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All AI factories are data centers, but not all data centers are AI factories. For much of the last two decades, enterprise infrastructure strategy has been grounded in cost control. Data centers were built to support core IT functions—storage, compute, and networking—with an emphasis on efficiency,
ReadKey Takeaways on Google’s 1Q25 Earnings Sustained AI Momentum Across Products and Infrastructure: Gemini 2.2 is now embedded across 15 products with 500M+ users, while AI Overviews in Search surpassed 1.5B MAUs. Visual and voice-first inputs like Google Lens and Circle to Search are driving deepe
ReadKey Takeaways: Google Cloud unveiled Ironwood (TPUv7), its first inference-optimized TPU, offering massive leaps in compute (5x), HBM capacity (6x), and power efficiency (2x) over the previous generation. Performance is estimated to be within 5% of an Nvidia B200, despite TPUv7’s single primary comp
ReadWe are in a huge AI compute shortage right now. At this very moment! When you hear most people talk about this, it’s generally in regards to model scaling and compute scaling for training. Not enough compute so labs can’t scale up the scaling laws. Right now is different, it’s the Ghibilification of
ReadExecutive Summary Custom CPU development is essential for maximizing the efficiency and performance of accelerated computing architectures. NVIDIA’s strategic approach to CPU design, specifically its purpose-built Arm-based CPUs, delivers significant competitive advantages by addressing the limitati
ReadThe GPU vs. Custom ASIC debate is easily one of the most talked about dynamics regarding AI compute infrastructure. I have dug deeply into the debate in this article here (Understanding the Hyper Scaler Custom ASIC Strategy). There is no doubt this is a central question NVIDIA executives get from i
ReadA closer look at NVIDIA’s Q4 earnings call highlights key investor concerns that management tackled head-on throughout the discussion. The conversation sheds light on the company’s strategic direction and how the market perceives its leadership in the rapidly evolving AI infrastructure space. 1. Bla
ReadA few days ago, xAI announced Grok 3 and it’s easily the best LLM I’ve used. Good vibes, great quality, fast token generation, and happened to be trained on the most compute for any model (200,000 H100s). During the stream, Elon Musk said xAI is already working on Grok 4’s data center, which will be
ReadChina’s smartphone market, the world’s largest, could be at an inflection point not seen since the initial smartphone boom. In recent quarters, the market has shown signs of recovery, with local brands benefiting from a surge in premium offerings as more Chinese consumers are willing to spend on new
ReadI know it sounds off, but after spending some time getting back into gaming thanks to the RTX 5090, and then looking at some of the technical details on the RTX 5090, I’ve been sold on Nvidia’s vision of neural rendering and AI-accelerated rendering. Frankly, I think it’s the only way going forward.
ReadWhat’s Important: DLSS 4 introduces Multi Frame Generation, exclusive to RTX 50 Series GPUs, increasing frame rates up to 8x. A new transformer-based AI model replaces the convolutional neural network, improving stability, motion clarity, and reducing ghosting. Enhanced Ray Reconstruction is compati
ReadRecent first-party benchmarking data provides crucial insight into the cost-performance dynamics between custom ASICs and GPUs across both training and inference workloads. The data reveals a nuanced competitive landscape where ASICs demonstrate meaningful cost-performance advantages, particularly i
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