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Can the NVIDIA GeForce RTX 4090 run Raon-1 mini Preview (30B-A4B)?

83Great quality (A) · 83 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q4_K_M it needs about 19.4 GB of 24.0 GB usable, with an estimated speed of 146 tok/s.

Best quant
Q4_K_M
Generation speed
146 tok/s
Prompt processing
13,831 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Best app to run it on the NVIDIA GeForce RTX 4090

On Windows

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/KRAFTON/Raon-1-mini-preview

On Linux

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/KRAFTON/Raon-1-mini-preview

Want an agent? Raon-1 mini Preview (30B-A4B) supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Raon-1 mini Preview (30B-A4B)

Original weights · Hugging Face

Full-precision files from KRAFTON, for fine-tuning, vLLM, Transformers.

huggingface.co/KRAFTON/Raon-1-mini-preview (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from KRAFTON. The weights themselves download from Hugging Face.

github.com/krafton-ai/Raon-1-mini-preview (opens in a new tab)

KRAFTON's official repo. KRAFTON also publishes GGUF files (KRAFTON/Raon-1-mini-preview-gguf), but for now they need KRAFTON's llama.cpp fork (krafton-ai/llama.cpp) because upstream llama.cpp support is still pending, so LM Studio and Ollama cannot load them yet. An official MLX version is at KRAFTON/Raon-1-mini-preview-mlx.

git clone https://github.com/krafton-ai/Raon-1-mini-preview

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 83Great quality (A) · 83 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great
How well it runs on the NVIDIA GeForce RTX 4090: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.37.6 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score92.6 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the NVIDIA GeForce RTX 4090

Quant Memory needed Available Speed Runs Quality Download
Q2_K 13.4 GB 24.0 GB 191 tok/s Runs great 61Good quality (B) · 61 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 16.0 GB 24.0 GB 169 tok/s Runs great 73Great quality (A) · 73 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 19.4 GB 24.0 GB 146 tok/s Runs great 83Great quality (A) · 83 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 22.3 GB 24.0 GB 130 tok/s Runs welltight fit 85Excellent quality (A+) · 85 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 25.5 GB 24.0 GB 76.9 tok/s Runs welloffloads to RAM 87Excellent quality (A+) · 87 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 32.4 GB 24.0 GB 27.0 tok/s Runs welloffloads to RAM 88Excellent quality (A+) · 88 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 59.4 GB 24.0 GB — Won't run 88Excellent quality (A+) · 88 of 100: how capable the model is at F16, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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