Qwen 3.5 0.8B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated February 15, 2026

Model library → Qwen 3.5 → Qwen 3.5 0.8B

Ultra-compact dense model fitting in 1 GB VRAM. Ideal for embedded devices, IoT, and mobile applications requiring on-device reasoning with minimal memory footprint.

Qwen 3.5 0.8B needs about 1 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

Parameters0.8 Billion
Context window256,000
ArchitectureDense Transformer (Gated DeltaNet)
ProviderAlibaba Cloud
LicenceApache 2.0
Specified atQ4_K_M
System RAM4 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_K0.3 GB1.1 GB~339 tok/s (est.)Fits comfortably
Q3_K_M0.3 GB1.1 GB~328 tok/s (est.)Fits comfortably
Q4_K_M0.5 GB1.3 GB~309 tok/s (est.)Fits comfortably
Q5_K_M0.6 GB1.4 GB~298 tok/s (est.)Fits comfortably
Q6_K0.7 GB1.5 GB~288 tok/s (est.)Fits comfortably
Q8_00.9 GB1.7 GB~268 tok/s (est.)Fits comfortably
F161.6 GB2.4 GB~212 tok/s (est.)Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the Qwen 3.5 0.8B 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 0.8B 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 0.8B

Install Ollama, then run:

ollama run qwen3.5:0.8b

Weights on Hugging Face: Qwen/Qwen3.5-0.8B-Instruct.

Best for: mobile, edge devices, embedded, low memory.

Can I Run Qwen 3.5 0.8B on My GPU?

Other Qwen 3.5 Sizes

Qwen 3.5 0.8B — Frequently Asked Questions

How much VRAM does Qwen 3.5 0.8B need?
About 1 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 0.8B run on an RTX 4090 (24 GB)?
Yes. Qwen 3.5 0.8B needs about 1 GB at Q4_K_M, inside a 24 GB card, at an estimated 309 tokens/sec.
How do I run Qwen 3.5 0.8B locally?
Install Ollama and run `ollama run qwen3.5:0.8b`. 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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