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Can the OnePlus 13 (24 GB RAM) run Raon-1 mini Preview (30B-A4B)?

73Great quality (A) · 73 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (tight fit)

Runs well (tight fit). At Q3_K_M it needs about 14.8 GB of 15.6 GB usable, with an estimated speed of 21.6 tok/s.

Best quant
Q3_K_M
Generation speed
21.6 tok/s
Prompt processing
54.5 tok/s
First token (1K prompt)
18.8 s
Largest safe context
108K

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

On a phone, tablet or other mobile device you install a chat app and get the model file through it, so there are no commands to copy.

On Android

No mobile app in the Play Store runs Raon-1 mini Preview (30B-A4B) right now: mobile apps need a GGUF version, and none is published. Mobile apps · models that run on mobile devices.

Files and commands for a computer

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 6 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 73Great quality (A) · 73 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_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 welltight fit
How well it runs on the OnePlus 13 (24 GB RAM): 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.33.3 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.11.0 / 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 score83.3 / 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 OnePlus 13 (24 GB RAM)

Quant Memory needed Available Speed Runs Quality File to pick in the app
Q2_K 12.2 GB 15.6 GB 27.0 tok/s Runs well 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. —
Q3_K_M 14.8 GB 15.6 GB 21.6 tok/s Runs welltight fit 73Great quality (A) · 73 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. —
Q4_K_M 18.2 GB 15.6 GB — Won't runover the app memory limit 83Great quality (A) · 83 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. —
Q5_K_M 21.1 GB 15.6 GB — Won't runover the app memory limit 85Excellent quality (A+) · 85 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. —
Q6_K 24.3 GB 15.6 GB — Won't runover the app memory limit 87Excellent quality (A+) · 87 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. —
Q8_0 31.2 GB 15.6 GB — Won't runover the app memory limit 88Excellent quality (A+) · 88 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. —
F16 58.2 GB 15.6 GB — Won't runover the app memory limit 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. —

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

Other hardware that runs it

Quick answers

How much memory does Raon-1 mini Preview (30B-A4B) need on the OnePlus 13 24GB?

At Q3_K_M it needs about 14.8 GB. The OnePlus 13 24GB has 15.6 GB usable.

How fast is Raon-1 mini Preview (30B-A4B) on the OnePlus 13 24GB?

About 21.6 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Raon-1 mini Preview (30B-A4B) should I use on the OnePlus 13 24GB?

Q3_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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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