Reflection AI's Beam: 501B Open Weights Incoming

Rows of GPU server racks in a data center, glowing blue

A new open-weight giant just showed up. On October 5, Reflection AI announced Beam, its first open-weight model: a sparse mixture-of-experts transformer with 501 billion total parameters and 23 billion active per token. It is text-only, built for coding, reasoning, and agentic work, and it carries a 1-million-token context window. The weights land under an Apache 2.0 license later this month, after final red-teaming. For now, early access is a waitlist.

The headline number that matters is not 23 billion. It is 501 billion. In a mixture-of-experts model only a fraction of the parameters fire on each token, but all of them have to sit in memory. Rule of thumb: expect roughly 500 GB at 8-bit precision, or about half that with a 4-bit community quantization. That is multi-GPU server territory, not a home workstation.

So what should you actually do? Do not plan a build around weights that are not public yet. Set up your evaluation set and serving path now with a model you can already download. Then, when Beam's weights land, you can test it on your own data the same day. The community will almost certainly produce quantized builds that shrink the footprint, but those take weeks to appear after release, and quality varies.

If you are sizing a machine today, start with what fits the card you already own. This VRAM calculator shows which open models run on your hardware, at which quantization levels, before you spend a dollar.

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