EXAONE 4.0 1.2B on AMD Ryzen AI Max+ PRO 495 NAS (192 GB)

Can the AMD Ryzen AI Max+ PRO 495 NAS (192 GB) run EXAONE 4.0 1.2B?

Yes, it runs great

Runs great. At F16 it needs about 16.4 GB of 159 GB usable, with an estimated speed of 52.9 tok/s.

Best quant: F16
Generation speed: 52.9 tok/s
Prompt processing: 29,741 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 64K

Best app to run it on the AMD Ryzen AI Max+ PRO 495 NAS (192 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

Usually the first to support new model architectures.

llama-server -hf LGAI-EXAONE/EXAONE-4.0-1.2B-GGUF:BF16

Ollama Runs the Hugging Face GGUF with ollama run hf.co/…
KoboldCpp

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download EXAONE 4.0 1.2B

Recommended for the AMD Ryzen AI Max+ PRO 495 NAS (192 GB): 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/LGAI-EXAONE/EXAONE-4.0-1.2B-GGUF (opens in a new tab)

ollama run hf.co/LGAI-EXAONE/EXAONE-4.0-1.2B-GGUF:BF16

Original weights · Hugging Face

Full-precision files from LG AI Research, for fine-tuning, vLLM, Transformers.

huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from LG AI Research. The weights themselves download from Hugging Face.

github.com/LG-AI-EXAONE/EXAONE-4.0 (opens in a new tab)

git clone https://github.com/LG-AI-EXAONE/EXAONE-4.0

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 30Basic quality (D) · 30 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (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 AMD Ryzen AI Max+ PRO 495 NAS (192 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.	13.5 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); 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.	2.4 / 15
Match score	55.9 / 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 AMD Ryzen AI Max+ PRO 495 NAS (192 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	14.4 GB	159 GB	370 tok/s	Runs great	21Basic quality (D) · 21 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q3_K_M`	14.5 GB	159 GB	291 tok/s	Runs great	25Basic quality (D) · 25 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q4_K_M`	14.6 GB	159 GB	225 tok/s	Runs great	28Basic quality (D) · 28 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	14.8 GB	159 GB	186 tok/s	Runs great	29Basic quality (D) · 29 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	14.9 GB	159 GB	156 tok/s	Runs great	29Basic quality (D) · 29 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	15.2 GB	159 GB	115 tok/s	Runs great	30Basic quality (D) · 30 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	16.4 GB	159 GB	52.9 tok/s	Runs great	30Basic quality (D) · 30 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (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

30Basic quality (D) · 30 of 100: how capable the model is at F16, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 F16 · 61.2 tok/s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at F16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 F16 · 78.4 tok/s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at F16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB F16 · 81.0 tok/s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at F16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER F16 · 113 tok/s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (16 GB) Q8_0 · 50.4 tok/s · Runs great
30Basic quality (D) · 30 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. Apple M4 Pro (24 GB) F16 · 52.9 tok/s · Runs great

Quick answers

How much memory does EXAONE 4.0 1.2B need on the AMD Ryzen AI Max+ PRO 495 NAS 192GB?

At F16 it needs about 16.4 GB. The AMD Ryzen AI Max+ PRO 495 NAS 192GB has 159 GB usable.

How fast is EXAONE 4.0 1.2B on the AMD Ryzen AI Max+ PRO 495 NAS 192GB?

About 52.9 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of EXAONE 4.0 1.2B should I use on the AMD Ryzen AI Max+ PRO 495 NAS 192GB?

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.