News from the AI & ML world
DeeperML - #cloudcomputing
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@www.nextplatform.com
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References:
AWS News Blog
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Nvidia's latest Blackwell GPUs are rapidly gaining traction in cloud deployments, signaling a significant shift in AI hardware accessibility for businesses. Amazon Web Services (AWS) has announced its first UltraServer supercomputers, which are pre-configured systems powered by Nvidia's Grace CPUs and the new Blackwell GPUs. These U-P6e instances are available in full and half rack configurations and leverage advanced NVLink 5 ports to create large shared memory compute complexes. This allows for a memory domain spanning up to 72 GPU sockets, effectively creating a massive, unified computing environment designed for intensive AI workloads.
Adding to the growing adoption, CoreWeave, a prominent AI cloud provider, has become the first to offer NVIDIA RTX PRO 6000 Blackwell GPU instances at scale. This move promises substantial performance improvements for AI applications, with reports of up to 5.6x faster LLM inference compared to previous generations. CoreWeave's commitment to early deployment of Blackwell technology, including the NVIDIA GB300 NVL72 systems, is setting new benchmarks in rack-scale performance. By combining Nvidia's cutting-edge compute with their specialized AI cloud platform, CoreWeave aims to provide a more cost-efficient yet high-performing alternative for companies developing and scaling AI applications, supporting everything from training massive language models to multimodal inference. The widespread adoption of Nvidia's Blackwell GPUs by major cloud providers like AWS and specialized AI platforms like CoreWeave underscores the increasing demand for advanced AI infrastructure. This trend is further highlighted by Nvidia's recent milestone of becoming the world's first $4 trillion company, a testament to its leading role in the AI revolution. Moreover, countries like Indonesia are actively pursuing sovereign AI goals, partnering with companies like Nvidia, Cisco, and Indosat Ooredoo Hutchison to establish AI Centers of Excellence. These initiatives aim to foster localized AI research, develop local talent, and drive innovation, ensuring that nations can harness the power of AI for economic growth and digital independence. Recommended read:
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Chris McKay@Maginative
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Snowflake is aggressively expanding its footprint in the cloud data platform market, moving beyond its traditional data warehousing focus to become a comprehensive AI platform. This strategic shift was highlighted at Snowflake Summit 2025, where the company showcased its vision of empowering business users with advanced AI capabilities for data exploration and analysis. A key element of this transformation is the recent acquisition of Crunchy Data, a move that brings enterprise-grade PostgreSQL capabilities into Snowflake’s AI Data Cloud. This acquisition is viewed as both a defensive and offensive maneuver in the competitive landscape of cloud-native data intelligence platforms.
The acquisition of Crunchy Data for a reported $250 million marks a significant step in Snowflake’s strategy to enable more complex data pipelines and enhance its AI-driven data workflows. Crunchy Data's expertise in PostgreSQL, a well-established open-source database, provides Snowflake with a FedRAMP-compliant, developer-friendly, and AI-ready database solution. Snowflake intends to provide enhanced scalability, operational governance, and performance tooling for its wider enterprise client base by incorporating Crunchy Data's technology. This strategy is meant to address the need for safe and scalable databases for mission-critical AI applications and also places Snowflake in closer competition with Databricks. Furthermore, Snowflake introduced new AI-powered services at the Summit, including Snowflake Intelligence and Cortex AI, designed to make business data more accessible and actionable. Snowflake Intelligence enables users to query data in natural language and take actions based on the insights, while Cortex AISQL embeds AI operations directly into SQL. These initiatives, coupled with the integration of Crunchy Data’s PostgreSQL capabilities, indicate Snowflake's ambition to be the operating system for enterprise AI. By integrating such features, Snowflake is trying to transform from a simple data warehouse to a fully developed platform for AI-native apps and workflows, setting the stage for further expansion and innovation in the cloud data space. Recommended read:
References :
Chris McKay@Maginative
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Snowflake has announced the acquisition of Crunchy Data, a leading provider of enterprise-grade PostgreSQL solutions. This strategic move is designed to enhance Snowflake's AI Data Cloud by integrating robust PostgreSQL capabilities, making it easier for developers to build and deploy AI applications and agentic systems. The acquisition brings approximately 100 employees from Crunchy Data into Snowflake, signaling a significant expansion of Snowflake's capabilities in the database realm. This positions Snowflake to better compete with rivals like Databricks in the rapidly evolving AI infrastructure market, driven by the increasing demand for databases that can power AI agents.
This acquisition comes amidst a "PostgreSQL gold rush," as major platforms recognize the critical role of the data layer in feeding AI agents. Just weeks prior, Databricks acquired Neon, another Postgres startup, and other companies like Salesforce and ServiceNow have also made acquisitions in the data management space. Snowflake's SVP of Engineering, Vivek Raghunathan, highlighted the massive $350 billion market opportunity, underscoring the trend where AI agents, rather than humans, are increasingly driving database usage. PostgreSQL's popularity among developers and its suitability for rapid, automated provisioning make it an ideal choice for AI agent demands. Crunchy Data brings enterprise-grade operational database capabilities that complement Snowflake's existing strengths. While Snowflake has excelled in analytical workloads involving massive datasets, it has been comparatively weaker on the transactional side, where real-time data storage and retrieval are essential. Crunchy Data's expertise in enterprise and regulated markets, including federal agencies and financial institutions, aligns well with Snowflake's existing customer base. The integration of Crunchy Data's PostgreSQL capabilities will enable Snowflake to provide a more comprehensive solution for organizations looking to leverage AI in their operations. Recommended read:
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Berry Zwets@Techzine Global
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Snowflake has unveiled a significant expansion of its AI capabilities at its annual Snowflake Summit 2025, solidifying its transition from a data warehouse to a comprehensive AI platform. CEO Sridhar Ramaswamy emphasized that "Snowflake is where data does more," highlighting the company's commitment to providing users with advanced AI tools directly integrated into their workflows. The announcements showcase a broad range of features aimed at simplifying data analysis, enhancing data integration, and streamlining AI development for business users.
Snowflake Intelligence and Cortex AI are central to the company's new AI-driven approach. Snowflake Intelligence acts as an agentic experience that enables business users to query data using natural language and take actions based on the insights they receive. Cortex Agents, Snowflake’s orchestration layer, supports multistep reasoning across both structured and unstructured data. A key advantage is governance inheritance, which automatically applies Snowflake's existing access controls to AI operations, removing a significant barrier to enterprise AI adoption. In addition to Snowflake Intelligence, Cortex AISQL allows analysts to process images, documents, and audio within their familiar SQL syntax using native functions. Snowflake is also addressing legacy data workloads with SnowConvert AI, a new tool designed to simplify the migration of data, data warehouses, BI reports, and code to its platform. This AI-powered suite includes a migration assistant, code verification, and data validation, aiming to reduce migration time by half and ensure seamless transitions to the Snowflake platform. Recommended read:
References :
@thetechbasic.com
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Microsoft has announced major layoffs affecting approximately 6,000 employees, which is equivalent to 3% of its global workforce. This move is part of a broader strategic shift aimed at streamlining operations and boosting the company's focus on artificial intelligence (AI) and cloud computing. The layoffs are expected to impact various divisions, including LinkedIn, Xbox, and overseas offices. The primary goal of the restructuring is to position Microsoft for success in a "dynamic marketplace" by reducing management layers and increasing agility.
The decision to implement these layoffs comes despite Microsoft reporting strong financial results for FY25 Q3, with $70.1 billion in revenue and a net income of $25.8 billion. According to Microsoft CFO Amy Hood, the company is focused on “building high-performing teams and increasing our agility by reducing layers with fewer managers". The cuts also align with a recurring trend across the industry, with firms eliminating staff who do not meet expectations. Microsoft's move to prioritize AI investments is costing the company a significant number of jobs. Microsoft is following a trend of other technology companies that are investing heavily in AI, the company has been pouring billions into AI tools and cloud services. The company's cloud service, Azure, is expanding at a rapid rate and the company aims to inject more money into this region. Recommended read:
References :
@the-decoder.com
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Google has announced implicit caching in Gemini 2.5, a new feature designed to significantly reduce developer costs. The company aims to cut costs by as much as 75 percent by automatically applying a 75% cached token discount. This is a substantial improvement over previous methods, where developers had to manually configure caching. The new implicit caching automatically detects and stores recurring content, ensuring that repeated prompts are only processed once, which can lead to substantial cost savings.
The new feature is particularly beneficial for applications that run prompts against the same long context or continue existing conversations. Google recommends placing the stable part of a prompt, such as system instructions, at the start and adding user-specific input, like questions, afterwards to maximize benefits. Implicit caching kicks in for Gemini 2.5 Flash starting at 1,024 tokens, and for Pro versions from 2,048 tokens onwards. This functionality is now live, and developers can find more details and best practices in the Gemini API documentation. This development builds on the overwhelmingly positive feedback to Gemini 2.5 Pro’s coding and multimodal reasoning capabilities. Beyond UI-focused development, these improvements extend to other coding tasks such as code transformation, code editing and developing complex agentic workflows. Simon Willison notes that Gemini 2.5 now applies the 75% cached token discount automatically, which he considers a potentially big cost saving for applications that run prompts against the same long context or continue existing conversations. Recommended read:
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