{
  "id": 8885616,
  "title": "Yandex open-sourced an 80B model trained from scratch: what's inside and where it wins",
  "url": "https://urgent.news/2026/09/21/yandex-open-sourced-an-80b-model-trained-from-scratch-whats-inside",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-21T08:45:41.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/klukyanov/yandex-open-sourced-an-80b-model-trained-from-scratch-whats-inside-and-where-it-wins-3c3g"
  },
  "original_language": "en",
  "account": "Yandex has released an 80 billion parameter language model called AliceAI-Foundation-80B-A3B-Base, trained entirely from scratch and released under Apache 2.0. This model uses a MoE architecture with 512 experts and context size of 262,144 tokens, offering superior performance in Russian language tasks, math, code, and long context tasks compared to its predecessor and similar-sized models. However, the full 80B model requires 160GB of memory in bf16, making it challenging to run locally. It is intended as a base model for further instruction tuning, and lacks chatbot capabilities. The open-source release provides a valuable foundation for Russian-language applications, particularly in legal and educational domains.",
  "summary": "Yandex just open-sourced a language model it trained entirely from scratch — no borrowed weights, no initialization from Qwen or Llama. It's called AliceAI-Foundation-80B-A3B-Base , it's on Hugging Face under Apache 2.0, and it's a surprisingly interesting release if you care about MoE architecture or non-English models. Here's what's inside, where it actually wins, and where the benchmark table…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}