KRAFTON Raon-1 mini local LLM: a Korean 30B MoE on one RTX 5090 32GB

Raon-1 mini is a 30B-A4B MoE language model that KRAFTON AI designed and trained from scratch. The 30B is the total parameter count and A4B means about 4B active per token. The model card describes strong performance in Korean conversation and agentic tasks. It is trained in-house rather than fine-tuned from another open model.

Its training languages are Korean, English, Japanese, Chinese, Spanish, French and Thai. The model card describes strong Korean and solid English. For Korean users, Korean appearing among the training languages matters by itself. How much training weight each language received is not published, so verify Korean quality directly.

This model matters for Korean users because it is a Korean-built model with Korean in its training data. If you are looking for a Korean local LLM⁵, a single RTX 5090 with 32GB is enough to try it first.

Of its 25 layers, 18 use Raon Delta Attention⁶ and 7 use MLA. The linear-attention RDA layers hold a fixed-size state, so the KV cache⁷ does not keep growing as input lengthens. Only the 7 MLA layers keep a cache, and the model card states roughly 8 KiB per token in BF16⁸.

For long-context work this makes memory use easier to predict. It only helps if the runtime⁹ supports this attention layout, though. Check when llama.cpp, Ollama or vLLM added support before choosing a file.

This model is not downloadable straight from the Hugging Face repository. You fill in your name, affiliation, job title, country and intended use, and agree to the licence. After approval you can fetch the model files and the GGUF¹⁰ or MLX¹¹ conversions. Approval is not automatic, so allow time for the first attempt.

Once you have access, pick the format that matches your use. Target a single RTX 5090 and the NVFP4 file is the one the model card points to; prefer compatibility and simpler tooling and GGUF is the choice. MXFP8 is also offered. Each format needs different memory and runtime support, so the same model does not mean the same running conditions.

The model card states that this release is not final and that specifications and results may change before the finished version. The benchmark tables are preview figures too. If a final release under the same name appears in a few weeks, scores and recommended hardware may differ, so record the version and measurement date before putting it into work.

The comparison table places Raon-1 mini beside Qwen3.6-35B-A3B, Gemma 4 26B-A4B, Qwen3.8-27B and Muse Glimmer 30B. Sharing a table does not prove the measurement environments match, so verify that separately. Some knowledge benchmarks are lower than rivals, so read them alongside the reasoning, instruction-following and agentic results the release highlights.

What makes this model interesting for Korean users is that a model with Korean in its training data can be tried on RTX 5090-class hardware. For work where Korean input is the point, such as meeting summaries, internal document triage or Korean customer replies, it is worth comparing against the Qwen or Gemma models you already use on identical sentences.

Conversely, if your work is only English code writing or long English documents, there is little reason to pick this model first. Switch only when a comparison on the same memory shows an advantage over a model you have already validated. Keep a preview model as a candidate for testing rather than a work baseline.

Raon-1 mini is released under the Apache 2.0 licence. Commercial use, modification and redistribution are allowed with no revenue threshold. The access gate is a distribution method, not a narrower licence. Apache 2.0 does require keeping copyright notices and marking changes, so retain the licence and NOTICE when embedding it in a product.