DRAM vs HBMX: Which Is the Better Pick in 2026?

Overview

DRAM and HBMX are both thematic equity ETFs focused on memory semiconductor ecosystems tied to artificial intelligence infrastructure, but they differ fundamentally in scope and approach. DRAM is a passively managed, broad-based memory ETF from Roundhill with $23.0B in assets, while HBMX is an actively managed, concentrated fund from Tuttle Capital that extends beyond DRAM chips to include NAND memory, high-bandwidth memory, packaging, testing, and equipment suppliers—with just $40.6M in assets. HBMX's narrower focus and active management create a higher-conviction portfolio, whereas DRAM offers scale and a lower fee structure.

How they differ

The biggest structural difference is management style and portfolio breadth: DRAM is passively indexed, while HBMX is actively managed and intentionally concentrated on the full memory ecosystem rather than DRAM production alone. This means HBMX's manager makes stock-selection calls within memory—favoring some equipment makers or packaging firms over others—whereas DRAM follows a defined methodology.

Second, the funds trade at vastly different scales. DRAM's $23.0B in AUM dwarfs HBMX's $40.6M, which creates a liquidity and cost-of-ownership gap; DRAM's 0.65% expense ratio is lower than HBMX's 0.95%, and the size difference suggests tighter bid-ask spreads on DRAM. HBMX's smaller size also means its active manager has more flexibility to move in and out of smaller-cap memory suppliers but carries higher operational drag per dollar invested.

Third, income treatment differs materially. DRAM has no stated distribution frequency (suggesting no regular income), while HBMX distributes annually, meaning HBMX holders may receive periodic cash returns—though as a growth-focused equity fund, these are likely to be modest relative to price appreciation potential.

Who each is best for

DRAM: Fits investors who want broad, liquid exposure to the memory semiconductor theme with minimal fees and a passive, buy-and-hold approach. Appeals to those seeking thematic AI infrastructure exposure without active manager risk or timing bets.

HBMX: Fits investors drawn to a manager's specific conviction within the memory ecosystem—willing to accept higher fees, concentration risk, and lower trading liquidity in exchange for a tighter, curated portfolio spanning DRAM, NAND, HBM, and the supply chain around them.

Key risks to know

• Concentration within a single theme. Both funds bet heavily on memory semiconductors as the AI infrastructure story unfolds; if that thesis stalls or compresses, both funds face correlated drawdowns. Their holdings likely overlap significantly (SK Hynix, Micron, Samsung, memory equipment makers), so owning both doesn't diversify away thematic risk.

• Active-management risk unique to HBMX. The fund's manager must choose which memory producers, packaging specialists, and equipment vendors to overweight or underweight; poor stock-picking within the theme can underperform a broader memory index, even if the theme itself thrives.

• Extreme valuations in memory semiconductors tied to AI hype. Memory stocks have rallied sharply on AI demand expectations. If AI capex cycles slow or memory chip prices normalize, multiple compression could be severe, affecting both funds. HBMX's concentrated portfolio amplifies this risk.

• Smaller AUM and liquidity constraints in HBMX. With only $40.6M in assets, HBMX faces higher per-share operational costs and potentially wider spreads during redemptions or market stress, whereas DRAM's $23.0B scale provides structural cost advantages.

Bottom line

DRAM offers low-cost, passive memory semiconductor exposure at institutional scale; HBMX bets on active management's ability to pick winners across a wider (but still narrower) memory ecosystem. If you want core memory exposure with minimal fees and maximum liquidity, DRAM's index approach and size advantage are material; if you value a manager's curated view of the full memory supply chain and accept higher costs and liquidity trade-offs, HBMX's focused strategy may appeal. Either way, memory-chip valuations remain closely tied to AI infrastructure spending cycles, and past performance doesn't predict future results.