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Sunday

  1. TechCrunch AI

    AWS CEO responds to data center controversy: no longer using NDAs

    Amazon Web Services CEO Matt Garman said in a blog post that the company no longer uses non-disclosure agreements (NDAs) when partnering with government agencies to build data centers. He also rebutted four claims about data centers—that they consume too much water, drive up electricity prices, cause serious pollution, and bring no benefit to communities—saying that direct water use by data centers accounts for only 0.5% of U.S. industrial water use, that backup generators sit idle 99.9% of the time, and that over the past three years Amazon has invested more than $1 billion in U.S. communities where it has data centers.

    Amazon Web Services CEO Matt Garman said in a blog post that the company no longer uses non-disclosure agreements (NDAs) when partnering with government agencies to build data centers. He also rebutted four claims about data centers—that they consume too much water, drive up electricity prices, cause serious pollution, and bring no benefit to communities—saying that direct water use by data centers accounts for only 0.5% of U.S. industrial water use, that backup generators sit idle 99.9% of the

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    #tech-media#Industry news#OpenAI#Anthropic

Saturday

Friday

Thursday

  3. A6 a6-051 · Cerebras：Blog

    Cerebras: Latency Debt Is Dragging Down AI Experience, GPUs Have Hit a Bottleneck

    Cerebras proposes the concept of "latency debt": model parameters have grown from 340M in BERT-Large to over 1T in Kimi-k2, the share of inference tokens has risen from under 10% to over 50%, context has expanded from about 1,000 tokens in GPT-2 to 10 million tokens, input length has doubled every year and output has tripled, causing latency to accumulate continuously. Cerebras says its Wafer-Scale Engine (WSE) keeps data on-chip, eliminating the memory bandwidth bottleneck of GPU inference, and OpenAI has already purchased 750 MW of compute from it.

    Cerebras proposes the concept of "latency debt": model parameters have grown from 340M in BERT-Large to over 1T in Kimi-k2, the share of inference tokens has risen from under 10% to over 50%, context has expanded from about 1,000 tokens in GPT-2 to 10 million tokens, input length has doubled every year and output has tripled, causing latency to accumulate continuously. Cerebras says its Wafer-Scale Engine (WSE) keeps data on-chip, eliminating the memory bandwidth bottleneck of GPU inference, and

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    #a6-editorial#Industry news#English text is being prepared.#Reasoning
  5. A6 a6-047 · Baseten Engineering Blog

    Baseten AI Engineering Blog: MCP and Skills, DeepSeek Harness, Qwen3-TTS Fine-Tuning, and More

    The Baseten engineering blog has released new MCP and Skills for coding agents, enabling coding agents to use Baseten directly. The blog also covers how to run any open-source model in DeepSeek Harness, fine-tuning Qwen3-TTS for high-quality voice cloning, and running Kimi K3, GLM-5.2, and more in any harness.

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    #a6-editorial#English text is being prepared.

Tuesday

  1. Data Center Knowledge

    The 200 GW Moment: Reshaping the Power Grid for the AI Economy

    Global data center power demand is projected to exceed 200 GW by 2030, and AI workloads are pushing typical rack densities from about 10-30 kW to 80-150 kW, with advanced deployments approaching 1 MW per rack. At Data Center World Power in Dallas, utilities, hyperscalers and developers discussed how to expand power supply while safeguarding grid reliability, increasingly adopting hybrid solutions combining the grid with on-site generation and energy storage.

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    #hot-signal#Industry news#English text is being prepared.#English text is being prepared.