Models / Raon-1 mini preview 30B-A4B

Raon-1 mini preview 30B-A4B

MoE workstation
HuggingFace → HuggingBay → KRAFTON → Find for my hardware

Raon-1 mini is a model that thinks before it answers and answers in Korean as easily as English. Picture a careful analyst who drafts an outline first and speaks second, at 4B-of-weights speed per token. It exists because game company KRAFTON needed believable AI teammates and shipped the capability as open weights instead of keeping it inside the studio. Reach for it when the work is Korean-first assistants, agentic search, or one-RTX-5090 deployments. The DFlash2 draft model ships beside it, so speculative decoding works on day one. Honest status: preview weights (NVFP4, MLX, GGUF) under Apache-2.0, gated but auto-approved; benchmarks vendor-measured (AA-Omniscience 55.7, GPQA Diamond 84.3, Chatbot Korean 92.9).

korean agentic thinking local moe apple-silicon nvidia
Parameters
30.0B
Context
256k
License
apache 2.0
Developer
KRAFTON
Origin
🇰🇷 South Korea
Released
Oct 2026

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Run it locally

Per-quant memory needs and a static "can you run it?" reference - no rig entry required

NVFP4
16.0GB 17.0GB min 24.0GB rec
NVIDIA FP4 - Blackwell or Apple MLX, near-lossless
Q4_K_M
17.5GB 18.0GB min 24.0GB rec
Balanced - the usual local sweet spot

The reference hardware

Schematic of the NVIDIA Jetson Orin NX 16GB reference rig - 16GB unified memory, 102 GB/s aggregate bandwidth
Schematic of the Single GTX 1080 Ti (11GB) reference rig - 11GB VRAM, 484 GB/s aggregate bandwidth
Schematic of the 4x H100 80GB (320GB) reference rig - 320GB VRAM, 13400 GB/s aggregate bandwidth
Schematic of the NVIDIA DGX Station 748GB reference rig - 748GB unified memory, 8000 GB/s aggregate bandwidth
Schematic of the 4x RTX 5090 (128GB) reference rig - 128GB VRAM, 7168 GB/s aggregate bandwidth
Schematic of the 8x RTX 3090 rack (192GB) reference rig - 192GB VRAM, 7489 GB/s aggregate bandwidth
Schematic of the AMD Instinct MI300X (192GB) reference rig - 192GB VRAM, 5324 GB/s aggregate bandwidth
Schematic of the 4x RTX 4090 (96GB) reference rig - 96GB VRAM, 4032 GB/s aggregate bandwidth
Schematic of the 2x RTX 5090 (64GB) reference rig - 64GB VRAM, 3584 GB/s aggregate bandwidth
Schematic of the Single RTX 5090 (32GB) reference rig - 32GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the RTX PRO 6000 Blackwell (96GB) reference rig - 96GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the 2x RTX 3090 (48GB) reference rig - 48GB VRAM, 1872 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 192GB reference rig - 192GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 512GB reference rig - 512GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Single RTX 4090 (24GB) reference rig - 24GB VRAM, 1008 GB/s aggregate bandwidth
Schematic of the MacBook Pro M5 Max 128GB reference rig - 128GB unified memory, 614 GB/s aggregate bandwidth
Schematic of the Dual EPYC 9004 + 768GB DDR5-4800 reference rig - 768GB unified memory, 460 GB/s aggregate bandwidth
Schematic of the DGX Spark 128GB unified reference rig - 128GB unified memory, 273 GB/s aggregate bandwidth
Schematic of the Ryzen AI Max+ 395 128GB reference rig - 128GB unified memory, 256 GB/s aggregate bandwidth
Schematic of the Jetson AGX Orin 64GB reference rig - 64GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-3200 + 2x RTX 3090 reference rig - 560GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-2400 + 2x RTX 3090 reference rig - 560GB unified memory, 153 GB/s aggregate bandwidth

22 reference configs, drawn in-house. Scroll for more.

Can you run it? - reference rigs

Rig NVFP4 Q4_K_M
NVIDIA Jetson Orin NX 16GB no -> cloud no -> cloud
Single GTX 1080 Ti (11GB) no -> cloud offload
4x H100 80GB (320GB) no -> cloud fast 3158.6t/s
NVIDIA DGX Station 748GB no -> cloud fast 1885.7t/s
4x RTX 5090 (128GB) fast 1848.0t/s fast 1689.6t/s
8x RTX 3090 rack (192GB) no -> cloud fast 1765.4t/s
AMD Instinct MI300X (192GB) no -> cloud fast 1255.1t/s
4x RTX 4090 (96GB) no -> cloud fast 950.4t/s
2x RTX 5090 (64GB) fast 924.0t/s fast 844.8t/s
Single RTX 5090 (32GB) fast 462.0t/s fast 422.4t/s
RTX PRO 6000 Blackwell (96GB) fast 462.0t/s fast 422.4t/s
2x RTX 3090 (48GB) no -> cloud fast 441.4t/s
Mac Studio M4 Ultra 192GB fast 307.1t/s fast 280.8t/s
Mac Studio M4 Ultra 512GB fast 307.1t/s fast 280.8t/s
Single RTX 4090 (24GB) no -> cloud fast 237.6t/s
MacBook Pro M5 Max 128GB fast 172.7t/s fast 157.9t/s
Dual EPYC 9004 + 768GB DDR5-4800 no -> cloud fast 108.6t/s
DGX Spark 128GB unified fast 70.4t/s fast 64.4t/s
Ryzen AI Max+ 395 128GB no -> cloud fast 60.3t/s
Jetson AGX Orin 64GB no -> cloud fast 48.3t/s
Epyc + 512GB DDR4-3200 + 2x RTX 3090 no -> cloud fast 48.3t/s
Epyc + 512GB DDR4-2400 + 2x RTX 3090 no -> cloud fast 36.2t/s

Fit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast >=20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark.

Download options

NVFP4 official
16.0GB dl 17.0GB min 24.0GB rec
REC RAM vs largest quant
30B at NVFP4 is about 16 GB; one 32 GB RTX 5090 with KV + the DFlash2 drafter in headroom
GGUF on HF →
Q4_K_M official
17.5GB dl 18.0GB min 24.0GB rec
REC RAM vs largest quant
official GGUF (Krafton's llama.cpp branch until upstream merge); 7.6k downloads
GGUF on HF →

Or run it in the cloud

No per-token API provider pricing tracked for Raon-1 mini preview 30B-A4B yet. For flagship list prices, see the calculator.

PRICE HISTORY

Inference cost over time

Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.

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