Can I run bge-m3?

bge-m3 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.

Showing the 1 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 bge-m3 locally

1. 1.Install Ollama for Windows, macOS or Linux.
2. 2.Download this exact build in a terminal: ollama pull bge-m3:567m
3. 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 bge-m3

How much VRAM do I need to run bge-m3?

At 4K context, the smallest published build of bge-m3 (F16) needs roughly 1.6 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 bge-m3 without a dedicated GPU?

A supported CPU runtime can use system RAM. The smallest listed build needs an estimated 1.6 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 bge-m3 download?

The smallest published build is 1.1 GB. Leave some extra free disk space beyond the download itself.

How do I run bge-m3 locally?

Install Ollama, then download a specific build with "ollama pull bge-m3:567m". This is an embedding model: use the embedding API with a supported client, not an interactive chat prompt.

Compare with similar models

Or start from your memory: 8 GB VRAM, 12 GB VRAM, 16 GB VRAM, 24 GB VRAM or a 16 GB Mac.