In the wake of the release of DeepSeek’s innovative R1 model, the investment community is experiencing a wave of anxiety regarding the implications for the AI hardware market, particularly concerning graphics processing units (GPUs). The rapid decrease in training costs associated with AI models has raised alarms among investors about the future demand for data center hardware required to support artificial intelligence applicationsIn light of these developments, JPMorgan has released an insightful report addressing the memory sector's outlook and potential vulnerabilities.

JPMorgan’s analysis highlighted the essential role of high-end training GPUs, which have historically driven increased computational demand across the memory industryOver the past two weeks, the market has witnessed an 11% decline in the average stock prices of memory-related companies, notably greater than the 9% fall seen in the Philadelphia Stock Exchange Semiconductor Index during the same period.

Although concerns surrounding the potential slowdown in GPU demand have surfaced, JPMorgan emphasizes the continuing growth of content driven by the migration of edge AI technologies and the burgeoning market for AI ASICs (Application-Specific Integrated Circuits). The bank has expressed optimism that improvements in the efficiency of inference models could lead to a rise in the demand for pre-trained models, thereby accelerating the penetration of AI applications into various endpoint devices.

Current trends indicate no signs of a slowdown in High Bandwidth Memory (HBM) orders or a reduction in investment plansMajor cloud service providers have publicly expressed optimism regarding their capital expenditure projections during their recent earnings callsThis forward-looking sentiment bodes well for the structure of the memory industry amid evolving technological landscapes.

JPMorgan observes a notable distinction in growth potential between inference demand for HBM versus traditional DRAM applications in edge AI settings

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The bank predicts that the accelerated adoption of cost-effective inference models—exemplified by innovations like DeepSeek—could boost the migration toward edge AIIn comparison to HBM, traditional DRAM appears more likely to see increased uptake, as inference product portfolios only represent 10% of centralized computational memory needsThis indicates that, from a content and bit demand perspective, the additional value of HBM for inference ASICs remains relatively low.

As the industry evolves, JPMorgan anticipates that by 2026, the adoption rates for edge AI in PCs and smartphones could reach as high as 25% and 40%, respectivelyShould the market fully transition to edge AI, this would unleash significant bit growth potentialWhen measured against a baseline scenario, the demand for bits related to edge AI could rise by over 220%, suggesting the potential for a 20% expansion in the overall DRAM market size.

From the perspective of HBM, JPMorgan forecasts that the emergence of low-cost inference models will not dampen the demand for commercial GPUs; rather, it could stimulate additional demandAs pointed out in DeepSeek’s findings, iterative training processes remain an effective method for optimizing inference models, implying that the need for training resources will persist.

Moreover, JPMorgan undertook a detailed sensitivity analysis of DRAM supply and demand dynamics in light of the prevailing trends of slowing HBM demand alongside rising edge AI requirementsThis intricate relationship reveals that a mere 10% decline in HBM demand could necessitate a 5% increase in the penetration rate of AI within edge devices to maintain equilibrium within the DRAM market.

In comparing commercial GPUs with ASICs, JPMorgan underscores the superior flexibility exhibited by GPUs in training methodologies amid rapidly changing market and technological conditionsAccordingly, the firm suggests that investors keep a keen eye on the supply chain primarily centered around NVIDIA, given its dominant position in the GPU market, which enables it to better adapt to shifts in demand.

In the Asian memory sector, JPMorgan shows a preference for SK Hynix, citing its technological capabilities and robust market share, which afford it competitive advantages within the dynamic memory market.

Following a thorough examination of market supply and demand, JPMorgan has drawn several important conclusions

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