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Home/Models & Labs
Models & Labs

JetBrains Releases Mellum2: Efficient 12B MoE Model

Hugging Face Blog·June 1, 2026·high confidence

Why it matters

  • →Mellum2 offers significant efficiency improvements for latency-sensitive AI tasks.
  • →The model's open-source nature allows for flexible deployment in private environments.
  • →It exemplifies a shift towards specialized models that enhance larger AI systems.
JetBrains Releases Mellum2: Efficient 12B MoE Model
©Hugging Face Blog

JetBrains has introduced Mellum2, a 12 billion parameter Mixture-of-Experts model optimized for low-latency text and code workloads. The model activates only 2.5 billion parameters per token, achieving over twice the inference speed of comparable models. Mellum2 is open-source under the Apache 2.0 license and is designed for high-throughput tasks in software engineering, such as code generation and summarization. This release highlights a trend towards specialized models that improve the efficiency and control of AI systems without replacing existing components.

Read original

The story around this

Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.

New Method Enhances LLM Training Efficiency — MIT News AI1New CPD Architecture Boosts LLM Serving Speed — Together AI Blog2Hugging Face Unveils EMO MoE Model — Hugging Face Blog3Together AI's Inference Engine Outperforms Competitors — Together AI Blog

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4llama.cpp b9271 Release Enhances Efficiency — llama.cpp Releases5vLLM v0.22.0 Release Enhances Model Performance — vLLM Releases6JetBrains Releases Mellum2: Efficient 12B MoE ModelLlama.cpp b9626 Release Adds Cohere2-MoE Support — llama.cpp Releases7Subquadratic claims breakthrough in LLM efficiency — MIT Technology Review AI8Startups Innovate Beyond Transformers in LLMs — MIT Technology Review AI9Granite 4.2 LLMs Introduced by IBM and Hugging Face — Hugging Face Blog10GLM 5.3 Flash: Z.AI's Cost-Efficient Model Variant — Sam Witteveen11Feb 26You are hereAug 30

How we got here

  1. 1
    New Method Enhances LLM Training Efficiency

    MIT News AI · February 26, 2026 · Background

  2. 2
    New CPD Architecture Boosts LLM Serving Speed

    Together AI Blog · March 4, 2026 · Background

  3. 3
    Hugging Face Unveils EMO MoE Model

    Hugging Face Blog · May 8, 2026 · Related

  4. 4
    Together AI's Inference Engine Outperforms Competitors

    Together AI Blog · May 19, 2026 · Background

  5. 5
    llama.cpp b9271 Release Enhances Efficiency

    llama.cpp Releases · May 22, 2026 · Related

  6. 6
    vLLM v0.22.0 Release Enhances Model Performance

    vLLM Releases · May 31, 2026 · Background

What happened next

  1. 7
    Llama.cpp b9626 Release Adds Cohere2-MoE Support

    llama.cpp Releases · June 14, 2026 · Related

  2. 8
    Subquadratic claims breakthrough in LLM efficiency

    MIT Technology Review AI · June 19, 2026 · Related

  3. 9
    Startups Innovate Beyond Transformers in LLMs

    MIT Technology Review AI · August 10, 2026 · Related

  4. 10
    Granite 4.2 LLMs Introduced by IBM and Hugging Face

    Hugging Face Blog · August 25, 2026 · Related

  5. 11
    GLM 5.3 Flash: Z.AI's Cost-Efficient Model Variant

    Sam Witteveen · August 30, 2026 · Related

Follow this story

Open the full story →

JetBrains Releases Mellum2: Efficient 12B MoE Model

2 developments

  1. Jun 1 · Hugging Face Blog
    JetBrains Releases Mellum2: Efficient 12B MoE Model (This article)
  2. Jun 5 · Lev Selector
    JetBrains Releases Mellum2 12B MoE Open-source
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Apple is finally unlocking its AI suite for Chinese users with the iOS 27.3 beta, marking a significant regulatory milestone after months of approval delays. The rollout relies on Alibaba’s Qwen model rather than OpenAI, integrating features like notification summaries and visual intelligence directly into the OS. By sidestepping the new Siri in favor of established text and image tools, Apple is prioritizing immediate utility over the full generative experience. This move effectively brings Apple’s AI capabilities to a massive new market, albeit with localized constraints that distinguish it from the global release.

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