Alibaba Cloud9B~6 GB VRAM at Q4_K_M

Qwen 3.5 9B — VRAM, speed & local setup

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

Written by Jakub Rusinowski · Last updated February 15, 2026

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.

Logic87

Creative82

Coding85

VRAM and speed by quantization

Quoted against NVIDIA RTX 4090 (24 GB). Includes the KV cache at 8K context, so it reads higher than the headline figure.

Quant	VRAM	Speed (est.)	Fit
Q2_K 2.63 bpw	4.1 GB	~156 tok/s	Fits
Q3_K_M 3.41 bpw	5 GB	~132 tok/s	Fits
Q4_K_M 4.83 bpw	6.6 GB	~103 tok/s	Fits
Q5_K_M 5.67 bpw	7.5 GB	~91 tok/s	Fits
Q6_K 6.56 bpw	8.5 GB	~82 tok/s	Fits
Q8_0 8.50 bpw	10.7 GB	~66 tok/s	Fits
F16 16.00 bpw	19.1 GB	~38 tok/s	Fits

Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. Qwen 3.5 9B VRAM calculator →

Get Qwen 3.5 9B running

The cheapest catalogued GPU that runs Qwen 3.5 9B is the Intel Arc B570 (10 GB).

Buy this hardware Intel Arc B570 10GB — 10 GB VRAM · 150 W board power Deploy in the cloud now RTX 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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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 ↗

Specifications

Curated — A hand-written entry from before this catalogue recorded its sources. The figures are long-standing but their provenance is not on file.

Parameters: 9 Billion
Context window: 256,000
Architecture: Dense Transformer (Gated DeltaNet)
Provider: Alibaba Cloud
Licence: Apache 2.0
Practical deployment: Laptop
Specified at: Q4_K_M
System RAM: 16 GB
Record updated: 2026-02-15

LicenceApache-2.0Commercial use permitted

Commercial use permitted. No usage restrictions beyond attribution.

Quality and use cases

Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.

Best forreasoningcodinganalysisgeneral 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 103 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.