v2.2 · 2026-06-12
VISION / MULTIMODAL MODEL — RUNTIME REQUIREMENTS
·Place this in an advanced multimodal retrieval section, not the main beginner model catalog.
·Beginner summary from supplied research: Qwen3-VL Embedding 2B is for visual retrieval, not normal chat. It embeds text, images, screenshots, video, or mixed inputs into one search space, making it useful for PDF images and visual archives in advanced RAG.
Qwen3-VL Embedding 2B
qwen3-vl-embedding-2b
Qwen3-VL Embedding 2B is a 2B embedding 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
FAMILY
Qwen3-VL Embedding
MODEL TYPE
embedding
PARAMETERS
2B
MODALITIES
text, image, screenshot, video, mixed-modal input
ARCHITECTURE
—
CONTEXT WINDOW
32,768 tokens
TRAINING TOKENS
—
RELEASE DATE
2026-01-08
HARDWARE FIT
CPU only (no GPU)
no
8 GB RAM
unknown
16 GB RAM
unknown
32 GB RAM
unknown
8 GB VRAM
unknown
12 GB VRAM
unknown
24 GB VRAM
unknown
Apple Silicon (unified memory)
unknown
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
·Searching screenshots, image-heavy PDFs, product photos, videos, and mixed text-image collections locally with GPUs available.
AVOID IF
·You only index plain text, or you need simple Ollama-first beginner setup today at home.
CAVEATS
·Place this in an advanced multimodal retrieval section, not the main beginner model catalog.
·No official Ollama command, LM Studio availability, GGUF source, or TEI compatibility was verified.
·Qwen's quantization support language appears to refer to embedding deployment and vector output behavior, not necessarily downloadable GGUF model-weight quantizations.
·The 8B variant should be treated as workstation or server-class unless local tooling improves.
·Commercial-use status from the supplied research: Yes under Apache-2.0.
·Beginner summary from supplied research: Qwen3-VL Embedding 2B is for visual retrieval, not normal chat. It embeds text, images, screenshots, video, or mixed inputs into one search space, making it useful for PDF images and visual archives in advanced RAG.
SOURCE URLS
FIELD EVIDENCE
parameterSizeBhuggingface.co/Qwen/Qwen3-VL-Embedding-2B
activeParametersBhuggingface.co/Qwen/Qwen3-VL-Embedding-2B
architecturehuggingface.co/Qwen/Qwen3-VL-Embedding-2B
contextWindowTokenshuggingface.co/Qwen/Qwen3-VL-Embedding-2B
releaseDategithub.com/QwenLM/Qwen3-VL-Embedding
hfGgufRepos—
q4FileSizeGb—
q5FileSizeGb—
q8FileSizeGb—
ollama—
setupHints—
supportsToolshuggingface.co/Qwen/Qwen3-VL-Embedding-2B
reasoningTunedhuggingface.co/Qwen/Qwen3-VL-Embedding-2B
embeddingModelhuggingface.co/Qwen/Qwen3-VL-Embedding-2B
visionModelhuggingface.co/Qwen/Qwen3-VL-Embedding-2B
trainingTokens—
LANGUAGES
30+ languages
CAPABILITIES
embedding modelvision / image input