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Can the NVIDIA GeForce RTX 4060 run EXAONE 4.0 1.2B?

65 ptsGood (B) · 65 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.7/15

Yes, it runs well

Runs well. At F16 it needs about 4.1 GB of 8.0 GB usable, with an estimated speed of 61.2 tok/s.

Best quant
F16
Generation speed
61.2 tok/s
Prompt processing
8,494 tok/s
First token (1K prompt)
0.3 s
Largest safe context
64K

Download EXAONE 4.0 1.2B

Recommended for the NVIDIA GeForce RTX 4060: 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 1 week ago against Hugging Face, Ollama and GitHub.

How the B grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.13.5 / 45
Memory fitFull marks when everything sits in GPU or unified 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.11.7 / 15
Total65.2 / 10065 ptsGood (B) · 65 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.7/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 4060

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 2.0 GB 8.0 GB 428 tok/s Runs well 55 ptsUsable (C) · 55 of 100 points: Model quality 9.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.8/15 GGUF files (opens in a new tab)
Q3_K_M 2.1 GB 8.0 GB 337 tok/s Runs well 57 ptsUsable (C) · 57 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.1/15 GGUF files (opens in a new tab)
Q4_K_M 2.3 GB 8.0 GB 261 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 12.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.6/15 GGUF (opens in a new tab)
Q5_K_M 2.4 GB 8.0 GB 215 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 13.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.0/15 GGUF (opens in a new tab)
Q6_K 2.6 GB 8.0 GB 181 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 13.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.4/15 GGUF (opens in a new tab)
Q8_0 2.9 GB 8.0 GB 133 tok/s Runs well 61 ptsGood (B) · 61 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15 GGUF (opens in a new tab)
F16 4.1 GB 8.0 GB 61.2 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.7/15 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

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.