Tag: ai-infrastructure
All the articles with the tag "ai-infrastructure".
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Open Weights Catch the Last Frontier, Not the Moving One
Across 19 model snapshots, open-weight leaders reached an earlier proprietary frontier in 24–58 days on coding, intelligence, and agentic benchmarks—but remained behind the live frontier.
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The AI Buildout Is Constrained by Its Slowest Layer
Updated:AI capacity becomes real only when chips, racks, cooling, networks, electrical equipment, grid power, permits, and operators arrive together.
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The AI Chips After NVIDIA
Updated:The serious AI chip challengers are not building one replacement for NVIDIA. They are attacking different bottlenecks in memory, latency, power, networking, and software.
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Groq Makes the Compiler Part of the Processor
Groq moves scheduling and data placement from reactive hardware into the compiler, turning a network of SRAM-heavy LPUs into the useful unit of inference capacity.
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d-Matrix Moves the Math Into Memory
d-Matrix treats inference as a memory-placement problem, combining digital in-memory compute, an SRAM performance tier, LPDDR capacity, and standard data-center fabrics.
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The AI Rack Became a Data Center Design Problem
Blackwell moved NVIDIA's top-end scale-up domain from an eight-GPU server into a liquid-cooled rack, making power, cooling, networking, and commissioning part of the computer.
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Cerebras Moved the Cluster Boundary Onto a Wafer
Cerebras removes many chip boundaries with wafer-scale integration, then rebuilds the system around distributed SRAM, streamed weights, rack-scale I/O, and a compiler.
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The Interconnect Determines How Much of the Chip You Can Use
At rack and pod scale, accelerator utilization depends on scale-up fabrics, scale-out networks, collective software, congestion control, and the physical path carrying every byte.