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Can the AMD Radeon RX 9060 XT 8GB run EXAONE 4.0 32B?

68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (heavy RAM offload)

Slow (heavy RAM offload). At Q4_K_M it needs about 21.3 GB of 8.0 GB usable, with an estimated speed of 3.4 tok/s.

Best quant
Q4_K_M
Generation speed
3.4 tok/s
Prompt processing
520 tok/s
First token (1K prompt)
2.0 s
Largest safe context
—

Best app to run it on the AMD Radeon RX 9060 XT 8GB

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
  • Usually the first to support new model architectures.
llama-server -hf LGAI-EXAONE/EXAONE-4.0-32B-GGUF:Q4_K_M
  • LM Studio Splits the model between GPU memory and system RAM, which this setup needs.
  • Lemonade Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
  • Usually the first to support new model architectures.
llama-server -hf LGAI-EXAONE/EXAONE-4.0-32B-GGUF:Q4_K_M
  • LM Studio Splits the model between GPU memory and system RAM, which this setup needs.
  • Lemonade Splits the model between GPU memory and system RAM, which this setup needs.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download EXAONE 4.0 32B

Recommended for the AMD Radeon RX 9060 XT 8GB: 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-4.0-32B-GGUF (opens in a new tab)

ollama run hf.co/LGAI-EXAONE/EXAONE-4.0-32B-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-4.0-32B (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-4.0 (opens in a new tab)

git clone https://github.com/LG-AI-EXAONE/EXAONE-4.0

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Slowheavy RAM offload
How well it runs on the AMD Radeon RX 9060 XT 8GB: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.30.8 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.4.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.4.4 / 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
Match score54.1 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the AMD Radeon RX 9060 XT 8GB

Quant Memory needed Available Speed Runs Quality Download
Q2_K 14.5 GB 8.0 GB 6.7 tok/s Slowheavy RAM offload 50Fair quality (C) · 50 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 17.4 GB 8.0 GB 4.7 tok/s Slowheavy RAM offload 60Good quality (B) · 60 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 21.3 GB 8.0 GB 3.4 tok/s Slowheavy RAM offload 68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 24.5 GB 8.0 GB 2.7 tok/s Very slowheavy RAM offload 69Good quality (B) · 69 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 28.0 GB 8.0 GB — Won't run 71Great quality (A) · 71 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 35.7 GB 8.0 GB — Won't run 72Great quality (A) · 72 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 65.7 GB 8.0 GB — Won't run 72Great quality (A) · 72 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 2 (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

Quick answers

How much memory does EXAONE 4.0 32B need on the RX 9060 XT 8GB?

At Q4_K_M it needs about 21.3 GB. The RX 9060 XT 8GB has 8.0 GB usable.

How fast is EXAONE 4.0 32B on the RX 9060 XT 8GB?

About 3.4 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of EXAONE 4.0 32B should I use on the RX 9060 XT 8GB?

Q4_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.

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