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LGAI-EXAONE/
K-EXAONE-2.0-750B-A37B-NVFP4

Name: K-EXAONE-2.0-750B-A37B-NVFP4
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K-EXAONE-2.0-750B-A37B-NVFP4

Try K-EXAONE 2.0 now! ➡️ Open Demo

Introduction

We introduce K-EXAONE 2.0, a frontier-scale multilingual language model developed by LG AI Research. K-EXAONE 2.0 was scaled to more than three times the size of its predecessor through upcycling, followed by continual pretraining, difficulty-focused mid-training, and post-training. K-EXAONE 2.0 is broadly competitive with leading open-weight models, demonstrating substantial improvements over its predecessor and achieving particularly strong results in long-context retrieval and safety.

Highlights

Frontier-Class Scale To build a large-scale foundation model with frontier-level intelligence, we upcycled the K-EXAONE model by expanding both its depth and width, resulting in a more favorable scaling curve. During this process, we found that clamping after two SwiGLU branches effectively mitigates the exploding activations in deeper layers, improving both training and inference stability.
Advanced Reasoning & Agentic Intelligence In response to the growth of agentic AI, we focused on expanding the model's capabilities in reasoning, agentic workflows, and long-context management. Through careful calibration of the training data and recipes, K-EXAONE 2.0 achieves consistent improvements in agentic coding and long-context understanding, with strong performance on long-context retrieval and safety.
Production-Ready Inference We support two speculative decoding methods to accelerate inference: MTP (Multi-Token Prediction) and DSpark. Both methods can speed up model generation by approximately 3–5×, reducing latency for long-horizon workloads such as agentic tasks.
Multilinguality & Openness We expanded multilingual coverage from six to ten languages: Korean, English, Spanish, German, Japanese, Vietnamese, French, Italian, Polish, and Portuguese. We also release K-EXAONE 2.0 under the Apache license 2.0 so that the broader AI ecosystem can inspect, deploy, and build upon it.

Model Configurations

Number of Parameters	750B
Active Parameters	37B
Hidden Dimension	6,144
Intermediate Size	18,432
Number of Layers	78 (2 heading Dense + 76 Sparse) Main layers 1 MTP layers
Attention	1 x Global (NoPE) 1 x 4096 SWA 19 x [3 x 128 SWA + 1 x Global] Blocks
Attention Heads	64 Q-heads / 8 KV-heads
Head Dimension	128
Number of Experts	1 Shared Expert 256 Total Experts 8 Activated Experts
Expert Dimension	2,048
Vocab Size	153,600
Context Length	262,144
Knowledge Cutoff	2025 2Q
Quantization	NVFP4

Evaluation Results

The following table shows the benchmark results for the K-EXAONE 2.0 BF16 model. Detailed evaluation results and configurations can be found in our technical report.

	K-EXAONE 2.0	K-EXAONE	Qwen3.5	GLM-5.1	DSV4 Pro (max)
Architecture	MoE	MoE	MoE	MoE	MoE
Total Params	750B	236B	397B	754B	1.6T
Active Params	37B	23B	17B	40B	49B
World Knowledge
MMLU-Pro	83.5	83.8	89.8	86.0	87.5
GPQA-Diamond	82.2	79.1	88.4	86.2	90.1
Humanity's Last Exam	18.3	13.6	28.7	31.0	37.7
Math
AIME 2026	92.3	92.2	91.3	95.3	95.2
HMMT Feb 2026	78.4	80.7	84.6	82.6	95.2
IMO Answer	78.6	76.3	80.9	83.8	89.8
Coding / Agentic Coding
SciCode	37.4	35.6	42.0	43.8	50.0
SWE Bench Verified	68.2	49.4	76.4	73.6	80.6
Terminal-Bench 2.1	43.8	30.3	51.3	61.8	64.0
Agentic Tool Use
τ³-Banking	14.2	14.2	13.4	11.5	25.8
Claw-Eval	77.7	70.3	79.7	84.4	82.7
Instruction Following
IFEval	92.4	89.7	92.6	93.9	94.0
IFBench	72.6	67.3	76.5	76.3	76.5
Long Context Understanding
OpenAI-MRCR	94.4	52.3	93.0	71.5	92.9
AA-LCR	56.2	53.5	65.7	62.3	66.3
Ko-LongBench	89.6	86.8	91.3	83.6	91.4
Korean
KMMLU-Pro	69.1	67.3	77.4	75.8	80.5
Click	84.2	83.9	88.9	88.7	91.6
HRM8K-KSM	91.1	91.9	91.2	89.4	94.3
Multilinguality
MMMLU	86.6	86.2	90.6	89.7	89.6
GlobalMMLU-Lite	86.6	86.9	92.1	90.7	92.0
PolyMath	71.3	57.4	73.3	73.8	80.9
Safety
KGC-Safety	99.8	96.1	92.0	69.3	82.8
ROK-Fortress	89.5	60.9	86.1	73.2	47.6

Quickstart

Serving K-EXAONE 2.0

SGLang

Install SGLang from our fork as follows:

bash

uv venv
source .venv/bin/activate
uv pip install git+https://github.com/lkm2835/sglang@add-k-exaone2
uv pip install git+https://github.com/nuxlear/transformers@add-k-exaone2

The following script shows how to serve K-EXAONE 2.0 with SGLang on single node of 8 x NVIDIA H200 GPUs.

Run the following scripts on every node.

bash

sglang serve \
    --model-path LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-NVFP4 \
    --served-model-name K-EXAONE-2.0-750B-A37B-NVFP4 \
    --tp 8 \
    --kv-cache-dtype fp8_e4m3 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder \
    --host 0.0.0.0 \
    --port 8000 \
    --max-running-requests 128 \
    --speculative-algo EAGLE \
    --speculative-num-steps 4 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 5 \
    --swa-full-tokens-ratio 0.3

[!NOTE] If you are using NVIDIA B200 GPUs, please add the --disable-prefill-cuda-graph option to prevent issues with model generation collapse. We will update this note once these issues have been resolved.

vLLM

You should install the vLLM library from our fork as below:

bash

uv venv
source .venv/bin/activate
uv pip install git+https://github.com/lkm2835/vllm@add-k-exaone2 --torch-backend auto
uv pip install git+https://github.com/nuxlear/transformers@add-k-exaone2

The following script shows how to serve K-EXAONE 2.0 with vLLM on single node of 8 x NVIDIA H200 GPUs.

Run the below script on every server.

bash

HEADLESS_ARG=''
if [ \"\$NODE_RANK\" -ne 0 ]; then
    HEADLESS_ARG='--headless'
fi

exec vllm serve LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-NVFP4 \
    --served-model-name K-EXAONE-2.0-750B-A37B-NVFP4 \
    --trust-remote-code \
    --tensor-parallel-size 8 \
    --kv-cache-dtype fp8_e4m3 \
    --max-num-seqs 256 \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_xml \
    --host 0.0.0.0 \
    --port 8000 \
    --speculative_config '{
        "method": "mtp", 
        "num_speculative_tokens": 4
    }' \
    $HEADLESS_ARG

Using K-EXAONE 2.0

[!IMPORTANT] To achieve the expected performance, we recommend using the following configurations:

We recommend using temperature=1.0 and top_p=0.95 for better output quality in most cases.

K-EXAONE 2.0 uses enable_thinking=True by default. Thus, you need to set enable_thinking=False to use non-reasoning mode.

We recommend using preserve_thinking=True in long-running tasks, such as agentic use or deep research.

Once K-EXAONE 2.0 is running on an inference engine, you can access it using the OpenAI Python SDK.

Reasoning mode

For tasks that require high accuracy, you can use the K-EXAONE 2.0 model in reasoning mode. The K-EXAONE 2.0 model supports the preserve_thinking option, which allows it to track previous reasoning_content across subsequent conversations. For agentic workflows, it is recommended to use preserve_thinking=True.

python

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",
)

messages = [
    {
        "role": "user",
        "content": "Implement fibonacci with python code.",
    }
]

response = client.chat.completions.create(
    model="LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-NVFP4",
    messages=messages,
    max_tokens=32768,
    temperature=1.0,
    top_p=0.95,
    extra_body={
        "chat_template_kwargs": {
            "enable_thinking": True,  # default: True
            "preserve_thinking": True,  # default: False
        }
    }, 
)
print(response)

Non-reasoning mode

For tasks where latency matters more than accuracy, you can run the K-EXAONE 2.0 model in non-reasoning mode.

python

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",
)

messages = [
    {
        "role": "user",
        "content": "Explain how useful you are.",
    }
]

response = client.chat.completions.create(
    model="LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-NVFP4",
    messages=messages,
    max_tokens=32768,
    temperature=1.0,
    top_p=0.95,
    extra_body={
        "chat_template_kwargs": {
            "enable_thinking": False,  # default: True
            "preserve_thinking": False,  # default: False
        }
    }, 
)
print(response)

Tool calling

For your AI-powered agent, you can leverage K-EXAONE 2.0’s tool calling capability.

python

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "roll_dice",
            "description": "Roll the dice with the number 1 to N. User can select the number N.",
            "parameters": {
                "type": "object",
                "properties": {
                    "max_num": {
                        "type": "integer",
                        "description": "The maximum number on the dice."
                    }
                },
                "required": ["max_num"]
            },
        },
    }
]

messages = [
    {
        "role": "user",
        "content": "Roll a D20 twice and sum the results."
    }
]

response = client.chat.completions.create(
    model="LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-NVFP4",
    messages=messages,
    tools=tools,
    max_tokens=32768,
    temperature=1.0,
    top_p=0.95,
    extra_body={
        "chat_template_kwargs": {
            "enable_thinking": True,  # default: True
            "preserve_thinking": True,  # default: False
        }
    },
)

print(response)

Agentic Use

You can leverage K-EXAONE 2.0’s agentic capabilities by integrating it with agent frameworks and harnesses.

OpenCode

To use the deployed K-EXAONE 2.0 model, you may need to update your opencode.json file. Below is an example JSON configuration for setting up a custom server as your model provider.

json

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "local": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Local OpenAI-compatible server",
      "options": {
        "baseURL": "http://localhost:8000/v1",
        "extraBody": {
          "chat_template_kwargs": {
            "enable_thinking": true,
            "preserve_thinking": true
          }
        }
      },
      "models": {
        "K-EXAONE-2": {
          "name": "K-EXAONE 2.0",
          "limit": {
            "context": 262144,
            "output": 32768
          }
        }
      }
    }
  }
}

Limitation

K-EXAONE 2.0 language models, like all existing language models, have certain limitations and may occasionally generate inappropriate responses. The language model generates responses based on the output probability of tokens, and it is determined during learning from training data. While we make every effort to exclude personal, harmful, and biased information from the training data, some problematic content may still be included, potentially leading to undesirable responses. Please note that the text generated by K-EXAONE 2.0 language models does not reflect the views of LG AI Research.

Inappropriate answers may be generated, which contain personal, harmful or other inappropriate information.
Biased responses may be generated, which are associated with age, gender, race, and so on.
The generated responses rely heavily on statistics from the training data, which can result in the generation of semantically or syntactically incorrect sentences.
Since the models do not reflect the latest information, the responses may be false or contradictory.

LG AI Research strives to reduce potential risks that may arise from K-EXAONE 2.0 language models. Users are not allowed to engage in any malicious activities (e.g., keying in illegal information) that may induce the creation of inappropriate outputs violating LG AI’s ethical principles when using K-EXAONE 2.0 language models.

License

The model is licensed under Apache License 2.0.

Citation

@article{k-exaone-2.0,
  title={K-EXAONE 2.0 Technical Report},
  author={{LG AI Research}},
  journal={arXiv preprint arXiv:2608.04505},
  year={2026}
}

Contact

LG AI Research Technical Support: contact_us@lgresearch.ai