TradingKey - With the rapid growth in demand for AI servers, HBM, DRAM, NAND, and enterprise SSDs have gradually become some of the most closely watched key events in the semiconductor market. But for average investors, these memory products are easily confused: Do AI servers actually need HBM or NAND more? How do Micron (MU), SanDisk (SNDK), and SK Hynix (SKHY) each benefit from which segment of demand?
As the AI industry gradually expands from large model training to inference, AI agents, and long-context applications, memory and storage requirements are also beginning to shift. HBM remains indispensable high-bandwidth memory for advanced AI accelerators, but enterprise SSDs and NAND are evolving from traditional data storage devices into critical components of AI infrastructure.
Do AI Servers Need HBM or NAND?
A complete AI server typically uses NAND, DDR memory, and HBM simultaneously. Data is first stored in enterprise SSDs based on NAND flash memory, loaded into system memory once computation tasks begin, and finally transferred to HBM located close to GPUs or AI accelerators to complete operations.
This process can be simply understood as NAND being a massive warehouse, DDR acting as a transit hub, and HBM serving as a high-speed workbench directly adjacent to the GPU. Without HBM, GPUs may fail to unleash their full computing power while waiting for data; without NAND, servers struggle to store ever-expanding training data, model parameters, vector databases, and inference caches.
Comparison Item | HBM | NAND |
Technical Attributes | Volatile DRAM; data is lost when powered off | Non-volatile flash memory; data is retained after power off |
Primary Form Factor | Stacked memory tightly connected to GPUs or AI accelerators | Enterprise SSDs and other flash storage devices |
Core Applications | Model weights, activation data, hot KV cache, and real-time computation | Training data, model checkpoints, vector databases, logs, and persistent KV cache |
Key Advantages | High bandwidth and low latency, boosting GPU utilization | Large capacity and low unit cost, suitable for long-term storage of massive data |
Key Limitations | High cost, limited capacity, and complex packaging | Speed and latency lag significantly behind HBM and DDR |
Primary Demand Drivers | AI training, high-performance inference, and advanced GPU upgrades | Data lakes, long context windows, AI agents, and large-scale inference |
During the training phase, GPUs must frequently access model parameters and intermediate computation results, making them highly sensitive to HBM's bandwidth, capacity, and power consumption. Upgrades to products like HBM3E and HBM4 allow a single AI accelerator to handle larger models while reducing wait times caused by data movement.
In the inference phase, the importance of NAND increases further. Concurrent calls to models by massive numbers of users generate huge KV caches, vector data, and interaction logs. If all this data were stored in expensive HBM, costs would become uncontrollable. Micron Technology has expanded its product portfolio to cover HBM, DDR, LPDDR, NAND, and data center SSDs, explicitly proposing that HBM handle high-frequency model computations and hot KV cache, while enterprise SSDs take on persistent cache and data lakes.
SK Hynix is also promoting a tiered storage architecture, using solutions such as SALT-KV to allocate KV caches to HBM, DRAM, and SSDs based on data access frequency. The company is also developing High-Bandwidth Flash (HBF) to establish a new storage tier between HBM and traditional SSDs, striking a balance among capacity, speed, and cost.
Therefore, HBM and NAND are not mutually substitute products. While AI training relies more heavily on HBM, inference, AI agents, and long-context applications will simultaneously drive demand for HBM, system memory, and NAND.
What Are the Differences Among the Businesses of Micron, SanDisk, and SK Hynix?
Micron: HBM and Server DRAM Are Core AI Growth Drivers
Micron is one of the most representative memory chip companies in the US market, with its AI business primarily focused on HBM, server DRAM, and enterprise SSDs.
In AI server architecture, Micron participates in both high-speed memory and server storage, making the growth in AI capital expenditures a relatively direct tailwind for the company.
In recent years, Micron has continuously increased the proportion of high-value data center products in its portfolio. The company previously disclosed that its data center business has become a key driver of overall performance growth, with HBM being one of the products attracting the most market attention. As the performance of AI accelerators continues to improve, the HBM capacity per GPU is also rising, which will further increase the memory content value per AI server.
Meanwhile, server DRAM is equally important. Traditional servers typically use memory modules such as RDIMMs, and as AI infrastructure continues to expand, the number of general-purpose CPU servers is also growing, which will further drive up demand for server DRAM.
TrendForce noted in a previous DRAM research report that as suppliers continue to shift a portion of their production capacity toward HBM and server applications, DRAM supply for PCs and smartphones has been squeezed, leaving the overall DRAM market in a tight state.
This is also one of the biggest differences between Micron and SanDisk.
SanDisk mainly benefits from NAND and SSD prices, whereas Micron can benefit simultaneously from rising HBM and DRAM prices. Therefore, if AI servers continue to drive up demand for high-performance memory, Micron's revenue and margin elasticity may come more from DRAM and HBM.
At the same time, however, Micron is also more exposed to the HBM competitive landscape and the DRAM cycle. Samsung and SK Hynix are both aggressively expanding their HBM capacity; thus, as more supply enters the market in the future, HBM pricing and profit margins will need to be reevaluated.
SK Hynix: HBM Is the Most Prominent AI Memory Thesis
SK Hynix has long been deeply rooted in DRAM and entered the HBM market early. As AI chipmakers such as Nvidia continuously boost computing performance, HBM has become one of the most critical high-value memory products supporting AI accelerators.
SK Hynix is currently advancing HBM4 and further developing next-generation products. The company's Q2 2026 financial results showed that demand for high-performance DRAM and HBM continues to drive revenue growth, and the company also stated that HBM4 has met customer standards for performance and power consumption and has begun ramping up supply.
This means that as long as AI accelerator shipments continue to rise, or the HBM capacity packed into each AI chip continues to increase, SK Hynix has the opportunity to sustain its growth.
However, SK Hynix is not limited to HBM alone.
Through its subsidiary Solidigm, the company also participates in the NAND and enterprise SSD markets. In recent years, Solidigm has focused on developing high-capacity QLC enterprise SSDs, which perfectly align with AI data centers' demand for high-capacity, cost-effective storage.
Therefore, SK Hynix actually covers two major memory and storage needs of AI servers simultaneously: the parent company SK Hynix primarily benefits from HBM and server DRAM, while Solidigm participates in the enterprise SSD market.
SanDisk: Primary AI Opportunities Come from NAND and Enterprise SSDs
The biggest difference between SanDisk and Micron or SK Hynix is that SanDisk does not produce HBM.
SanDisk primarily focuses on NAND Flash and related storage products; hence, its AI-driven opportunities stem mainly from enterprise SSDs and high-capacity storage demand, rather than HBM used by AI accelerators.
This does not mean SanDisk plays a smaller role in AI servers.
On the contrary, as AI models move from the training phase into widespread practical application, the volume of data that data centers need to store is likely to grow continuously. In particular, AI inference, AI agents, and long-term data archiving will generate vast amounts of data that require high-speed reading and long-term retention.
Consequently, AI servers require not only GPUs and HBM, but also an increasing number of high-capacity enterprise SSDs.
TrendForce expects demand for enterprise SSDs in 2026 to remain driven by generative AI, AI agent services, and hyper-scale data center construction. Major US cloud service providers have also recently raised their enterprise SSD procurement forecasts, so enterprise SSD orders in Q4 2026 are expected to maintain growth.
This represents SanDisk's largest industry opportunity at present.
However, compared with Micron and SK Hynix, SanDisk faces more pronounced NAND cyclical risks. TrendForce projects that the tight NAND supply may begin to ease in the second half of 2027, as new capacity rollouts and weak consumer electronics demand could gradually improve the supply-demand balance.
Therefore, SNDK's stock price and earnings depend not only on AI data center demand, but are also subject to the NAND price cycle.
Summary: As AI Storage Demand Grows, Who Has a More Direct Path to Benefit?
The biggest difference among the three companies lies in their product coverage and degree of reliance on the HBM and NAND markets. Micron and SK Hynix operate DRAM, HBM, and NAND businesses simultaneously, whereas SanDisk focuses primarily on NAND flash and SSDs.
Company | Core Business | HBM Positioning | NAND Positioning | AI Server Benefit Drivers |
Micron | DRAM, HBM, NAND, and SSD | Positioned in high-end products such as HBM3E and HBM4 | Covers NAND and enterprise SSDs | Benefits simultaneously from the expansion of GPU memory, server memory, and AI storage |
SanDisk | NAND flash, enterprise SSDs, and consumer storage | Currently has no HBM products | Business is highly concentrated in NAND and SSDs | Primarily benefits from inference data, enterprise SSDs, and NAND price cycles |
SK Hynix | DRAM, HBM, NAND, and enterprise SSDs | Strong competitive advantage in HBM, continuously advancing HBM4 and custom HBM | Positions in enterprise SSDs through its own NAND and Solidigm | Covers both high-end AI memory and high-capacity data center storage |
If AI servers continue to expand around GPU clusters and large model training, HBM remains one of the segments with the clearest value growth. Under this scenario, SK Hynix has more direct business exposure to HBM, while Micron benefits simultaneously from demand for HBM, server DRAM, and data center SSDs.
If the AI industry's growth center of gravity gradually shifts from training to inference, the importance of NAND will rise significantly. AI agent applications, enterprise knowledge bases, video generation, and long-context models will all generate more data that requires long-term storage. Enterprises cannot put all content into costly HBM, thus needing to rely on high-capacity SSDs to build a tiered storage architecture. Under this trend, SanDisk has high sensitivity to shifts in NAND and enterprise SSD demand, while SK Hynix and Micron will also capture incremental gains through their respective NAND businesses.
It is worth noting that new technologies such as HBF may further blur the boundary between memory and storage. HBF seeks to use a stacking approach similar to HBM to boost NAND bandwidth, narrowing the performance gap with traditional SSDs while offering capacity far exceeding that of HBM. However, in the short term, HBF is more likely to act as a complementary layer between HBM and SSDs rather than replacing HBM.
Find out more
Comments