Insights on GPU infrastructure, AI, and data centers.
Deploying a large language model used to require weeks of infrastructure work, custom optimization scripts, and a team of ML engineers who understood the dark arts of inference tuning. NVIDIA changed
Alibaba Cloud discovered their vGPU deployment achieving only 47% of bare-metal performance despite marketing claims of 95% efficiency, costing them $73 million in over-provisioned infrastructure to
The NVIDIA Blackwell Ultra GPU delivers 15 petaflops of dense FP4 compute, 50% more memory than the B200, and 1.5 times faster performance.¹ A single GB300 NVL72 rack achieves 1.1 exaflops of FP4
DeepSeek claims to have trained its R1 model for just $5.6 million using 2,000 NVIDIA H800 GPUs.¹ Comparable Western models required $80 million to $100 million and 16,000 H100 GPUs.² The January
Memory bottlenecks kill AI performance. Large language models routinely exceed 80 to 120GB per GPU for KV cache alone, overwhelming even the most expensive HBM-equipped accelerators.¹ Compute Express
Anthropic closed the largest TPU deal in Google's history in November 2025—committing to hundreds of thousands of Trillium TPUs in 2026, scaling toward one million by 2027.¹ The company that built
Meta achieved a 3.8x improvement in model training speed by implementing GPUDirect Storage across their research clusters, eliminating the CPU bottleneck that previously limited data loading to
A single GPT-3 inference request costs $0.06 at full precision but drops to $0.015 after optimization, a 75% reduction that transforms AI economics at scale. Model serving optimization techniques
AI PCs will represent 31% of the total PC market globally by the end of 2025, with shipments projected at 77.8 million units.¹ Eight out of ten IT decision makers plan to invest in AI PCs this year.²
Microsoft's Satya Nadella revealed a crucial insight about GPU infrastructure planning: "I didn't want to go get stuck with four or five years of depreciation on one generation."¹ The comment
Europe's largest economy faces a defining moment. Germany committed €5.5 billion to make AI account for 10% of domestic economic output by 2030.¹ Google announced €5.5 billion in German data center
The jump from 700W to 1200W GPU power consumption represents more than a 70% increase—it fundamentally breaks every assumption that guided data center design for the past decade, requiring
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