LOCAL_AI_STACK
v2.2 · 2026-06-12
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VISION / MULTIMODAL MODEL — RUNTIME REQUIREMENTS
·Best treated as a light screenshot and image-description model, not the main advanced recommendation.
·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 2B is the lightest local entry. It handles basic screenshots, simple document questions, image descriptions, and OCR-like extraction, but accuracy and reasoning depth trail the larger variants on complex visual tasks and visual workflows.

Qwen3-VL 2B Instruct

qwen3-vl-2b-instruct
VISION-LANGUAGE

Qwen3-VL 2B Instruct is a 2B 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
2B
MODALITIES
text input, image input, multi-image input, video input in supported runtimes, text output
ARCHITECTURE
Dense transformer VLM with DeepStack vision fusion and text-timestamp video alignment.
CONTEXT WINDOW
262,144 tokens
TRAINING TOKENS
—
RELEASE DATE
2025-10-21
LICENSE & LINKS
GGUF REPOSITORIES
Official GGUF repository referenced by the June 2026 model research.
SETUP HINTS
OLLAMA
ollama run qwen3-vl:2b
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-2B-Instruct-GGUF) with current llama.cpp-compatible tooling.
QUANTIZATION FILE SIZES (GGUF)
Q4_K_M
1.11 GB
Q8_0
1.83 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
8 GB
MIN VRAM
3 GB
COMFORTABLE VRAM
8 GB
These are conservative local-inference estimates, not official hardware requirements.
HARDWARE FIT
CPU only (no GPU)
yes
8 GB RAM
comfortable
16 GB RAM
comfortable
32 GB RAM
comfortable
8 GB VRAM
comfortable
12 GB VRAM
comfortable
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
·Small laptops, quick screenshot checks, simple image descriptions, and lightweight private OCR-style experiments offline workflows.
AVOID IF
·Avoid if you need reliable document extraction, video reasoning, or complex spatial analysis locally today.
CAVEATS
·Best treated as a light screenshot and image-description model, not the main advanced recommendation.
·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 2B is the lightest local entry. It handles basic screenshots, simple document questions, image descriptions, and OCR-like extraction, but accuracy and reasoning depth trail the larger variants on complex visual tasks and visual workflows.
SOURCE URLS
FIELD EVIDENCE
trainingTokens—
LANGUAGES
Unknown
CAPABILITIES
vision / image input
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