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Can the NVIDIA RTX PRO 6000 Blackwell run K-EXAONE 236B-A23B?

66 ptsGood (B) · 66 of 100 points: Model quality 27.7/45, Memory fit 10.0/25, Speed 13.9/15, Memory use 15.0/15

Yes, with part of the model in system RAM

Runs with offload. At Q2_K it needs about 101 GB of 96.0 GB usable, with an estimated speed of 34.5 tok/s.

Best quant
Q2_K
Generation speed
34.5 tok/s
Prompt processing
3,778 tok/s
First token (1K prompt)
0.3 s
Largest safe context
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Download K-EXAONE 236B-A23B

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

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

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 7 days 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.27.7 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.13.9 / 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
Total66.7 / 10066 ptsGood (B) · 66 of 100 points: Model quality 27.7/45, Memory fit 10.0/25, Speed 13.9/15, 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 RTX PRO 6000 Blackwell

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 101 GB 96.0 GB 34.5 tok/s Runs with offload 66 ptsGood (B) · 66 of 100 points: Model quality 27.7/45, Memory fit 10.0/25, Speed 13.9/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q3_K_M 122 GB 96.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 10.0/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF files (opens in a new tab)
Q4_K_M 150 GB 96.0 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
Q5_K_M 174 GB 96.0 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
Q6_K 200 GB 96.0 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
Q8_0 257 GB 96.0 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)
F16 480 GB 96.0 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.