K-EXAONE-236B-A23B-W4A16-G128

🔄 (2026-04-15) Improved Quantization - scale-up calibration dataset

• (# of Calibration Dataset 512, Sequence len 2048)

🔄 (2026-04-13) Bug fixed

• the vLLM rms_norm contiguous-buffer patch is no longer required.
• the tensor-parallel issue is resolved, and inference now works correctly on other GPUs(a100, ...) as well.

🔄 (2026-04-13) Improved Quantization - scale-up calibration dataset

• (# of Calibration Dataset 512, Sequence len 512)

🔄 (2026-04-10) Initial commit

• (# of Calibration Dataset 32, Sequence len 128)

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W4A16 AWQ quantization of LGAI-EXAONE/K-EXAONE-236B-A23B, produced with llm-compressor.

This is the first W4A16 AWQ checkpoint for K-EXAONE-236B-A23B publicly available — the original model only has FP8 and GGUF variants on HuggingFace.

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Model Details

Property	Value
Base model	LGAI-EXAONE/K-EXAONE-236B-A23B
Architecture	ExaoneMoEForCausalLM
Total parameters	~236B
Active parameters	~23B per token
Quantization method	AWQ (Activation-aware Weight Quantization)
Weight precision	INT4 (packed)
Activation precision	BF16
Group size	128
Quantization scope	All Linear layers except lm_head and gate projections
Compressed-tensors version	0.15.0
Context length	262,144 tokens
Languages	Korean, English

Architecture Highlights

• 48 transformer layers with mixed sliding-window (LLLG pattern) and full attention
• MoE layers: 47 sparse MoE layers + 1 dense MLP (layer 0)
• 128 routed experts + 1 shared expert per MoE layer; top-8 experts activated per token
• Sigmoid scoring with norm_topk_prob=True
• Hidden size: 6144, MoE intermediate size: 2048

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Quantization Details

Quantization was performed using llm-compressor with a MoE-aware AWQ recipe.

The EXAONE specific MoE-aware AWQ recipe was developed in SqueezeBits/llm-compressor-K-EXAONE.

Method: AWQ applies channel-wise scaling to minimize quantization error by protecting salient weights, using a calibration dataset to determine optimal scales.

Recipe highlights:

• scheme: W4A16 (INT4 weights, BF16 activations)
• group_size: 128
• n_grid: 20 (search resolution for AWQ scale optimization)
• duo_scaling: True
• Smooth mappings cover all MoE expert layers (layers 1–47) independently, plus attention and MLP projections
• Layer 0 (dense MLP) and lm_head are excluded from quantization
• Gate weight tensors are excluded from quantization

Calibration dataset: neuralmagic/LLM_compression_calibration (512 samples, sequence length 2048)

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Hardware Requirements

Precision	VRAM
This model (W4A16)	~120 GB
Original BF16	~480 GB

CUDA / driver requirement: for H200(sm_90), vLLM 0.19.0 wheels are compiled with the CUDA 12.9 toolkit, so you need CUDA ≥ 12.9 (NVIDIA driver ≥ 575.x) to run without issues. If your driver is older, follow the monkey-patch workaround in the inference section below.

• It is okay for A100(sm_80)

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Setup

# 1. Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. Create a Python 3.12 virtual environment
uv venv --python 3.12

# 3. Activate it
source .venv/bin/activate

# 4. Install vLLM and Transformers
uv pip install "vllm==0.19.0"
uv pip install "transformers==5.5.0"

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Running Inference

Save the script below as vllm_inference.py and run:

python vllm_inference.py

from vllm import LLM, SamplingParams

MODEL_PATH = "Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128"


"""# ── Monkey-patch required if NVIDIA driver < 575.x (CUDA < 12.9) ─────────────
# for H200, vLLM 0.19.0 is compiled with CUDA 12.9; older drivers cannot JIT-compile its
# PTX and crash with "cudaErrorUnsupportedPtxVersion" during weight loading.
# This patch forces vLLM to use WNA16MoEMethod (no Marlin CUDA kernels) instead
# of MarlinMoEMethod.  Safe to keep even after upgrading the driver.
import vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe as _ct_moe
_ct_moe.check_moe_marlin_supports_layer = lambda *args, **kwargs: False
# ─────────────────────────────────────────────────────────────────────────────"""


def main():
    llm = LLM(
        model=MODEL_PATH,
        max_model_len=8192,
        trust_remote_code=True,     # K-EXAONE uses custom modeling code
        tensor_parallel_size=2,     # 2x H200; or 4x A100
        enforce_eager=True,
    )

    sampling_params = SamplingParams(
        temperature=0.2,
        top_p=0.9,
        max_tokens=2048,
    )

    prompts = [
        "What is the capital of South Korea?",
        "Explain the difference between MoE and dense transformer models.",
        "Write a short Python function to compute Fibonacci numbers.",
    ]

    tokenizer = llm.get_tokenizer()
    formatted_prompts = [
        tokenizer.apply_chat_template(
            [{"role": "user", "content": p}],
            tokenize=False,
            add_generation_prompt=True,
        )
        for p in prompts
    ]

    outputs = llm.generate(formatted_prompts, sampling_params)

    for prompt, output in zip(prompts, outputs):
        print(f"Prompt : {prompt}")
        print(f"Response: {output.outputs[0].text.strip()}")
        print("-" * 60)


if __name__ == "__main__":
    main()

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Files

File	Description
model-00001-of-00003.safetensors	Model weights shard 1/3
model-00002-of-00003.safetensors	Model weights shard 2/3
model-00003-of-00003.safetensors	Model weights shard 3/3
model.safetensors.index.json	Weight shard index
config.json	Model config with quantization metadata
recipe.yaml	llm-compressor AWQ recipe used for quantization
tokenizer.json	Tokenizer
tokenizer_config.json	Tokenizer config
chat_template.jinja	Chat template
generation_config.json	Default generation config

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License

This model inherits the license of the base model LGAI-EXAONE/K-EXAONE-236B-A23B. Please refer to the original model page for license details.

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Citation

If you use this model, please cite the original K-EXAONE work:

@misc{k-exaone-236b,
  title  = {K-EXAONE-236B-A23B},
  author = {LG AI Research},
  year   = {2025},
  url    = {https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B}
}