Can the NVIDIA L40S run Raon 1 nano?

Yes, it runs great

Runs great. At F16 it needs about 10.3 GB of 48.0 GB usable, with an estimated speed of 70.5 tok/s.

Best quant: F16
Generation speed: 70.5 tok/s
Prompt processing: 18,683 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 256K

Best app to run it on the NVIDIA L40S

On Linux

Inference server · runs on vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

vllm serve KRAFTON/Raon-1-nano

LM Studio Point-and-click: search, download and chat in one window.
SGLang The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

Point-and-click: search, download and chat in one window.

Open Raon 1 nano in LM Studio

llama.cpp Usually the first to support new model architectures.
Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

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 nano

Recommended for the NVIDIA L40S: F16. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the F16 file (opens in a new tab)

huggingface.co/KRAFTON/Raon-1-nano-gguf (opens in a new tab)

ollama run hf.co/KRAFTON/Raon-1-nano-gguf:F16

Original weights · Hugging Face

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

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

No sign-in needed.

Links checked 10 hours ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 35Basic quality (D) · 35 of 100: how capable the model is at F16, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at F16, 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 L40S: 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.	15.8 / 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.	4.9 / 15
Match score	60.7 / 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 L40S

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	4.6 GB	48.0 GB	493 tok/s	Runs great	24Basic quality (D) · 24 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q3_K_M`	5.0 GB	48.0 GB	388 tok/s	Runs great	29Basic quality (D) · 29 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q4_K_M`	5.4 GB	48.0 GB	300 tok/s	Runs great	33Basic quality (D) · 33 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	5.7 GB	48.0 GB	248 tok/s	Runs great	33Basic quality (D) · 33 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q6_K`	6.1 GB	48.0 GB	208 tok/s	Runs great	34Basic quality (D) · 34 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	7.0 GB	48.0 GB	153 tok/s	Runs great	35Basic quality (D) · 35 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	10.3 GB	48.0 GB	70.5 tok/s	Runs great	35Basic quality (D) · 35 of 100: how capable the model is at F16, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab) 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

35Basic quality (D) · 35 of 100: how capable the model is at F16, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER F16 · 41.1 tok/s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 Q8_0 · 48.2 tok/s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at F16, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT F16 · 45.0 tok/s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at F16, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER F16 · 54.8 tok/s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at F16, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER F16 · 60.0 tok/s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at F16, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti F16 · 73.1 tok/s · Runs great

Quick answers

How much memory does Raon 1 nano need on the NVIDIA L40S 48GB?

At F16 it needs about 10.3 GB. The NVIDIA L40S 48GB has 48.0 GB usable.

How fast is Raon 1 nano on the NVIDIA L40S 48GB?

About 70.5 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Raon 1 nano should I use on the NVIDIA L40S 48GB?

F16. 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.