Ollama Library · bert
Can I run snowflake-arctic-embed?
A suite of text embedding models by Snowflake, optimized for performance.
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 contextsnowflake-arctic-embed 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 |
|---|---|---|---|
| snowflake-arctic-embed:22m22M parameters (tag)Maximum context unavailableBuild sourceCatalog checked 2026-10-13 | F16 | 0.0 GB | ~0.6 GB |
| snowflake-arctic-embed:33m33M parameters (tag)Maximum context unavailableBuild sourceCatalog checked 2026-10-13 | F16 | 0.1 GB | ~0.6 GB |
| snowflake-arctic-embed:110m110M parameters (tag)Maximum context unavailableBuild sourceCatalog checked 2026-10-13 | F16 | 0.2 GB | ~0.7 GB |
| snowflake-arctic-embed:137m137M parameters (tag)2K documented maximum contextThe displayed memory scenario exceeds this build's supported context.Build sourceCatalog checked 2026-10-13 | F16 | 0.3 GB | ~0.9 GB |
| snowflake-arctic-embed:335m335M parameters (tag)Maximum context unavailableBuild sourceCatalog checked 2026-10-13 | F16 | 0.6 GB | ~1.1 GB |
Showing the 5 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 snowflake-arctic-embed locally
- 1.Install Ollama for Windows, macOS or Linux.
- 2.Download this exact build in a terminal:
ollama pull snowflake-arctic-embed:22m - 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 snowflake-arctic-embed
- How much VRAM do I need to run snowflake-arctic-embed?
- At 4K context, the smallest published build of snowflake-arctic-embed (F16) needs roughly 0.6 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 snowflake-arctic-embed without a dedicated GPU?
- A supported CPU runtime can use system RAM. The smallest listed build needs an estimated 0.6 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 snowflake-arctic-embed download?
- The smallest published build is 0.0 GB. There are 5 builds in total, the largest being 0.6 GB. Leave some extra free disk space beyond the download itself.
- How do I run snowflake-arctic-embed locally?
- Install Ollama, then download a specific build with "ollama pull snowflake-arctic-embed:22m". This is an embedding model: use the embedding API with a supported client, not an interactive chat prompt.
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Or start from your memory: 8 GB VRAM, 12 GB VRAM, 16 GB VRAM, 24 GB VRAM or a 16 GB Mac.
Model data from the official Ollama registry · verified 13/10/2026