Kanana 2 30B-A3B Thinking
By Kakao · South Korea
Updated 2026-07-13
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.
In practice, Kanana 2 30B-A3B Thinking wants a 24 GB card at Q4_K_M (18 GB). Stepping up to Q8_0 nearly doubles the footprint to 33 GB, and unquantized FP16 weights take 60 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Kanana 2 30B-A3B Thinking needs roughly 32 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 15 tokens/sec on entry-level GPUs, on the order of 40 tokens/sec on a mid-range card, and up to 100 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Kanana 2 30B-A3B Thinking to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Kanana 2 30B-A3B Thinking |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 18 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 18 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 18 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (22 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (22 GB used) |
Which GPU should you buy to run Kanana 2 30B-A3B Thinking?
To run Kanana 2 30B-A3B Thinking locally at Q4, you need ~18 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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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
Typical workloads
In our catalog grid, Kanana 2 30B-A3B Thinking is filed under Korean Agents, Asian Multilingual — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks; multilingual workloads.
The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: MoE · 30B · Kakao Brain Kanana 2 · 131k context · native Korean
Training: Kakao — strong in Korean, hybrid thinking/non-thinking reasoning.
The strongest open Korean model right now, with thinking mode and a sane VRAM budget on the side.
Quick start
ollama pull hf.co/kakaoai/Kanana-2-30B-GGUFOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Kanana 2 30B-A3B Thinking need?
At the recommended Q4_K_M quantization, Kanana 2 30B-A3B Thinking needs about 18 GB of VRAM. Q8_0 takes 33 GB, and unquantized FP16 weights take 60 GB.
Can Kanana 2 30B-A3B Thinking run without a GPU?
Yes — with roughly 32 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Kanana 2 30B-A3B Thinking support?
Kanana 2 30B-A3B Thinking supports a 128k-token context window (131,072 tokens).
Can I use Kanana 2 30B-A3B Thinking commercially?
Yes. Kanana 2 30B-A3B Thinking is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Kanana 2 30B-A3B Thinking on consumer hardware?
Our compatibility engine estimates on the order of 40 tokens/sec on a mid-range GPU and up to 100 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Kanana 2 30B-A3B Thinking should I download first?
Start with Q4_K_M (18 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.
Is Kanana 2 30B-A3B Thinking the right pick for you?