v2.2 · 2026-06-12

VISION / MULTIMODAL MODEL — RUNTIME REQUIREMENTS

·Beginner summary from supplied research: The best beginner default in the Qwen3.5 line: still laptop-realistic, but more capable than the 4B model for multilingual chat, reasoning, coding help, and image-aware local workflows.

Qwen3.5 9B

qwen3-5-9b

MULTIMODAL

Qwen3.5 9B is a 9B multimodal model profile for local AI planning. This page records provider, license, context, quantization, hardware-fit estimates, setup hints, source links, and caveats so readers do not choose a model by name alone.

PROVIDER

Alibaba Qwen Team

FAMILY

Qwen3.5

MODEL TYPE

multimodal

PARAMETERS

MODALITIES

text, image

ARCHITECTURE

Dense hybrid Gated DeltaNet / FFN / Gated Attention model with vision encoder

CONTEXT WINDOW

262,144 tokens

TRAINING TOKENS

—

RELEASE DATE

2026-03-02

LICENSE & LINKS

Apache-2.0license text

MODEL CARD

huggingface.co/Qwen/Qwen3.5-9B

HUGGING FACE

Qwen/Qwen3.5-9B

SETUP HINTS

OLLAMA

ollama run qwen3.5:9b

LM STUDIO

Available according to the supplied June 2026 research; select a cited GGUF/MLX build and verify current app metadata before relying on hands-on setup guidance.

LLAMA.CPP

Use a cited GGUF build with current llama.cpp-compatible tooling.

QUANTIZATION FILE SIZES (GGUF)

Q4_K_M

6.6 GB

Q8_0

11 GB

Quantization note: Q4_K_M for first local attempts unless a cited runtime recommends a different default.

RAM / VRAM ESTIMATES

MIN RAM

16 GB

COMFORTABLE RAM

32 GB

MIN VRAM

16 GB

COMFORTABLE VRAM

32 GB

These are conservative local-inference estimates, not official hardware requirements.

HARDWARE FIT

CPU only (no GPU)

8 GB RAM

16 GB RAM

limited

32 GB RAM

comfortable

8 GB VRAM

12 GB VRAM

24 GB VRAM

limited

Apple Silicon (unified memory)

limited

Hardware fit values are conservative local-inference estimates based on GGUF size plus runtime and KV-cache overhead. Actual requirements depend on context length, quantization, runtime, GPU offload, and other running apps.

BEST FOR

·General-purpose local Qwen on 16GB or 32GB systems.

AVOID IF

·You only have 8GB RAM or need very fast responses.

CAVEATS

·Q4 is the practical beginner quantization; Q8 needs more memory headroom.

·The 256K context limit is theoretical for many local setups.

·Vision and thinking mode increase latency and memory use.

·Commercial-use status from the supplied research: permitted

SOURCE URLS

[source 1] [source 2] [source 3] [source 4]

FIELD EVIDENCE