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Can the NVIDIA GeForce RTX 4090 run EXAONE 4.5 33B?

88 ptsExcellent (S) · 88 of 100 points: Model quality 35.1/45, Memory fit 24.0/25, Speed 14.2/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q4_K_M it needs about 23.5 GB of 24.0 GB usable, with an estimated speed of 36.0 tok/s.

Best quant
Q4_K_M
Generation speed
36.0 tok/s
Prompt processing
1,731 tok/s
First token (1K prompt)
0.6 s
Largest safe context
13K

Download EXAONE 4.5 33B

Recommended for the NVIDIA GeForce RTX 4090: 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/EXAONE-4.5-33B-GGUF (opens in a new tab)

ollama run hf.co/LGAI-EXAONE/EXAONE-4.5-33B-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-4.5-33B (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-4.5 (opens in a new tab)

git clone https://github.com/LG-AI-EXAONE/EXAONE-4.5

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the S 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.35.1 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.14.2 / 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
Total88.3 / 10088 ptsExcellent (S) · 88 of 100 points: Model quality 35.1/45, Memory fit 24.0/25, Speed 14.2/15, 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 4090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 16.3 GB 24.0 GB 59.2 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 25.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q3_K_M 19.4 GB 24.0 GB 46.6 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 31.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q4_K_M 23.5 GB 24.0 GB 36.0 tok/s Tight fit 88 ptsExcellent (S) · 88 of 100 points: Model quality 35.1/45, Memory fit 24.0/25, Speed 14.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 26.9 GB 24.0 GB 11.0 tok/s Runs with offload 68 ptsGood (B) · 68 of 100 points: Model quality 35.8/45, Memory fit 10.0/25, Speed 7.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 30.7 GB 24.0 GB 5.7 tok/s Runs with offload 67 ptsGood (B) · 67 of 100 points: Model quality 36.5/45, Memory fit 10.0/25, Speed 5.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 38.9 GB 24.0 GB 2.7 tok/s Heavy offload 59 ptsGood (B) · 59 of 100 points: Model quality 36.9/45, Memory fit 4.0/25, Speed 3.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 71.2 GB 24.0 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.1/45, 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.