Qwen 3.5 4B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated February 15, 2026

Model library → Qwen 3.5 → Qwen 3.5 4B

Strong 4B dense model with native multimodal vision. Fits on any 6 GB VRAM GPU. Supports 201 languages and configurable thinking mode for deeper reasoning tasks.

Qwen 3.5 4B needs about 3 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

Parameters4 Billion
Context window256,000
ArchitectureDense Transformer (Gated DeltaNet)
ProviderAlibaba Cloud
LicenceApache 2.0
Specified atQ4_K_M
System RAM8 GB
Record updated2026-02-15

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.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K1.3 GB2.1 GB~218 tok/s (est.)Fits comfortably
Q3_K_M1.7 GB2.5 GB~196 tok/s (est.)Fits comfortably
Q4_K_M2.4 GB3.2 GB~166 tok/s (est.)Fits comfortably
Q5_K_M2.8 GB3.6 GB~152 tok/s (est.)Fits comfortably
Q6_K3.3 GB4.1 GB~139 tok/s (est.)Fits comfortably
Q8_04.3 GB5.0 GB~118 tok/s (est.)Fits comfortably
F168.0 GB8.8 GB~74 tok/s (est.)Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the Qwen 3.5 4B 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)

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Recommended GPU

The cheapest catalogued GPU that runs Qwen 3.5 4B is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
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How to Run Qwen 3.5 4B

Install Ollama, then run:

ollama run qwen3.5:4b

Weights on Hugging Face: Qwen/Qwen3.5-4B-Instruct.

Best for: laptop, general use, multilingual, vision.

Can I Run Qwen 3.5 4B on My GPU?

Other Qwen 3.5 Sizes

Qwen 3.5 4B — Frequently Asked Questions

How much VRAM does Qwen 3.5 4B need?
About 3 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 3.5 4B run on an RTX 4090 (24 GB)?
Yes. Qwen 3.5 4B needs about 3 GB at Q4_K_M, inside a 24 GB card, at an estimated 166 tokens/sec.
How do I run Qwen 3.5 4B locally?
Install Ollama and run `ollama run qwen3.5:4b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Qwen 3.5 come in?
Qwen 3.5 0.8B (1 GB), Qwen 3.5 2B (2 GB), Qwen 3.5 4B (3 GB), Qwen 3.5 9B (6 GB), Qwen 3.5 27B (17 GB), Qwen 3.5 35B-A3B (22 GB), Qwen 3.5 122B-A10B (74 GB), Qwen 3.5 397B-A17B (240 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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