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LatestSktK2
SktText / LLM

SK Telecom Releases A.X-K2 Multilingual LLM

The Korean telecom carrier's latest open language model targets English, Korean, Chinese, Japanese, and Spanish under a permissive license.

Jul 28, 2026
NotableApache 2.0
A.X-K2

SK Telecom has published A.X-K2, a new entry in its A.X family of language models, on Hugging Face. The model is a text-focused system built for multilingual use, covering English, Korean, Chinese, Japanese, and Spanish.

The release carries an Apache-2.0 license, which allows commercial use, modification, and redistribution with minimal restrictions. That choice matters for developers and enterprises that want to build on the model without navigating the bespoke community terms that accompany many other open-weight releases.

Why it matters

SKT is one of a growing number of telecom operators investing directly in foundation models, and its A.X line reflects a regional bet: strong Korean-language performance paired with broader multilingual coverage. A permissively licensed model from a major carrier gives the Korean and wider Asian developer ecosystem another practical option beyond the usual US and Chinese labs.

Key details from the record:

  • Company: SK Telecom
  • Modality: text-based large language model
  • Languages: English, Korean, Chinese, Japanese, Spanish
  • License: Apache-2.0

SKT has not published parameter counts, context length, or benchmark figures in the available metadata, so the model's scale and positioning against peers remain to be seen. Interested developers can find the weights and any accompanying documentation on the official Hugging Face page.

Sources

  • skt/A.X-K2

    Hugging Face

    Visit
OlderSenseTime Debuts SenseNova U1.5 8B Multimodal PreviewSenseTime · Any-to-Any · 3 months agoNewerMiniMax Releases H3 Video Model on Hugging FaceMiniMax · Text → Video · 3 months ago

Get the model

Hugging Face

Specs

Context window262K tokens
Size694.2 GB
PrecisionFP8
ArchitectureAXK2ForCausalLM
LicenseAPACHE-2.0
Downloads10.1K
Likes108

Can you run it?

Datacenter only — about 416.8 GB even at 4-bit.

  • FP8 (as published)

    Multi-node cluster

    695.8 GB
  • 4-bit

    8×H100 80 GB node

    416.8 GB
Your machine
GGUF builds
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLM

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