Open-weight and small models dominate as AI workloads shift to cheaper options
A healthcare technology company is shifting most AI workloads to open-weight and small models, achieving major cost savings while maintaining performance.
The KOSPI slid 3.5% at Tuesday's open, cutting its 2026 gain to about 31% from 116%; AI-spending worries and chip-stock losses have weighed on the index.
With the AI-driven sell-off in chip stocks still weighing, the KOSPI opened Tuesday 3.5% lower. Its 2026 advance has been cut to about 31% from approximately 116%, the level at its record close in June.
The benchmark currently stands roughly 39% under that high. The downturn picked up speed in July, when 22% was lost, and October had taken away another 17% by Oct. 16.
In May, Samsung Electronics and SK Hynix — both memory-chip makers — made up 44% of the benchmark.
From Oct. 1 through Oct. 16, the two stocks declined 19.74% and 28.38%, as reported by JoongAng. A drop in bond prices at cloud provider CoreWeave deepened worries about credit tied to AI spending.
Foreign money moved to net selling, and the KOSPI dropped for seven consecutive sessions up to Oct. 15.
US inflation figures that came in hotter than expected were released Oct. 15, and the benchmark went on to lose 7.57%, ending at 5,554.22.
On June 24, margin loans — borrowed funds used to buy stocks — reached a peak of 38.63 trillion won.
Four straight sessions through Oct. 16 saw forced selling by brokers — liquidations triggered when investors miss payment deadlines — exceed 50 billion won.
Kospi….
— Heisenberg (@Mr_Derivatives), October 20, 2026
It’s been rough lately. Remember a lot of the traders there are on leverage too. Exacerbating the situation…
After opening down 3.17% on Monday, the KOSPI was lifted 1.76% by institutional buyers to 5,768.01; the advance preceded Tuesday's drop.
Given the benchmark's heavy reliance on those two stocks, its next move may rest on AI spending plans staying firm and foreign selling easing.
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A healthcare technology company is shifting most AI workloads to open-weight and small models, achieving major cost savings while maintaining performance.
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