Qwen 3.5 9B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated February 15, 2026

Model library → Qwen 3.5 → Qwen 3.5 9B

The standout of the Qwen 3.5 lineup. Scores 81.7% on GPQA Diamond — outperforming GPT-oss 120B on reasoning while fitting in 8 GB VRAM. Configurable thinking mode activates extended chain-of-thought for hard problems. Best local reasoning model under 10B parameters.

Qwen 3.5 9B 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

Parameters9 Billion
Context window256,000
ArchitectureDense Transformer (Gated DeltaNet)
ProviderAlibaba Cloud
LicenceApache 2.0
Specified atQ4_K_M
System RAM16 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_K3.0 GB3.8 GB~144 tok/s (est.)Fits comfortably
Q3_K_M3.8 GB4.6 GB~123 tok/s (est.)Fits comfortably
Q4_K_M5.4 GB6.2 GB~98 tok/s (est.)Fits comfortably
Q5_K_M6.4 GB7.2 GB~87 tok/s (est.)Fits comfortably
Q6_K7.4 GB8.2 GB~78 tok/s (est.)Fits comfortably
Q8_09.6 GB10.4 GB~64 tok/s (est.)Fits comfortably
F1618.0 GB18.8 GB~37 tok/s (est.)Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the Qwen 3.5 9B 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 Qwen 3.5 9B is the Intel Arc B570 (10 GB).

Affiliate disclosure: Some links on this page are affiliate links — if you buy through them, LLM Configurator may earn a commission at no extra cost to you. As an Amazon Associate, LLM Configurator earns from qualifying purchases.
Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
Check price on Amazon

How to Run Qwen 3.5 9B

Install Ollama, then run:

ollama run qwen3.5:9b

Weights on Hugging Face: Qwen/Qwen3.5-9B-Instruct.

Best for: reasoning, coding, analysis, general use.

Can I Run Qwen 3.5 9B on My GPU?

Other Qwen 3.5 Sizes

Qwen 3.5 9B — Frequently Asked Questions

How much VRAM does Qwen 3.5 9B 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 Qwen 3.5 9B run on an RTX 4090 (24 GB)?
Yes. Qwen 3.5 9B needs about 6 GB at Q4_K_M, inside a 24 GB card, at an estimated 98 tokens/sec.
How do I run Qwen 3.5 9B locally?
Install Ollama and run `ollama run qwen3.5:9b`. 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.

← All Qwen 3.5 models | VRAM calculator | Build a PC for this model | Check your own hardware