Qwen3-Embedding-8B vs Qwen3-VL-Embedding-2B

Qwen3-Embedding-8B

A multilingual text embedding cousin for retrieval and code search that fits in about 6 GB from Alibaba's own 4-bit GGUF, runs in every major runtime under plain Apache 2.0 and has several hosts. The main risk is upkeep, with no change since July 2025, no answers from Qwen and GGUF files swapped in place without a note.

Qwen3-VL-Embedding-2B

A small multimodal embedding cousin that puts text, images, screenshots and video into one vector space, in 4.3 GB of BF16 files that fit any 8 GB machine, under plain Apache 2.0. It loads with Sentence Transformers, vLLM or SGLang. The main gaps are no quantised build from Alibaba, few publisher replies and no safety material.

Qwen3-Embedding-8B

1. Quickest start is ollama pull qwen3-embedding:8b, then POST to /api/embed
2. With llama.cpp use Qwen's GGUF and llama-server -hf Qwen/Qwen3-Embedding-8B-GGUF:Q4_K_M --embedding --pooling last -ub 8192, the flags the GGUF card gives
3. Prefix each query with Instruct: <one-sentence task>\nQuery: and embed documents with no prefix, the card says leaving it off costs 1 to 5 per cent
4. Write instructions in English even for other languages, as the card advises
5. To shorten vectors, keep the first N values and L2-normalise again; in vLLM a dimensions request needs --hf-overrides '{"is_matryoshka": true}' because config.json doesn't declare it
6. Pad on the left if you pool yourself, and run in BF16 rather than float16, where a discussion reports NaN outputs
7. Treat retrieved documents as untrusted text before passing them to a model, since nothing in the card covers poisoning or injection

Qwen3-VL-Embedding-2B

1. Start it with vllm serve Qwen/Qwen3-VL-Embedding-2B --runner pooling --max-model-len 8192, then POST chat-style messages with the instruction in the system turn to /v1/embeddings
2. Or pip install -U sentence-transformers (5.4 or later) and SentenceTransformer('Qwen/Qwen3-VL-Embedding-2B').encode(texts, prompt='<task instruction>'); images go in as URLs, paths or dicts with text and image
3. Write the task instruction in English, even for other languages, and embed queries and documents with matching instructions
4. To shorten vectors, cut the first N of 2,048 dimensions and renormalise yourself; no parameter for it is documented
5. Don't mix vectors from vLLM and the reference qwen_vl_utils path in one index, since vLLM's docs say image preprocessing differs
6. Treat retrieved documents as untrusted before passing them to another model