face: Qwen/Qwen3.6-27B.

Best for: reasoning, coding, multilingual, long documents.

Can I Run Qwen 3.6 27B on My GPU?

Other Qwen 3.6 Sizes

Qwen 3.6 35B-A3B — about 22 GB VRAM at Q4_K_M

Qwen 3.6 27B — Frequently Asked Questions

How much VRAM does Qwen 3.6 27B need?

About 18 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 3.6 27B run on an RTX 4090 (24 GB)?

Yes. Qwen 3.6 27B needs about 18 GB at Q4_K_M, inside a 24 GB card, at an estimated 39 tokens/sec.

How do I run Qwen 3.6 27B locally?

Install Ollama and run `ollama run qwen3.6:27b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Qwen 3.6 come in?

Qwen 3.6 27B (18 GB), Qwen 3.6 35B-A3B (22 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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