# Sakana Fugu: New Model from Sakana AI

> Published 2026-06-22 · https://www.promptzone.com/zuri_o_brien/sakana-fugu-new-model-from-sakana-ai-55ko

Sakana AI released **Fugu**, a compact bilingual model optimized for Japanese and English tasks. The project first gained traction on [Hacker News](https://sakana.ai/fugu/) with 142 points and 83 comments.

> **Model:** Sakana Fugu | **Parameters:** 7B | **Speed:** 38 tokens/s | **License:** Apache 2.0

## What It Is and How It Works

Fugu combines a 7B transformer backbone with Sakana’s evolutionary model merging technique. The model was trained on a 120B token mix of Japanese web text and English technical corpora. It supports both text generation and lightweight instruction following without separate fine-tunes.

The architecture uses grouped-query attention and a 32k context window. No external retrieval is required for standard prompts.


![Sakana Fugu: New Model from Sakana AI](https://static0.makeuseofimages.com/wordpress/wp-content/uploads/wm/2026/03/lm-studio-running-on-windows-11-laptop-with-chat-open.JPG?w=1600&h=900&fit=crop)

## Benchmarks and Performance Numbers

Early testers report **38 tokens per second** on an RTX 4090 at 4-bit quantization. Memory footprint sits at 4.1 GB. On Japanese-to-English translation, Fugu scores 41.2 BLEU on the JESC test set.

| Feature          | Sakana Fugu | Llama-3-8B | Qwen2-7B |
|------------------|-------------|------------|----------|
| Tokens/s (4090)  | 38          | 31         | 34       |
| Japanese BLEU    | 41.2        | 28.7       | 37.9     |
| VRAM (4-bit)     | 4.1 GB      | 5.2 GB     | 4.8 GB   |
| License          | Apache 2.0  | Llama 3    | Apache 2.0 |

## How to Try It

Download the weights from the official repository and run with llama.cpp or vLLM.

```bash
git clone https://github.com/sakana-ai/fugu
cd fugu && pip install -r requirements.txt
python -m fugu.chat --model fugu-7b-q4
```

An Ollama tag is also available: `ollama run sakana/fugu`.

## Pros and Cons

- Strong Japanese performance at small size
- Apache 2.0 license allows commercial use
- Runs on consumer GPUs with low VRAM
- Limited English reasoning compared with larger models
- No built-in tool-calling or agent scaffolding yet

## Alternatives and Comparisons

Llama-3-8B and Qwen2-7B remain the main local alternatives. Fugu leads on Japanese benchmarks while trailing slightly on English MMLU. Developers needing bilingual output without 20+ GB VRAM now have a clear third option.

## Who Should Use This

Researchers and developers building Japanese-facing chatbots or translation tools will benefit most. Teams focused solely on English reasoning or multi-agent workflows should continue with larger general models.

## Bottom Line / Verdict

Fugu gives practitioners a practical, Apache-licensed model that closes the Japanese performance gap at 7B scale.

Sakana’s merging approach suggests further small, high-quality bilingual models will follow within months.