Raon-1-mini

[!NOTE] This repository contains the NVFP4 weights of Raon-1 mini preview for vLLM.

The weights are quantized to NVFP4 and run on a single GPU.

Raon-1 mini is a 30B-A4B Mixture-of-Experts (MoE) language model, designed and trained from scratch by KRAFTON AI. It demonstrates strong capabilities in Korean conversation and agentic tasks. As part of the Raon open model family, it is released under the Apache 2.0 license.

[!TIP] Want to try Raon-1 mini without setting anything up? Chat with it for free, for a limited time, in the Raon-1 mini playground.

[!NOTE] This is a preview release, not a final model. Specs and results may change before the final version.

Highlights

• Reasoning: Built-in thinking, with leading results on the math and science benchmarks shown below.
• Instruction following and search: Leads the comparison models on the instruction-following and search benchmarks shown below.
• Korean and English: Strong Korean language ability, and solid performance in English.
• Agent execution: Tool calling for agentic workflows, with support for Hermes Agent, Pi and OpenCode.
• Speculative decoding support: A DFlash2 draft model is provided for accelerated speculative decoding.
• Quantization options: Available in MXFP8 and NVFP4 formats, with NVFP4 enabling inference on a single NVIDIA RTX 5090 gpu (32 GB VRAM).

Model Overview

Raon-1 mini uses a hybrid Mixture-of-Experts (MoE) architecture, consisting of 18 linear attention layers and 7 full attention layers.

• Raon Delta Attention (RDA): Raon Delta Attention decouples the erase and write gates of Kimi Delta Attention, which improves long-context performance over KDA.
• Raon-mHC: Raon-mHC reduces the computational cost of DeepSeek’s manifold-constrained hyper-connections (mHC), which speeds up training.
• Hybrid layout: 18 of the 25 layers are Raon Delta Attention layers with a fixed-size state. Only the 7 MLA layers keep a KV cache, about 8 KiB per token in BF16.
• Long context: Natively supports up to 262,144 tokens of context.
• Sparse MoE: 6 of 128 routed experts plus 1 shared expert are active per token, so each token uses 4.3B of the 28.8B total parameters.

Benchmark Results

• Red indicates the best performance of Raon-1 mini preview among MoE baselines.

Judge and user simulator: gpt-5.6-luna for all evaluations. Red = Raon-1 mini preview is best within the MoE group (Raon-1 mini preview, Qwen3.6-35B-A3B, Gemma 4 26B-A4B). Dense models (Qwen3.8-27B, Muse Glimmer 30B) are shown for reference only. ¹ SWE-Verified: evaluated with the mini-SWE-agent harness. ² GDPVal (dataset): metric is a judge score, not Elo. ³ AutomationBench: public split, not the official evaluation set. ⁴ GDP.pdf: official Artificial Analysis (artificialanalysis.ai) text-only version. PDFs are OCR-ed in advance; no image input. ⁵ Chatbot Korean (internal): an internal benchmark measuring Korean colloquial ability in chatbot conversations across diverse situations. Not publicly released.

Sampling parameters

[!Important] Raon-1 mini is designed for thinking mode; disabling it is not recommended.

Settings shared by all tasks:

Settings that depend on the task:

Speculative Decoding

We release a DFlash2 draft model alongside Raon-1 mini preview to accelerate inference through speculative decoding. In our benchmarks on a single NVIDIA B300, it delivers up to 2.18× higher output throughput than autoregressive decoding. Speedups vary by hardware and workload.

Throughput is measured in output tok/s, with speedup over autoregressive decoding in parentheses. Acceptance length (AL) is the mean number of tokens per verification step, averaged across requests at concurrency 1.

AR: autoregressive decoding; C: concurrency. Throughput is total output tokens divided by end-to-end wall time. Evaluated with vLLM 0.28.0 and an MXFP8 target model; block size 8 (7 draft tokens), probabilistic draft sampling, te