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Cerebras proposes the concept of "latency debt": model parameters have grown from 340M in BERT-Large to over 1T in Kimi-k2, the share of inference tokens has risen from under 10% to over 50%, context has expanded from about 1,000 tokens in GPT-2 to 10 million tokens, input length has doubled every year and output has tripled, causing latency to accumulate continuously. Cerebras says its Wafer-Scale Engine (WSE) keeps data on-chip, eliminating the memory bandwidth bottleneck of GPU inference, and OpenAI has already purchased 750 MW of compute from it.
Cerebras proposes the concept of "latency debt": model parameters have grown from 340M in BERT-Large to over 1T in Kimi-k2, the share of inference tokens has risen from under 10% to over 50%, context has expanded from about 1,000 tokens in GPT-2 to 10 million tokens, input length has doubled every year and output has tripled, causing latency to accumulate continuously. Cerebras says its Wafer-Scale Engine (WSE) keeps data on-chip, eliminating the memory bandwidth bottleneck of GPU inference, and
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The Baseten engineering blog has released new MCP and Skills for coding agents, enabling coding agents to use Baseten directly. The blog also covers how to run any open-source model in DeepSeek Harness, fine-tuning Qwen3-TTS for high-quality voice cloning, and running Kimi K3, GLM-5.2, and more in any harness.
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Global data center power demand is projected to exceed 200 GW by 2030, and AI workloads are pushing typical rack densities from about 10-30 kW to 80-150 kW, with advanced deployments approaching 1 MW per rack. At Data Center World Power in Dallas, utilities, hyperscalers and developers discussed how to expand power supply while safeguarding grid reliability, increasingly adopting hybrid solutions combining the grid with on-site generation and energy storage.
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