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Home/Open Source
Open Source

Ricursive AI Releases Ripple 65B for Chip Design

Sam Witteveen·November 5, 2026·high confidence

Why it matters

  • →Ripple 65B demonstrates that specialized models can outperform general-purpose open LLMs in niche technical domains.
  • →The $32M training cost suggests a viable path for smaller labs to compete with major players using focused data strategies.
  • →Apache 2.0 licensing makes the model immediately usable for commercial semiconductor engineering workflows.
Ricursive AI Releases Ripple 65B for Chip Design
©Sam Witteveen

Ricursive AI has launched Ripple 65B, an open-source large language model designed specifically for chip design tasks. The company, which previously focused on creating models for semiconductor engineering, released the weights under the Apache 2.0 license on Hugging Face. Ripple 65B reportedly outperforms existing US-based open models in its target domain, achieved through a 'thin and deep' architecture and a training budget of approximately $32 million. The release highlights a growing trend toward vertical-specific AI solutions that leverage specialized datasets to achieve superior performance in narrow industrial applications.

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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.

OpenAI's Jalapeño Chip Boosts AI Inference Efficiency — OpenAI1OpenAI unveils Jalapeño AI chip, outperforms Nvidia — The Rundown AI2Fine-tuning 350M Model for Structured Outputs — Hugging Face Blog3Liquid AI releases DSpark drafter for vision models — Hugging Face Blog4MIT documents AI hardware evolution over eight years — MIT News AI5Ricursive Intelligence bets on AI-designed chips — TechCrunch AI6General Compute raises $100M Series A for inference chips — TechCrunch AI7Together AI automates inference optimization with agents — Together AI Blog8Ricursive AI Releases Ripple 65B for Chip DesignMETR: Anthropic's AI progress doubled internally — The Rundown AI9Open Chinese AI Models Match Frontier Labs in Hacking — WIRED AI10Aug 25You are hereNov 6

How we got here

  1. 1
    OpenAI's Jalapeño Chip Boosts AI Inference Efficiency

    OpenAI · August 25, 2026 · Background

  2. 2
    OpenAI unveils Jalapeño AI chip, outperforms Nvidia

    The Rundown AI · August 26, 2026 · Background

  3. 3
    Fine-tuning 350M Model for Structured Outputs

    Hugging Face Blog · September 3, 2026 · Background

  4. 4
    Liquid AI releases DSpark drafter for vision models

    Hugging Face Blog · September 24, 2026 · Background

  5. 5
    MIT documents AI hardware evolution over eight years

    MIT News AI · October 6, 2026 · Background

  6. 6
    Ricursive Intelligence bets on AI-designed chips

    TechCrunch AI · October 15, 2026 · Related

  7. 7
    General Compute raises $100M Series A for inference chips

    TechCrunch AI · October 27, 2026 · Background

  8. 8
    Together AI automates inference optimization with agents

    Together AI Blog · November 4, 2026 · Background

What happened next

  1. 9
    METR: Anthropic's AI progress doubled internally

    The Rundown AI · November 6, 2026 · Background

  2. 10
    Open Chinese AI Models Match Frontier Labs in Hacking

    WIRED AI · November 6, 2026 · Background

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BAAI Releases AREX-2 Agent Model

BAAI’s AREX-2 shifts the burden of agent reliability from external orchestration to the model itself. Fine-tuned on Qwen3.8-27B, this open-weights model is explicitly trained to execute the try-check-retry loop internally rather than relying on a separate harness to manage failures. This architectural choice targets long-horizon tasks where external loops often break down under complexity. By embedding reflection and self-correction directly into the weights, it offers a more robust path for autonomous coding agents that need to recover from errors without human intervention.

Sam Witteveen·Nov 6, 2026

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