Check my GPU

on

Can the NVIDIA GeForce RTX 4070 SUPER run EXAONE 3.0 7.8B Instruct?

72 ptsGreat (A) · 72 of 100 points: Model quality 17.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q8_0 it needs about 10.0 GB of 12.0 GB usable, with an estimated speed of 40.3 tok/s.

Best quant
Q8_0
Generation speed
40.3 tok/s
Prompt processing
3,269 tok/s
First token (1K prompt)
0.3 s
Largest safe context
4K

Download EXAONE 3.0 7.8B Instruct

Recommended for the NVIDIA GeForce RTX 4070 SUPER: Q8_0. 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 Q8_0 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:Q8_0

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.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the A 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.17.1 / 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
Total72.1 / 10072 ptsGreat (A) · 72 of 100 points: Model quality 17.1/45, 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 4070 SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 4.9 GB 12.0 GB 130 tok/s Runs well 61 ptsGood (B) · 61 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.4/15 GGUF (opens in a new tab)
Q3_K_M 5.6 GB 12.0 GB 102 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 14.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.7/15 GGUF (opens in a new tab)
Q4_K_M 6.5 GB 12.0 GB 79.1 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.5/15 GGUF (opens in a new tab)
Q5_K_M 7.3 GB 12.0 GB 65.4 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.0/15 GGUF (opens in a new tab)
Q6_K 8.1 GB 12.0 GB 54.9 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 16.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 10.0 GB 12.0 GB 40.3 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 17.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 17.4 GB 12.0 GB 5.7 tok/s Heavy offload 41 ptsPoor (D) · 41 of 100 points: Model quality 17.1/45, Memory fit 4.0/25, Speed 5.7/15, 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.