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Kimi K3's 2.8 Trillion Parameters and 64-Chip Deployment May Strengthen Nvidia GPU and HBM Demand

GateNews
NVDA-2.32%
According to SemiAnalysis, Kimi K3 with over 2.8 trillion parameters requires a 64-chip deployment architecture and over 1.5TB of HBM capacity. Contrary to market concerns that linear attention mechanisms weaken high-end AI hardware demand, the research firm said K3's scale and inference architecture may actually strengthen demand for Nvidia GPUs, HBM, and interconnect equipment. SemiAnalysis noted that even with limited user concurrency, KV cache requires significant offloading to CPU DDR5 memory and NVMe storage, leaving limited HBM headroom. The firm believes more efficient model architectures lower AI inference costs, driving broader application adoption and long-term demand for GPU, HBM, DRAM, and network infrastructure.
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