# Nvidia, ASML Plunge as DeepSeek Triggers Tech Stock Selloff

- **Nvidia and ASML shares fell sharply** as the Chinese AI startup DeepSeek demonstrated competitive performance against Western chatbots at a significantly lower cost, raising concerns about the sustainability of the current AI business model reliant on high-end chips.

- **Qwen2.5-1M models** introduce **two new checkpoints** capable of processing **1 million tokens**, significantly enhancing long-context capabilities compared to previous versions.

- **Nvidia's stock faces significant challenges** as competition intensifies from innovative architectures like Cerebras and Groq, which threaten its data center dominance and high margins, while major customers develop custom silicon to reduce reliance on Nvidia's products.

- **Nvidia's stock plummeted 17%**, resulting in a staggering **$600 billion loss in market cap**, marking the largest single-day drop for any U.S. company, driven by fears of competition from China's AI lab DeepSeek.

- **DeepSeek's training efficiency is 45x greater**, suggesting they could have led the LLM market, yet they opted for open-sourcing, likely to foster collaboration and innovation within the community.

- The **Janus Pro Technical Report** details advancements in AI technology, specifically focusing on **enhanced inference capabilities** and **model efficiency** that significantly improve performance in various applications.

- **Janus-Pro** is an innovative autoregressive framework that enhances **multimodal understanding and generation** by decoupling visual encoding, allowing for improved flexibility and performance compared to previous models.

- **Notion** serves as a **comprehensive workspace** that integrates notes, tasks, wikis, and databases, enhancing productivity through its versatile features and user-friendly interface.

- **AVX-512 instruction** enables a **phrase search algorithm** that outperforms Meilisearch by up to **1600x**, showcasing significant advancements in computational efficiency for phrase searches in large datasets. The source code is available [here](https://github.com/Gab-Menezes/simdphrase) and the benchmarking methodology is detailed in the article.

- **ErisForge** is a Python library that enables the modification of **Large Language Models (LLMs)** by transforming their internal layers, allowing for the creation of both ablated and augmented model versions tailored to specific inputs.

- **Fast Think-on-Graph (FastToG)** enhances large language models (LLMs) by enabling them to reason **"community by community"** within knowledge graphs (KGs), addressing limitations of existing Graph Retrieval Augmented Generation (GRAG) systems.

- **DeepSeek-R1** is a groundbreaking open weights model that excels in reasoning tasks by utilizing a unique training method that incorporates long chains of reasoning and reinforcement learning, resulting in a model capable of generating detailed thought processes.

- **Flash Attention 2** significantly enhances transformer inference speed, but requires careful installation using a `-devel` Docker container and the `ninja` build tool for optimal performance.

- **ZETA** introduces a novel method for **top-k attention** by utilizing **Z-order curves**, enabling efficient parallel querying of past tokens, which significantly enhances training efficiency for long sequences.