Rakuten AI 3.0

Overview

Rakuten's flagship ~700B MoE model built under Japan's GENIAC program and released under Apache 2.0. Best-in-class Japanese performance with serious enterprise e-commerce DNA.

When to pick this model

• Japanese-language production workloads (support, content, search)
• Bilingual JP/EN e-commerce and retail applications
• Enterprise deployments needing Apache 2.0 licensing
• Replacing closed JP models like proprietary Rakuten/LINE APIs
• Localization pipelines targeting the Japanese market

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	420 GB
Q5_K_M	500 GB
Q8_0	745 GB
FP16 (no quantization)	1400 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

• Top-tier Japanese fluency, beating most open models on JP benchmarks
• 700B total parameters give it broad knowledge depth
• Apache 2.0 — no commercial restrictions
• Backed by Rakuten's massive e-commerce and fintech corpus
• Built under Japan's GENIAC sovereign-AI initiative

Limitations

• Roughly 420 GB VRAM in Q4 — datacenter-only
• 32k context is tight versus modern 128k+ flagships
• Heavily skewed toward Japanese and commerce; weaker on global general tasks

Architecture & training

Architecture: MoE · 700B total · Rakuten AI 3 · 32k context

Training: Rakuten — massive JP/EN corpus for e-commerce and enterprise.

Quick start

# Infrastructure lourde requise — non disponible en local standard

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