LOCAL_AI_STACK
v2.2 · 2026-06-12
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VISION / MULTIMODAL MODEL — RUNTIME REQUIREMENTS
·Ollama lists text and image input; video workflows may require Transformers, vLLM, qwen-vl-utils, or sampled frames.
·Beginner summary from supplied research: Qwen3-VL 4B is a better small-machine choice than 2B. It keeps low memory demands while improving screenshot reading, document questions, image reasoning, and OCR-like extraction for everyday local workflows and private testing on laptops locally.

Qwen3-VL 4B Instruct

qwen3-vl-4b-instruct
VISION-LANGUAGE

Qwen3-VL 4B Instruct is a 4.44B vision language 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
Qwen / Alibaba Cloud
FAMILY
Qwen3-VL
MODEL TYPE
vision-language
PARAMETERS
4.44B
MODALITIES
text input, image input, multi-image input, video input in supported runtimes, text output
ARCHITECTURE
Dense transformer VLM with Interleaved-MRoPE, DeepStack vision fusion, and text-timestamp video alignment.
CONTEXT WINDOW
262,144 tokens
TRAINING TOKENS
—
RELEASE DATE
2025-10-15
LICENSE & LINKS
GGUF REPOSITORIES
Official GGUF repository referenced by the June 2026 model research.
SETUP HINTS
OLLAMA
ollama run qwen3-vl: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 the cited GGUF repository (Qwen/Qwen3-VL-4B-Instruct-GGUF) with current llama.cpp-compatible tooling.
QUANTIZATION FILE SIZES (GGUF)
Q4_K_M
2.5 GB
Q8_0
4.28 GB
Quantization note: Q4_K_M for first local attempts unless a cited runtime recommends a different default.
RAM / VRAM ESTIMATES
MIN RAM
3 GB
COMFORTABLE RAM
16 GB
MIN VRAM
3 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
·Budget laptops, classroom documents, UI screenshots, basic visual reasoning, and small private agent experiments locally.
AVOID IF
·Avoid if you want the strongest OCR, long video analysis, or workstation-level visual reasoning today.
CAVEATS
·Good small-model choice, but 8B should be the default recommendation when the machine can run it.
·Ollama lists text and image input; video workflows may require Transformers, vLLM, qwen-vl-utils, or sampled frames.
·GGUF sizes are for Q4_K_M and Q8_0; a Q5 size was not found in the cited sources.
·Use dedicated OCR or human review for legal, financial, or high-stakes extraction.
·Commercial-use status from the supplied research: Permitted under Apache-2.0 license, subject to license terms.
·Beginner summary from supplied research: Qwen3-VL 4B is a better small-machine choice than 2B. It keeps low memory demands while improving screenshot reading, document questions, image reasoning, and OCR-like extraction for everyday local workflows and private testing on laptops locally.
SOURCE URLS
FIELD EVIDENCE
trainingTokens—
LANGUAGES
Unknown
CAPABILITIES
vision / image input
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