Arthur Hayes:为了创作真实的数字艺术,我开发出Ordinals项目Airheads

长文源:区块律动Pubblicato 2002-08-24Pubblicato ultima volta 2024-08-02

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CPU, Quietly Returning to the Center of the AI Computing Power Stage

Over the past three years, AI computing power narratives have been dominated by GPUs. However, starting in 2026, this story began to shift. While training large models remains GPU-intensive, the rapid growth of inference and AI agent workloads, which require high levels of task orchestration, concurrency, and data flow management, has highlighted a renewed critical role for CPUs. These are tasks GPUs are not designed to handle. Intel's recent launch of the Xeon 6+ processor, built on its Intel 18A process and featuring up to 288 efficiency cores (E-cores), exemplifies this strategic pivot. It is positioned not as a mere companion to GPUs but as the essential "control plane" for AI infrastructure, optimized for high-density, energy-efficient, and high-throughput workloads characteristic of AI agents and inference. This "CPU resurgence" is not about CPUs outperforming GPUs in raw computation. It reflects a systemic bottleneck: as AI scales from training single models to deploying countless intelligent agents, the demand for coordination and data handling surges. Major cloud providers are also developing their own high-density ARM-based server CPUs for similar workloads. However, Intel's success with this strategy faces significant challenges. Competition includes NVIDIA's integrated CPU-GPU solutions, the expanding adoption of cloud vendors' in-house ARM CPUs, and the crucial market test of Intel's 18A manufacturing process against rivals like TSMC's N2. In conclusion, CPUs are indeed reclaiming a central, though redefined, role in AI compute—managing the complex orchestration that enables massive-scale AI deployment. While the trend is clear, which company will ultimately lead this CPU resurgence remains an open question to be decided in the data centers of 2027 and beyond.

marsbit5 min fa

CPU, Quietly Returning to the Center of the AI Computing Power Stage

marsbit5 min fa

After Collaborating with 35+ DeFi Projects, Pink Brains Discovers the New 2026 KOL Marketing Rules

After collaborating with over 35 leading DeFi projects on marketing over three years, Pink Brains identifies a key shift for effective marketing in 2026: prioritizing the user journey over traditional campaign tactics. The most effective marketing mirrors how users actually behave—starting with discovery on social platforms like X (formerly Twitter), followed by data-driven verification on sites like DefiLlama, and finally, participation with small test funds. Success hinges on genuine, verifiable mechanisms, not just marketing hype. Current user interest centers on several key themes: new DeFi trends (RWA, perps, crypto x AI), meaningful airdrops requiring real contributions, real yield from protocol revenue, and tokens with value capture mechanisms directly tied to product usage. Case studies like Hyperliquid's HYPE (with its aggressive buyback program) and Venice's VVV (linking demand to AI compute) exemplify how strong tokenomics foster user retention. New trading venues like prediction markets, collectibles platforms, and GambleFi are also gaining traction, driven by verifiable activity. The article outlines common mistakes in DeFi KOL marketing, such as using creators unfamiliar with the product, generic messaging, or relying on a few top-tier KOLs. Instead, effective strategies align with different KOL types—educators, content creators, airdrop hunters, and niche experts—for various stages of the user journey. Ultimately, long-term user retention depends on a combination of a genuinely useful product, responsive support, community-aligned tokenomics, and strategic community building. The core takeaway is that sustainable growth stems from products whose value is validated by data and real-world utility, not just promotional efforts.

marsbit24 min fa

After Collaborating with 35+ DeFi Projects, Pink Brains Discovers the New 2026 KOL Marketing Rules

marsbit24 min fa

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