Which Local LLM Models Support Japanese Best?

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Quick Answer

The best Japanese local LLM depends on your task. For conversation: Rinna 3.6B (runs on 4 GB RAM). For instruction following: ELYZA-7B. For coding with Japanese: Qwen2.5-Coder. All run via Ollama.

▸Rinna 3.6B — Japanese-native, 4 GB RAM minimum, daily conversation
▸ELYZA-7B — instruction following and Q&A, 6 GB RAM
▸Qwen2.5 7B — multilingual JA/ZH/EN and coding, 6 GB RAM

Updated: 2026-05

Model ComparisonsIntermediate

Key Takeaways

✓Rinna 3.6B is the lightest Japanese-native model — runs on 4 GB RAM via Ollama (dedicated inference only; close all background apps) with no fine-tuning needed
✓ELYZA-7B (fine-tuned Llama) leads on instruction following in Japanese; use for Q&A and task automation
✓Qwen2.5 7B is the best multilingual choice: strong Japanese alongside Chinese and English, plus coding support
✓Japanese tokenization runs ~20–30% fewer effective tokens/second than English due to kanji/kana overhead — factor this into latency expectations
✓Q4_K_M is the minimum recommended quantization for Japanese; Q3 and below show measurable quality degradation

Japanese Model Comparison Table

As of May 2026, five local LLMs stand out for Japanese-language tasks: Rinna 3.6B, ELYZA-7B, CyberAgent CALM3-22B, Qwen2.5 7B, and Phi-4. Each fills a different hardware and use-case niche. The table below gives you the decision anchor points.

Decision shortcut: Use Rinna 3.6B if you have only 4 GB RAM and need Japanese-native conversation. Use ELYZA-7B for structured instruction following on 6 GB hardware. Use Qwen2.5 7B when you need multilingual output across Japanese, Chinese, and English in a single model.

Model	Size / Min RAM	Best for
Rinna 3.6B	3.6B / 4 GB RAM	Daily conversation in Japanese
ELYZA-7B	7B / 6 GB RAM	Instruction following, Q&A
CyberAgent CALM3-22B	22B / 16 GB RAM	Business documents in Japanese
Qwen2.5 7B	7B / 6 GB RAM	Multilingual JA/ZH/EN, coding
Phi-4	14B / 10–12 GB RAM	Reasoning + Japanese (via fine-tune)

Recommendations by Task

Match the model to your task rather than defaulting to the largest available. Japanese tokenization produces ~20–30% fewer effective tokens per second compared to English text — kanji, hiragana, and katakana each require separate token slots, which means a model rated at 20 tok/s on English delivers roughly 14–16 effective tok/s on Japanese. Plan latency accordingly.

Task-to-model mapping: Daily chat → Rinna 3.6B (lightest, Japanese-native, no fine-tuning required). Business documents and formal writing → ELYZA-7B or CyberAgent CALM3-22B (CALM3 is the stronger option when RAM allows 16 GB). Coding assistance in Japanese → Qwen2.5-Coder (multilingual code model with strong Japanese comment and documentation support). Translation between Japanese, English, and Chinese → Qwen2.5 7B (single model handles all three languages without swapping).

Quantization matters more for Japanese than English. Q4_K_M is the recommended minimum — testing shows minimal quality degradation. Q3_K_M produces a ~5–10% reduction in Japanese output quality. Q2 quantization is not recommended for Japanese use. All models in this comparison are available at Q4_K_M via Ollama or LM Studio.

For apps to run these models on Android in Japan, see the Android LLM apps for Japan guide. For GPU recommendations to run 7B+ Japanese models locally in Japan, see the Japan GPU price guide. For a broader local model selection guide, see best local LLMs for coding and LLM quantization explained.

Quick Answers About Japanese Local LLMs

Do Llama and Mistral support Japanese?▾

Basic support only. Llama 3.1 8B includes some Japanese training data but performs 30–40% worse than Japanese-specific models on Japanese-language benchmarks. Mistral 7B has minimal Japanese training data and is not recommended for Japanese tasks. Use ELYZA-7B (Llama fine-tune) or Rinna 3.6B for reliable Japanese output.

Does quantization hurt Japanese quality?▾

Q4_K_M has minimal degradation and is the recommended minimum for Japanese. Q3_K_M shows approximately 5–10% quality reduction on Japanese text — noticeable in longer responses and formal writing. Avoid Q2 for Japanese use entirely. Q8_0 provides the best quality when VRAM is available.

Does a Japanese model run on an 8 GB MacBook?▾

Yes. Rinna 3.6B Q4 and ELYZA-7B Q4_K_M both run on a MacBook with 8 GB unified memory via Ollama. Apple Silicon treats system RAM as unified memory, so the full 8 GB is available to the model. Expect ~8–12 tok/s on M1/M2 hardware at these sizes.

How do I start a Japanese model in Ollama?▾

Run ollama run rinna or ollama run elyza in a terminal. Ollama downloads the model automatically on first run. Check the Ollama model library at ollama.com/library for the latest available variants and quantization options for each Japanese model.

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