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Can the NVIDIA RTX Spark N1X (5120-core GPU) (32 GB) run Raon-1 mini Preview (30B-A4B)?

61Good quality (B) · 61 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q2_K it needs about 13.5 GB of 16.0 GB usable, with an estimated speed of 97.7 tok/s.

Best quant
Q2_K
Generation speed
97.7 tok/s
Prompt processing
4,019 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Best app to run it on the NVIDIA RTX Spark N1X (5120-core GPU) (32 GB)

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 61Good quality (B) · 61 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, 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 RTX Spark N1X (5120-core GPU) (32 GB): 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.27.7 / 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.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 score82.7 / 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 RTX Spark N1X (5120-core GPU) (32 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 13.5 GB 16.0 GB 97.7 tok/s Runs great 61Good quality (B) · 61 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 16.0 GB 16.0 GB 69.3 tok/s Runs wellover GPU memory limit 73Great quality (A) · 73 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 19.5 GB 16.0 GB 56.5 tok/s Slowfar over GPU memory limit 83Great quality (A) · 83 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 22.4 GB 16.0 GB — Won't run 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. All files (opens in a new tab)
Q6_K 25.5 GB 16.0 GB — Won't run 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. All files (opens in a new tab)
Q8_0 32.5 GB 16.0 GB — Won't run 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. All files (opens in a new tab)
F16 59.5 GB 16.0 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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