Tag: Compute Efficiency
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Meta’s AWS Deal Shows How Agentic AI Is Moving Onto Graviton Chips
Meta’s agreement with AWS to power agentic AI on Amazon’s Graviton chips is a practical signal for engineers: production AI is becoming a deployment and inference-efficiency problem, not just a model problem.
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Google’s New Eighth-Gen TPUs Aim at the Agentic Inference Bottleneck
Google’s latest TPU announcement is less about training headlines and more about serving AI efficiently at scale. The shift points to a new infrastructure priority: lower-cost, higher-throughput inference for agentic workloads.
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Fast, Thinking, or Pro? Navigating the New Gemini 3 Architecture
A practical guide to choosing the right Gemini mode for speed, reasoning depth, and heavier technical work.
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The Silicon Backbone: Why Custom ASICs Are Winning the Industrial AI Race
Why custom ASICs are becoming the better fit for industrial AI systems that need efficiency, determinism, and long service life.
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The Context Window Race: Why LLM ‘Memory’ is the New Frontier
Why larger context windows matter for AI systems that need to hold more of the real problem in memory without losing accuracy.
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The 2nm Bottleneck: Why Money Can’t Fix Physics
The next AI bottleneck is not just more demand for chips, but whether leading-edge manufacturing and advanced packaging can scale economically.