Best of NVIDIA — November 2025

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   Article

   InfoWorld·29w

   Perplexity’s open-source tool to run trillion-parameter models without costly upgrades

   Perplexity AI released TransferEngine, an open-source tool that enables trillion-parameter language models to run across different cloud providers' GPU hardware at full speed. The software solves vendor lock-in by creating a universal interface for GPU-to-GPU communication that works on both Nvidia ConnectX and AWS EFA networking protocols. This allows companies to run massive models like DeepSeek V3 and Kimi K2 on older H100 and H200 systems instead of purchasing expensive next-generation hardware. TransferEngine achieves 400 Gbps throughput using RDMA technology and is already powering Perplexity's production AI search engine, handling disaggregated inference, reinforcement learning, and Mixture-of-Experts routing.

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   Article

   NVIDIA Developer·28w

   Release v1.10.0 · NVIDIA/warp

   NVIDIA Warp v1.10.0 introduces experimental JAX automatic differentiation support and multi-device compatibility with jax.pmap(). The release enhances tile programming with axis-specific reductions and component-level indexing, while delivering significant performance improvements including up to 70× faster built-in function calls from Python and in-place BVH rebuilding with CUDA graph support. New features include negative array indexing, atomic bitwise operations, and error functions. The warp.sim module has been removed after deprecation, with users directed to migrate to the Newton physics engine.

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   Article

   Where's Your Ed At·26w

   The Hater's Guide To NVIDIA

   NVIDIA dominates the AI hardware market by selling increasingly expensive GPUs (from $10,000 A100s to $30,000+ B200s) that power large language models. The company's success depends on customers—primarily Microsoft, Google, Meta, and Amazon—continuously purchasing new GPU generations, often funded through massive debt. Building a small 25MW AI data center costs over $1 billion, with $600 million for GPUs alone, plus 20 acres of land and 6-18 months of construction. Despite NVIDIA's $50+ billion quarterly revenue and 8% weight in the S&P 500, the underlying economics appear unsustainable: AI companies generate only ~$61 billion in revenue annually while spending hundreds of billions on infrastructure, with no clear path to profitability.

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   Video

   Theo - t3․gg·26w

   NVIDIA's first real competition (Google is KILLING it)

   Google announced its seventh-generation TPU (Ironwood), claiming 10x performance improvements over previous versions and positioning itself as a serious competitor to Nvidia in AI accelerator hardware. Meta is reportedly in talks with Google for a multi-billion dollar chip deal starting in 2027, causing Nvidia's stock to drop 4% and wiping $112 billion off its market cap. Google is the only major tech company operating across all AI layers: applications (Google Search), foundation models (Gemini), cloud inference (GCP), and custom accelerator hardware (TPUs). The move represents a strategic shift as companies seek alternatives to Nvidia's dominant position and pricing in the GPU market, with Google leveraging its vertical integration and custom silicon expertise to challenge the status quo.

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