v2.2 · 2026-06-12

VISION / MULTIMODAL MODEL — RUNTIME REQUIREMENTS

·Very long context and image input can exceed the simple RAM estimates.

·Ollama MLX tags are text-only; vision support depends on the runtime and package.

·Beginner summary from supplied research: A small multimodal Qwen3.5 model that fits ordinary laptops and gives beginners local text, image, reasoning, and long-context features without jumping into workstation memory requirements.

Qwen3.5 4B

qwen3-5-4b

MULTIMODAL

Qwen3.5 4B is a 4B 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/Qwen3.5-4B

HUGGING FACE

Qwen/Qwen3.5-4B

SETUP HINTS

OLLAMA

ollama run qwen3.5:4b

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

3.4 GB

Q8_0

5.3 GB

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

RAM / VRAM ESTIMATES

MIN RAM

8 GB

COMFORTABLE RAM

16 GB

MIN VRAM

8 GB

COMFORTABLE VRAM

16 GB

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

HARDWARE FIT

CPU only (no GPU)

yes

8 GB RAM

limited

16 GB RAM

comfortable

32 GB RAM

comfortable

8 GB VRAM

limited

12 GB VRAM

limited

24 GB VRAM

comfortable

Apple Silicon (unified memory)

comfortable

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

·First local multimodal Qwen model on 8GB or 16GB machines.

AVOID IF

·You want the strongest coding or reasoning quality available locally.

CAVEATS

·Use Q4 on 8GB systems; Q8 is more comfortable on 16GB systems.

·Very long context and image input can exceed the simple RAM estimates.

·Ollama MLX tags are text-only; vision support depends on the runtime and package.

·Commercial-use status from the supplied research: permitted

SOURCE URLS

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

FIELD EVIDENCE