Alibaba Cloud · qwen3
Can I run qwen3-embedding?
Building upon the foundational models of the Qwen3 series, Qwen3 Embedding provides a comprehensive range of text embeddings models in various sizes
Parameter counts and memory belong to each build below. A family can contain several sizes and tasks; fitting in memory is not a quality ranking.
Calculate memory for a build and contextqwen3-embedding system requirements
Memory needed at 4K context, counting model weights, KV cache, compute buffers and runtime overhead. Download sizes come from the official Ollama registry.
| Build | Quantization | Download | Memory needed |
|---|---|---|---|
| qwen3-embedding:0.6b600M parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q8_0 | 0.6 GB | ~1.5 GB |
| qwen3-embedding:0.6b-fp16600M parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | F16 | 1.1 GB | ~2.1 GB |
| qwen3-embedding:4b4B parameters (tag)40K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 2.3 GB | ~3.5 GB |
| qwen3-embedding:4b-q8_04B parameters (tag)40K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q8_0 | 4.0 GB | ~5.3 GB |
| qwen3-embedding:8b8B parameters (tag)40K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 4.4 GB | ~5.7 GB |
| qwen3-embedding:8b-q8_08B parameters (tag)40K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q8_0 | 7.5 GB | ~9.0 GB |
| qwen3-embedding:4b-fp164B parameters (tag)40K documented maximum contextBuild sourceCatalog checked 2026-10-13 | F16 | 7.5 GB | ~9.0 GB |
| qwen3-embedding:8b-fp168B parameters (tag)40K documented maximum contextBuild sourceCatalog checked 2026-10-13 | F16 | 14.1 GB | ~16.2 GB |
Showing the 8 smallest non-alias builds. Choose any build and change the context. A context above the build's documented maximum is a hypothetical calculation, not a supported setting.
How to run qwen3-embedding locally
- 1.Install Ollama for Windows, macOS or Linux.
- 2.Download this exact build in a terminal:
ollama pull qwen3-embedding:0.6b - 3.Use the Ollama embedding API with this tag. Embedding vectors are used for search and retrieval; this is not a chat model.
Frequently asked questions about qwen3-embedding
- How much VRAM do I need to run qwen3-embedding?
- At 4K context, the smallest published build of qwen3-embedding (Q8_0) needs roughly 1.5 GB of GPU memory once weights, KV cache, compute buffers and runtime overhead are counted. Bigger quantizations and longer context windows need more. It can also run partly or entirely on the CPU using system RAM, more slowly.
- Can I run qwen3-embedding without a dedicated GPU?
- A supported CPU runtime can use system RAM. The smallest listed build needs an estimated 1.5 GB at 4K context, plus headroom for your operating system. Check the build's runtime requirements. We have not measured its speed on your CPU.
- How big is the qwen3-embedding download?
- The smallest published build is 0.6 GB. There are 8 builds in total, the largest being 14.1 GB. Leave some extra free disk space beyond the download itself.
- How do I run qwen3-embedding locally?
- Install Ollama, then download a specific build with "ollama pull qwen3-embedding:0.6b". This is an embedding model: use the embedding API with a supported client, not an interactive chat prompt.
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Model data from the official Ollama registry · verified 13/10/2026