# Sakana Namazu

Sakana AI and Modal put Sakana Namazu, a roughly 1-trillion-parameter mixture-of-experts model, into production API service at NVFP4 precision. Compression was applied only to the routed experts that hold most of the parameters, while attention, the router and the shared experts stayed at BF16, and calibration used Japanese-heavy conversations so the model matched its production checkpoint on Japanese instruction following. A custom draft model trained on more than 30 billion raw tokens raised speculative token acceptance by 25% over a generic drafter; a second drafter retrained on about 1 million real requests lifted average acceptance length from 3.51 to 5.01 and cut p99 latency from 68s to 52s across 1,000 held-out requests.

Sakana AI and Modal put Sakana Namazu, a roughly 1-trillion-parameter mixture-of-experts model, into production API service at NVFP4 precision. Compression was applied only to the routed experts that hold most of the parameters, while attention, the router and the shared experts stayed at BF16, and calibration used Japanese-heavy conversations so the model matched its production checkpoint on Japanese instruction following. A custom draft model trained on more than 30 billion raw tokens raised speculative token acceptance by 25% over a generic drafter; a second drafter retrained on about 1 million real requests lifted average acceptance length from 3.51 to 5.01 and cut p99 latency from 68s to 52s across 1,000 held-out requests.