Nvidia-based AI servers could become more than 15% costlier as rising HBM and DRAM prices increase the cost of AI infrastructure.
Nvidia-based artificial intelligence servers could become more than 15% costlier, with server manufacturers reportedly preparing price increases for systems scheduled for delivery from early 2027. Rising prices of high-bandwidth memory (HBM) and conventional server DRAM are emerging as key drivers of the increase.
The higher costs are expected to benefit major memory suppliers such as Samsung Electronics and SK hynix, while potentially putting additional pressure on companies investing heavily in AI data centre infrastructure.
The reported price revisions are expected to affect Nvidia’s next-generation Vera Rubin systems as well as existing Grace Blackwell platforms. The extent of the increase could vary depending on the accelerator generation and memory configuration.
AI accelerators depend on HBM to deliver the bandwidth required for intensive workloads, while data centre servers also consume large quantities of conventional DRAM. Strong demand from cloud providers and technology companies has tightened memory supplies, giving manufacturers greater pricing power.
TrendForce expects server DRAM contract prices to rise by 13% to 18% in the third quarter of 2026 from the previous quarter. The pressure has been intensified by manufacturers allocating more production capacity towards AI-related memory products.
Samsung, SK hynix positioned to gain
The supply constraints are strengthening the position of Samsung and SK hynix in the AI memory market. SK hynix has established a leading position in HBM, which is used alongside AI accelerators, while Samsung remains a major supplier of conventional DRAM and is expanding its HBM business.
The higher server prices could have broader implications for hyperscalers, including Microsoft, Google and Oracle, as they continue to expand AI computing capacity.
For large-scale data centre projects, higher memory and server costs could increase overall capital expenditure and affect the expected returns from AI infrastructure investments.
The development highlights a key challenge for the AI hardware ecosystem: rapidly growing demand for computing capacity is driving investment, but shortages of critical memory components are simultaneously making AI infrastructure more expensive.
