Nvidia valuation slides 15% as high-bandwidth memory costs bottleneck data centers

The transition from a GPU shortage to a memory shortage marks a maturation of the AI infrastructure stack. As hyperscalers successfully deploy custom silicon like Google’s TPU and Amazon’s Trainium, Nvidia’s absolute pricing power for general-purpose compute is eroding. For the streaming and video processing ecosystem, this likely signals a stabilization of compute costs but potentially higher overall data center bills due to skyrocketing HBM and DRAM prices. Companies should watch for memory-related surcharges in cloud contracts and a wider availability of lower-cost inference-optimized instances in the second half of 2026.

The memory sector is currently experiencing what analysts describe as an unprecedented supercycle. Per Forbes, May 2026, Micron’s valuation surged to $1 trillion on the strength of its HBM3E chips, which are critical for AI training. Shortages are so severe that HBM capacity for 2026 is reportedly sold out across major suppliers like SK Hynix and Micron, with demand expected to persist through 2027. This scarcity is driven by AI servers requiring six times the DRAM of traditional servers, according to data from Micron cited in May 2026. This shift is forcing manufacturers to reallocate 23% of total DRAM wafer capacity toward high-margin HBM, which generates three to five times higher profit margins.

Simultaneously, the competitive landscape for processors is shifting toward application-specific integrated circuits (ASICs). Per Tom’s Hardware, May 2026, ASIC-based AI server shipments are projected to grow 44.6% year-over-year in 2026, nearly triple the growth rate of merchant GPUs. Major hyperscalers are prioritizing these custom chips for inference tasks to achieve a 65% total cost of ownership advantage over general-purpose GPUs. Despite this, companies like Amazon and Microsoft continue to buy record volumes of Nvidia’s Blackwell and future Rubin platforms for frontier model training, creating a dual-track market where Nvidia dominates high-end training while losing share in large-scale inference.

Cloud rental market data from Thunder Compute and CloudZero, July 2026, highlights the extreme price fragmentation resulting from this competition. While on-demand H100 rates at hyperscalers remain as high as $11 per hour, neocloud and marketplace providers have seen floor prices drop to approximately $2 per hour. This deflation in compute costs is a direct response to improved supply chains and the entry of new accelerator players. However, memory remains the inflexible expense; per TrendForce, June 2026, conventional DRAM contract prices rose nearly 95% in the first quarter alone, effectively negating the savings organizations might have realized from cheaper GPU silicon.