on

Can the NVIDIA GeForce RTX 4060 run EXAONE 3.0 7.8B Instruct?

71 ptsGood (B) · 71 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 6.3 GB of 8.0 GB usable, with an estimated speed of 42.7 tok/s.

Best quant
Q4_K_M
Generation speed
42.7 tok/s
Prompt processing
1,390 tok/s
First token (1K prompt)
0.7 s
Largest safe context
4K

Download EXAONE 3.0 7.8B Instruct

Recommended for the NVIDIA GeForce RTX 4060: Q4_K_M. 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 Q4_K_M file (opens in a new tab)

huggingface.co/bartowski/EXAONE-3.0-7.8B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/bartowski/EXAONE-3.0-7.8B-Instruct-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

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.16.2 / 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.15.0 / 15
Total71.2 / 10071 ptsGood (B) · 71 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

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 4.7 GB 8.0 GB 70.1 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.5/15 GGUF (opens in a new tab)
Q3_K_M 5.4 GB 8.0 GB 55.2 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 14.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 6.3 GB 8.0 GB 42.7 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 7.1 GB 8.0 GB 35.3 tok/s Tight fit 69 ptsGood (B) · 69 of 100 points: Model quality 16.6/45, Memory fit 24.0/25, Speed 14.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 7.9 GB 8.0 GB 29.6 tok/s Tight fit 68 ptsGood (B) · 68 of 100 points: Model quality 16.9/45, Memory fit 24.0/25, Speed 12.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 9.8 GB 8.0 GB 12.9 tok/s Runs with offload 50 ptsUsable (C) · 50 of 100 points: Model quality 17.1/45, Memory fit 10.0/25, Speed 8.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 17.2 GB 8.0 GB 3.6 tok/s Heavy offload 40 ptsPoor (D) · 40 of 100 points: Model quality 17.1/45, Memory fit 4.0/25, Speed 4.5/15, Memory use 15.0/15 Original weights (opens in a new tab)

Memory figures assume 4K 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.