VARCO LLM 2.0 is NC's large language model that can be applied to the development of various natural language processing-based AI services
such as text generation, question answering, chatbots, summarization, and information extraction.
NC's VARCO LLM 2.0 was developed with our own technology, including data construction, pre-training, instruction tuning and alignment tuning.
We evaluated VARCO LLM 2.0 on various NLP tasks and its performance has significantly improved compared to VARCO LLM 1.0, and it boasts the highest performance among other Korean LLMs of similar sizes.
In particular, it has been trained to be used in high-level natural language processing applications such as creative writing, summarization, question and answering, chatbots and translation, and shows high performance in related quantitative indicators.
For inquiries regarding further performance improvement or collaboration for service applications, please contact us by email (varco_llm@ncsoft.com).
Highlights
Korean Text Generation :
VARCO LLM 2.0 is optimized for Korean natural language generation applications.
In particular, it provides more natural and creative responses in understanding user instructions and generating text.
Key Skills
* Question Answering
* Summarization
* Translation
* Text Generation
* Chatbots
* Information Extraction
* Natural Language Understanding
* Creative Writing
* Instruction Following
* Sentiment Analysis
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
You pay based on usage, billed per host hour. Two dimensions let you match cost to how you run the model. The ml.g4dn.12xlarge option runs inference in batch mode, processing grouped requests. The ml.g5.4xlarge option runs inference in real-time mode, handling live requests. Each dimension pairs a specific instance type with a processing mode, so your choice reflects both the compute you need and whether you want batch or real-time inference. You are charged for the hours the instance runs.
Top-of-mind questions for buyers
What compute do the ml.g4dn.12xlarge and ml.g5.4xlarge instances provide, and how are they billed?
Each dimension pairs a GPU-backed AWS SageMaker instance type with a processing mode. You are billed per host hour the instance runs. The ml.g4dn.12xlarge is a multi-GPU instance used for batch inference. The ml.g5.4xlarge is a single-GPU instance used for real-time inference.
Am I charged when an instance is provisioned but not actively processing requests?
Billing meters host hours, so charges apply for the full time the instance runs, not only when it processes requests. To stop software charges, you shut the instance down. A stopped instance may still incur underlying AWS storage or infrastructure fees separate from this listing.
When should I choose the batch dimension versus the real-time dimension?
The ml.g4dn.12xlarge batch dimension processes grouped requests together, which suits scheduled or high-volume jobs. The ml.g5.4xlarge real-time dimension handles live, one-at-a-time requests, which suits interactive services like chatbots or Q&A. Both charge per host hour, so your choice reflects workload type and instance compute.
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An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Real-time inference
Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference .
Batch transform
Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI .
Version release notes
Major Version release of VARCO LLM 2.0 SMALL from NCSOFT
Additional details
Inputs
Outputs
Sample notebooks
Inputs
Summary
Model accepts JSON requests. You can check examples and fields descriptions.
NC R&D Center, 12, Daewangpangyo-ro 644beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea
Tel :02-6201-0099/Email :nc-ai@ncsoft.com
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