Kanana 2 30B-A3B Thinking

Overview

Kakao's agentic 30B MoE (3B active) with native hybrid thinking and Korean-first training. Apache 2.0 with MLA attention and 131k context.

When to pick this model

• Korean-language products from chat to content generation
• Multilingual deployments covering KR/EN/JP/ZH/TH/VI
• Agentic workflows that benefit from a togglable thinking mode
• Long-document analysis up to 131k tokens
• Apache 2.0 commercial use on a single 24GB GPU

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	18 GB
Q5_K_M	22 GB
Q8_0	33 GB
FP16 (no quantization)	60 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Strengths

• 131k context window in a 30B MoE
• Hybrid thinking/non-thinking mode toggle
• Native Korean performance backed by Kakao's corpus
• MLA attention cuts KV-cache footprint
• Apache 2.0 with only 3B active params per token

Limitations

• Around 18 GB VRAM in Q4 — fits a single GPU but tight on consumer cards
• Quality drops outside Korean and English

Architecture & training

Architecture: MoE · 30B · Kakao Brain Kanana 2 · 131k context · native Korean

Training: Kakao — strong in Korean, hybrid thinking/non-thinking reasoning.

Quick start

ollama pull hf.co/kakaoai/Kanana-2-30B-GGUF

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.