Check my GPU

on

Can the Apple M3 Ultra (512 GB) run K-EXAONE 236B-A23B?

83 ptsGreat (A) · 83 of 100 points: Model quality 37.6/45, Memory fit 25.0/25, Speed 10.3/15, Memory use 10.6/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 176 GB of 384 GB usable, with an estimated speed of 18.8 tok/s.

Best quant
Q4_K_M
Generation speed
18.8 tok/s
Prompt processing
766 tok/s
First token (1K prompt)
1.3 s
Largest safe context
256K

Download K-EXAONE 236B-A23B

Recommended for the Apple M3 Ultra (512 GB): 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/LGAI-EXAONE/K-EXAONE-236B-A23B-GGUF (opens in a new tab)

ollama run hf.co/LGAI-EXAONE/K-EXAONE-236B-A23B-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B (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/K-EXAONE (opens in a new tab)

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

Links checked 2 hours 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.37.6 / 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.10.3 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.10.6 / 15
Total83.5 / 10083 ptsGreat (A) · 83 of 100 points: Model quality 37.6/45, Memory fit 25.0/25, Speed 10.3/15, Memory use 10.6/15

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

Every quantization on the Apple M3 Ultra (512 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 127 GB 384 GB 30.9 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 27.7/45, Memory fit 25.0/25, Speed 13.2/15, Memory use 7.6/15 GGUF files (opens in a new tab)
Q3_K_M 148 GB 384 GB 24.3 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 11.7/15, Memory use 8.9/15 GGUF files (opens in a new tab)
Q4_K_M 176 GB 384 GB 18.8 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 37.6/45, Memory fit 25.0/25, Speed 10.3/15, Memory use 10.6/15 GGUF (opens in a new tab)
Q5_K_M 200 GB 384 GB 15.5 tok/s Runs well 84 ptsGreat (A) · 84 of 100 points: Model quality 38.4/45, Memory fit 25.0/25, Speed 9.4/15, Memory use 12.0/15 GGUF (opens in a new tab)
Q6_K 226 GB 384 GB 13.1 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 39.2/45, Memory fit 25.0/25, Speed 8.6/15, Memory use 13.6/15 GGUF (opens in a new tab)
Q8_0 283 GB 384 GB 9.6 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 39.6/45, Memory fit 25.0/25, Speed 7.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 506 GB 384 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.