The global memory market has a problem. AI infrastructure buildout has reallocated semiconductor manufacturing capacity away from conventional memory toward high-bandwidth memory (HBM) for AI accelerators, and the downstream effects are reaching every corner of the tech industry. DRAM prices have more than doubled through 2025, and analysts project further significant increases through 2026.
If you’re running a tech company, this affects your cloud bills, your hardware refresh plans, your product economics, and your AI deployment costs. This series maps the full picture — what’s happening, why, who is winning and losing, and what you can do about it.
Explore the full series:
- How AI killed the memory supply chain and why everything else is paying for it — the root causes and technical mechanics
- DRAM prices in 2026 have doubled and the numbers are getting worse — price data, market forecasts, and historical comparisons
- Samsung, SK Hynix, Micron, and the hyperscalers who locked up all the memory — winners, losers, and procurement power dynamics
- How the AI memory crunch is wrecking PC, smartphone, and gaming GPU markets — downstream market impact
- How to run AI workloads and manage infrastructure costs during the memory shortage — the enterprise response playbook
- China, DRAM, and export controls: the geopolitical factors shaping memory supply — geopolitics and trade policy
- When will the memory shortage end? Fab timelines and what they tell us — resolution scenarios and planning horizons
What is the AI memory shortage and why is it happening now?
The AI memory shortage is a manufacturing capacity problem, not a materials shortage. Semiconductor fabs — principally Samsung, SK Hynix, and Micron — have reallocated wafer production from conventional DRAM (used in PCs, phones, and servers) to high-bandwidth memory (HBM), which AI accelerators like Nvidia GPUs require. Because one HBM stack uses roughly three times the wafer capacity of equivalent standard DRAM, the pivot to HBM is compressing supply across every other memory-dependent market.
Unlike the pandemic-era chip shortage, this shortage stems from deliberate manufacturing allocation decisions, not external supply disruption. When a fab pivots production lines to HBM, the conventional DRAM those wafers would have produced simply does not exist. There is no buffer, no substitute, no emergency reserve.
The tipping point came when hyperscaler capital expenditure commitments locked in allocation priorities at a scale that pushed the market into acute shortage. OpenAI‘s Stargate initiative alone is projected to consume up to 40% of global DRAM output. Google, Amazon, Microsoft, and Meta placed open-ended orders with memory suppliers, indicating they would accept as much supply as available regardless of cost.
IDC‘s Francisco Jeronimo put it plainly: “For an industry that has long been characterised by boom-and-bust cycles, this time is different.”
Understanding why AI consumes so much memory helps explain why these allocation decisions are unlikely to reverse any time soon. For the full technical explanation of how we got here, see How AI killed the memory supply chain and why everything else is paying for it.
Why does AI need so much memory — what makes AI workloads so memory-hungry?
Large language models are not compute-limited — they are memory-bandwidth-limited. The “memory wall” means GPUs can only process data as fast as memory can deliver it, so AI accelerators require high-bandwidth memory that conventional DRAM cannot provide. For inference, the KV cache (the working memory that tracks active conversation state) grows with every concurrent user and every longer context window, making memory demand non-linear as AI products scale.
A single large LLM inference session requires significantly more high-speed memory than comparable traditional compute workloads. HBM is not optional for AI at scale — adding more GPU compute without proportional HBM bandwidth is largely wasted investment. SemiAnalysis estimates that HBM constitutes 50% or more of the cost of the packaged GPU.
For companies building AI products, inference memory costs are the dominant ongoing infrastructure expense, not training costs. Micron predicts the total HBM market will grow from $35 billion in 2025 to $100 billion by 2028 — a figure larger than the entire DRAM market in 2024.
We cover the technical deep dive on the memory wall in How AI killed the memory supply chain, and the cost management implications in How to run AI workloads and manage infrastructure costs.
How much have memory prices risen and where are they heading?
Memory prices have already risen sharply and forecasters expect further increases through at least 2026. DRAM prices overall have risen 172% since the shortage began. TrendForce revised its Q1 2026 conventional DRAM contract price forecast to +90–95% quarter-on-quarter — more than double the previous record quarterly increase. The global memory market is forecast to reach $551.6 billion in 2026.
The price escalation is broad-based. DDR5 for enterprise servers, LPDDR5X for smartphones, and HBM for AI accelerators have all seen steep increases. Enterprise SSDs have been prioritised over consumer NAND, pushing NAND flash contract prices up 55–60% as well. Spot market buyers — those without long-term supply agreements — are experiencing the full force of this volatility.
The current analyst consensus is that price normalisation is unlikely before 2027–2028 at the earliest. For the full price data and market forecasts, see DRAM prices in 2026 have doubled and the numbers are getting worse.
Who is winning and who is losing from the memory shortage?
The three major memory manufacturers — Samsung, SK Hynix, and Micron — are the primary beneficiaries. Constrained supply inflates their margins on both HBM and conventional DRAM, and together they control more than 90% of global memory chip production. Hyperscalers secured multi-year long-term agreements (LTAs) ahead of the shortage and have preferential supply access. PC makers, smartphone OEMs, gaming hardware vendors, and most enterprise buyers are absorbing cost increases with limited recourse.
On the buyer side, the LTA is the mechanism that separates the insulated from the exposed. Those without LTAs — which includes most enterprise companies and virtually all SMBs — compete on the volatile spot market. Google has stationed procurement executives in South Korea. Microsoft executives staged a walkout at SK Hynix negotiations. This is not gentle commerce.
The cost impact on PC OEMs is severe: memory now makes up approximately 35% of Dell and HP PC build materials, up from 15–18%. Apple is paying a 230% premium on LPDDR5X for the iPhone 17, even with secured LTAs.
For the full analysis of market power dynamics and LTA mechanics, see Samsung, SK Hynix, Micron, and the hyperscalers who locked up all the memory.
Which markets and industries are being hit hardest?
The PC and smartphone markets are experiencing the sharpest downstream impact. IDC projects a 10–11% decline in PC shipments and an 8–9% decline in smartphone shipments in 2026 as memory costs make affordable devices financially unviable. Gaming GPU supply has been depressed, with Nvidia unable to produce discrete gaming cards at previous volumes. Enterprise hardware prices are rising, and cloud infrastructure costs are beginning to increase.
The PC market is being hit from two directions: memory costs are rising while demand is also under pressure. Gartner projects entry-level laptops under $500 will become financially unviable within two years if current cost trajectories persist. PC vendors — Lenovo, Dell, HP, Acer, ASUS — have warned clients of 15–20% price hikes and contract resets.
Smartphone OEMs face a similar squeeze. Apple is partially insulated by LTAs but is still paying premium prices. Akihabara retailers in Tokyo have already implemented RAM purchase limits to prevent hoarding. The irony in the PC space is that AI PCs require a minimum of 16GB RAM for Microsoft Copilot+, but adding more RAM has become prohibitively expensive — the AI PC narrative is undermined by the same AI demand that created it.
For the full breakdown by market segment, see How the AI memory crunch is wrecking PC, smartphone, and gaming GPU markets.
What role does geopolitics play in the memory supply chain?
Geopolitics is actively shaping who can produce memory, who can sell it to whom, and where new capacity will be built. US export controls restrict advanced semiconductor manufacturing equipment from reaching China. The US has simultaneously threatened 100% tariffs on Samsung and SK Hynix chips — punishing the South Korean suppliers that Western buyers currently depend on while trying to build domestic alternatives.
China’s domestic memory response centres on CXMT (ChangXin Memory Technologies), which is targeting HBM3 mass production by end 2026 and has filed for a $4.2 billion IPO. But US export controls constrain CXMT’s access to ASML lithography equipment and other tooling, with localisation at only around 20%. SemiAnalysis projected CXMT would account for nearly 15% of global DRAM production by 2026 — meaningful, but not enough to resolve the shortage.
On the US side, the CHIPS Act is funding domestic capacity — primarily Micron’s $100 billion-plus fab complex in New York state. But fab construction and qualification timelines are 2–4 years. US Commerce Secretary Howard Lutnick made the geopolitical stakes explicit at Micron’s groundbreaking: “Everyone who wants to build memory has two choices: they can pay a 100% tariff, or they can build in America.”
For your planning purposes, the geopolitical dimension increases the risk that supply chain disruption is not simply a market cycle but a structural feature of the memory market for the rest of the decade. For the full analysis, see China, DRAM, and export controls: the geopolitical factors shaping memory supply.
When will the memory shortage end?
The consensus among analysts is that meaningful supply relief will not arrive before 2027, and the shortage may well persist into 2028. Building new fab capacity or converting existing capacity takes 2–4 years. All three major manufacturers have announced capacity expansion programmes, but these investments will not produce marketable memory for 12–24 months from their respective commitment dates.
Intel CEO Lip-Bu Tan was direct about it at the Cisco AI Summit in February 2026: “There’s no relief until 2028.”
The specific investments in train — Samsung’s Pyeongtaek P5 fab expansion, SK Hynix’s $13 billion new HBM assembly plant, and Micron’s New York state fab complex — are all real commitments, but none will translate to significant additional supply before 2027.
There is a meaningful uncertainty in forecasts. Demand destruction is an underaddressed scenario. If AI investment slows due to business model failures or algorithmic efficiency gains — smaller models requiring less memory — demand could moderate before new supply comes online, potentially bringing forward normalisation. This is not the consensus scenario but it is worth tracking.
You should plan infrastructure budgets on the assumption that memory-intensive costs will remain elevated through at least 2027. For the full fab timeline analysis, see When will the memory shortage end? Fab timelines and what they tell us.
What should technology companies do right now?
The highest-leverage responses are: optimise AI inference workloads using quantisation and architectural efficiency techniques to reduce memory footprint per inference call; evaluate infrastructure procurement decisions now — whether to commit to reserved cloud instances, move to bare metal, or maintain on-demand flexibility; and model infrastructure costs at current elevated prices to stress-test product economics rather than assuming prices will return to 2023 levels.
Quantisation — reducing model weight precision from 16-bit to 4-bit or 8-bit — is the most accessible immediate mitigation. Halving precision often allows GPU Tensor Cores to roughly double throughput, improving speed, memory usage, and energy efficiency simultaneously.
Infrastructure procurement decisions involve real trade-offs. Reserved cloud instances offer price certainty but require commitment. Bare metal offers the best per-unit economics but requires operational overhead. On-demand cloud offers flexibility but maximum price exposure. The right choice depends on your workload predictability and company size.
FinOps discipline is increasingly necessary. As hyperscalers begin passing memory cost increases through to cloud customers, infrastructure costs that were predictable are becoming volatile. Proactive tagging, usage monitoring, and commitment evaluation are baseline requirements.
If you don’t have the scale to sign long-term agreements directly with memory manufacturers, focus on the levers you do control: reducing per-inference memory consumption, locking in cloud commitment pricing where available, and timing hardware refreshes to avoid peak spot market pricing. For the full enterprise response playbook, see How to run AI workloads and manage infrastructure costs during the memory shortage.
Resource Hub: AI Memory Shortage Library
Understanding the Crisis
- How AI killed the memory supply chain and why everything else is paying for it — the foundational technical narrative covering HBM, the memory wall, wafer reallocation, and the causality chain from AI architecture to hardware prices.
- DRAM prices in 2026 have doubled and the numbers are getting worse — data-driven analysis of price increases across memory types, with market size forecasts and historical comparisons.
Market Dynamics and Impact
- Samsung, SK Hynix, Micron, and the hyperscalers who locked up all the memory — who is winning, who is losing, how long-term agreements work, and why hyperscalers have supply access that most companies do not.
- How the AI memory crunch is wrecking PC, smartphone, and gaming GPU markets — downstream impact analysis across consumer and enterprise hardware markets.
What Companies Can Do
- How to run AI workloads and manage infrastructure costs during the memory shortage — the enterprise response playbook covering quantisation, FinOps, infrastructure procurement decisions, and timing strategies.
Geopolitics and Timeline
- China, DRAM, and export controls: the geopolitical factors shaping memory supply — how US export controls, CXMT’s domestic ambitions, tariff threats, and CHIPS Act investments are reshaping the global memory supply map.
- When will the memory shortage end? Fab timelines and what they tell us — forward-looking analysis of fab construction timelines, capacity expansion commitments, and what to plan for in 2027 and beyond.
FAQ Section
What is high-bandwidth memory (HBM) and how is it different from regular DRAM?
HBM is a specialised DRAM architecture that stacks up to 12 memory dies vertically using through-silicon vias (TSVs), delivering dramatically higher data transfer bandwidth than conventional DDR or LPDDR modules. One HBM stack uses approximately three times the wafer capacity of equivalent standard DRAM, which is the core reason the shift to HBM production is compressing conventional memory supply. See How AI killed the memory supply chain for the full technical explanation.
How does the 2024–2026 memory shortage compare to the 2020–2023 chip shortage?
The 2020–2023 shortage was driven by pandemic supply chain disruptions — external shocks to production and logistics. The 2024–2026 shortage is driven by deliberate manufacturing strategy: fabs are actively choosing to reallocate wafer capacity to higher-margin HBM because AI demand is so strong. This makes the resolution timeline different and slower — the market is waiting for capital investment to produce new capacity, not for disruptions to clear. See DRAM prices in 2026 for the comparative analysis.
What is a long-term agreement (LTA) and why does it matter for memory supply?
An LTA is a multi-year supply contract between a memory buyer and a manufacturer that locks in committed volumes. Hyperscalers signed these agreements before the shortage tightened, securing preferential supply access. Buyers without LTAs — which includes most enterprise companies and virtually all SMBs — compete on the volatile spot market. See Samsung, SK Hynix, Micron, and the hyperscalers for a full explanation of how LTAs work.
Will cloud costs rise because of the memory shortage?
Yes, with a lag. Hyperscalers have so far largely absorbed increased memory costs rather than passing them immediately to cloud customers, but this is not sustainable indefinitely. As memory contract terms roll over and new agreements are signed at elevated prices, cloud pricing increases are expected to follow. See How to run AI workloads and manage infrastructure costs for how to prepare.
Can China’s domestic memory industry fix the shortage?
CXMT represents China’s strategic response but its near-term capacity is constrained by US export controls on advanced semiconductor manufacturing equipment. CXMT is targeting HBM3 mass production by end 2026 and could meaningfully supplement conventional DRAM supply, but it cannot substitute for SK Hynix, Samsung, or Micron at current capacity levels. See China, DRAM, and export controls for the full analysis.
Should technology companies buy server hardware now or wait for prices to improve?
It depends on your timeline and workload certainty. If you have predictable, near-term memory-intensive requirements, buying now at current elevated prices may be preferable to buying at potentially higher spot prices in 12–18 months. If requirements are uncertain or 2–3 years out, waiting for supply normalisation may yield better unit economics. See When will the memory shortage end? for supply timeline analysis.
What does quantisation mean and why does it help with memory costs?
Quantisation reduces the numerical precision of AI model weights — for example, from 16-bit floating point to 8-bit or 4-bit integers. Lower-precision weights require less memory to store and less bandwidth to transfer during inference, reducing the GPU memory footprint per inference call. Halving precision typically allows Tensor Cores to roughly double throughput. Dropbox uses quantisation in production for its Dash product. See How to run AI workloads and manage infrastructure costs for the full optimisation playbook.
