Interview with an industry expert about insights into GPUs, ASICs, and memory trends (
$NVDA,
$AMD,
$MU,
$GOOGL ):
- The expert emphasizes that GPU demand, especially for
$NVDA, remains extremely strong and unchanged in terms of availability or lead times. He stresses that
$AMD is also ramping quickly, with the MI300 and 400 series GPUs growing at around 15–20% year-over-year; however, adoption is concentrated almost entirely among hyperscalers because
$AMD's software and ecosystem are still immature.
- According to the expert,
$NVDA is maintaining pricing very firm, with little to no discounting, due to extremely strong demand, while deep learning ASIC start-ups are offering discounts in the 10–20% range, primarily targeting non-hyperscaler and international customers seeking to build domestic AI infrastructure. He stresses that this discounting is less about performance and more about positioning on price, as these smaller players try to carve out market share outside the core hyperscaler segment.
- In relation to ASICs, expert explained that hyperscalers build their own ASICs, such as
$GOOGL's TPU, almost exclusively for internal use, while start-ups target the merchant market. However, because hyperscalers rely on in-house chips or established GPU providers like
$NVDA and
$AMD, the addressable market for these start-ups is shrinking. He stresses that this makes opportunities increasingly narrow, especially as
$NVDA and
$AMD combine hardware strength with massive investments in software and ecosystem support.
- The expert emphasizes that HBM remains one of the most significant bottlenecks in the AI semiconductor supply chain, with only three major vendors; SK hynix, Samsung, and Micron producing HBM3 and HBM3E. He notes that supply from all three sources is fully allocated through 2025, and lead times for new demand are now approaching 12 months. These suppliers prioritize
$NVDA first, then the hyperscalers, which leaves start-up ASIC vendors struggling to secure capacity.
- He explains that HBM3 pricing has already increased by 20–30% year-over-year, driven by both extreme demand and the fact that each new GPU or ASIC generation requires significantly more HBM content. Looking forward, he expects continued upward pressure on pricing as vendors invest heavily in advancing to HBM4, which will double I/O bandwidth and density.
-The expert stresses that while the overall AI semiconductor market is expanding rapidly,
$AMD has been the clear share gainer, now reaching an estimated 5%–10% share and around $7–8 billion in GPU revenue. He emphasizes that this hasn’t reduced
$NVDA's revenue but has modestly slowed its growth as
$AMD ramps new products.