Key Statistics
Key Takeaways
- Enterprise ssd for ai market was valued at USD 527.0 million in 2025 and is projected to reach USD 1.09 billion by 2034, representing a 8.4% CAGR over 2026–2034.
- The market is valued at USD 527 million in 2025 and is projected to reach USD 1.09 billion by 2034, representing an 8.4% CAGR over 2026–2034.
- PCIe 5.0 SSDs form the commercial center of current AI deployments because they combine high throughput, low latency and broad server-platform support, while PCIe 6.0 begins the next performance step.
- AI training and retrieval workloads are the strongest applications because GPU clusters require sustained ingestion of large training sets, checkpoints and vector data without starving accelerator compute.
- North America leads through hyperscale AI investment, while Asia Pacific provides the majority of NAND flash manufacturing and a large share of enterprise SSD production.
Enterprise SSD for AI Market Overview
Enterprise ssd for ai market was valued at USD 527.0 million in 2025, is estimated at USD 571.1 million in 2026, and is projected to reach USD 1.09 billion by 2034, advancing at a 8.4% CAGR during 2026–2034. North America represents the largest regional market in 2025, supported by the concentration of relevant manufacturing, downstream demand and supplier ecosystems.
Enterprise SSDs for AI are data-center solid-state drives optimized for machine-learning, deep-learning and high-performance analytics workloads that place unusually high demands on throughput, random IOPS, latency consistency, endurance and power efficiency. Unlike ordinary enterprise storage, AI infrastructure must feed large GPU clusters, sustain checkpoint traffic and serve vector or retrieval datasets at high concurrency.
The product roadmap is moving rapidly from PCIe 5.0 toward PCIe 6.0 while capacities rise above 30 TB per drive. AI operators increasingly evaluate SSDs as part of a full accelerator data path, considering firmware quality, telemetry, security, power-per-IOPS and direct-storage compatibility alongside headline sequential speed.
The commercial structure of the enterprise ssd for ai market is shaped by design qualification, production economics and the interaction between component performance and downstream system requirements. Suppliers that can combine consistent manufacturing, application engineering and supply assurance are better positioned to convert technical eligibility into repeat business. Customer decisions increasingly account for lifecycle continuity, geographic resilience and integration cost, which makes proven process control and ecosystem compatibility as important as nominal product capability.
Segment Analysis: By Type
By type, the Enterprise SSD for AI market is segmented into PCIe 4.0 SSDs, PCIe 5.0 SSDs, PCIe 6.0 & Emerging SSDs. PCIe 5.0 dominates new high-performance AI deployments because current GPU servers and storage fabrics can use its bandwidth efficiently. PCIe 4.0 remains important for cost-sensitive tiers, while PCIe 6.0 is emerging for next-generation accelerator platforms where storage throughput must scale with GPU count. Purchasing decisions vary by performance requirement, qualification burden, integration complexity and lifecycle economics, so suppliers compete on application fit rather than on a single technical specification.
| Type | Role in the market | Demand characteristics |
|---|---|---|
| PCIe 4.0 SSDs | PCIe 4.0 SSDs addresses a distinct part of the product and process mix. Its commercial position depends on technical fit, manufacturing maturity, integration requirements and customer qualification rather than on headline specifications alone. | Demand is strongest where the segment aligns with established customer designs, predictable supply, and measurable system-level performance. Suppliers that can document repeatability, compatibility and lifecycle support are better placed to defend design wins. |
| PCIe 5.0 SSDs | PCIe 5.0 SSDs addresses a distinct part of the product and process mix. Its commercial position depends on technical fit, manufacturing maturity, integration requirements and customer qualification rather than on headline specifications alone. | Demand is strongest where the segment aligns with established customer designs, predictable supply, and measurable system-level performance. Suppliers that can document repeatability, compatibility and lifecycle support are better placed to defend design wins. |
| PCIe 6.0 & Emerging SSDs | PCIe 6.0 & Emerging SSDs addresses a distinct part of the product and process mix. Its commercial position depends on technical fit, manufacturing maturity, integration requirements and customer qualification rather than on headline specifications alone. | Demand is strongest where the segment aligns with established customer designs, predictable supply, and measurable system-level performance. Suppliers that can document repeatability, compatibility and lifecycle support are better placed to defend design wins. |
Segment Analysis: By Capacity
By capacity, the Enterprise SSD for AI market is segmented into Below 8 TB, 8–30.72 TB, Above 30.72 TB. The 8–30.72 TB range is the current enterprise sweet spot, while QLC-based drives above 30 TB are gaining value in AI data lakes and warm storage because rack density matters increasingly. Purchasing decisions vary by performance requirement, qualification burden, integration complexity and lifecycle economics, so suppliers compete on application fit rather than on a single technical specification.
| Capacity | Role in the market | Demand characteristics |
|---|---|---|
| Below 8 TB | Below 8 TB represents a relevant purchasing and deployment pathway within this market. Commercial attractiveness depends on how well it aligns with customer architecture, supply-chain constraints and required performance. | Growth is linked to design-in activity, installed-base transitions and the ability of suppliers to meet qualification, cost and availability requirements over the full product lifecycle. |
| 8–30.72 TB | 8–30.72 TB represents a relevant purchasing and deployment pathway within this market. Commercial attractiveness depends on how well it aligns with customer architecture, supply-chain constraints and required performance. | Growth is linked to design-in activity, installed-base transitions and the ability of suppliers to meet qualification, cost and availability requirements over the full product lifecycle. |
| Above 30.72 TB | Above 30.72 TB represents a relevant purchasing and deployment pathway within this market. Commercial attractiveness depends on how well it aligns with customer architecture, supply-chain constraints and required performance. | Growth is linked to design-in activity, installed-base transitions and the ability of suppliers to meet qualification, cost and availability requirements over the full product lifecycle. |
Segment Analysis: By Application
By application, the Enterprise SSD for AI market is segmented into AI Training, AI Inference, Vector Databases & RAG, HPC & Real-Time Analytics. AI training requires very high sequential and mixed-read throughput for data loading and checkpoints, while inference and RAG depend more on low-latency random reads and dense capacity. HPC and real-time analytics extend the same performance requirements into scientific and financial workloads. Purchasing decisions vary by performance requirement, qualification burden, integration complexity and lifecycle economics, so suppliers compete on application fit rather than on a single technical specification.
| Application | Role in the market | Demand characteristics |
|---|---|---|
| AI Training | AI Training creates demand when the underlying system requires the market’s core function at production scale. Procurement is normally tied to platform launches, equipment upgrades, capacity expansion or replacement cycles. | Buyers evaluate reliability, qualification time, integration cost and vendor support alongside unit economics. Adoption accelerates when the solution removes a system bottleneck or supports a new performance tier without creating disproportionate redesign risk. |
| AI Inference | AI Inference creates demand when the underlying system requires the market’s core function at production scale. Procurement is normally tied to platform launches, equipment upgrades, capacity expansion or replacement cycles. | Buyers evaluate reliability, qualification time, integration cost and vendor support alongside unit economics. Adoption accelerates when the solution removes a system bottleneck or supports a new performance tier without creating disproportionate redesign risk. |
| Vector Databases & RAG | Vector Databases & RAG creates demand when the underlying system requires the market’s core function at production scale. Procurement is normally tied to platform launches, equipment upgrades, capacity expansion or replacement cycles. | Buyers evaluate reliability, qualification time, integration cost and vendor support alongside unit economics. Adoption accelerates when the solution removes a system bottleneck or supports a new performance tier without creating disproportionate redesign risk. |
| HPC & Real-Time Analytics | HPC & Real-Time Analytics creates demand when the underlying system requires the market’s core function at production scale. Procurement is normally tied to platform launches, equipment upgrades, capacity expansion or replacement cycles. | Buyers evaluate reliability, qualification time, integration cost and vendor support alongside unit economics. Adoption accelerates when the solution removes a system bottleneck or supports a new performance tier without creating disproportionate redesign risk. |
Segment Analysis: By Form Factor
By form factor, the Enterprise SSD for AI market is segmented into U.2 / U.3, E1.S / E1.L, E3.S / E3.L. U.2/U.3 remains widely deployed, but EDSFF formats are gaining share because they improve serviceability, airflow and density in modern AI servers and storage appliances. Purchasing decisions vary by performance requirement, qualification burden, integration complexity and lifecycle economics, so suppliers compete on application fit rather than on a single technical specification.
| Form Factor | Role in the market | Demand characteristics |
|---|---|---|
| U.2 / U.3 | U.2 / U.3 represents a relevant purchasing and deployment pathway within this market. Commercial attractiveness depends on how well it aligns with customer architecture, supply-chain constraints and required performance. | Growth is linked to design-in activity, installed-base transitions and the ability of suppliers to meet qualification, cost and availability requirements over the full product lifecycle. |
| E1.S / E1.L | E1.S / E1.L represents a relevant purchasing and deployment pathway within this market. Commercial attractiveness depends on how well it aligns with customer architecture, supply-chain constraints and required performance. | Growth is linked to design-in activity, installed-base transitions and the ability of suppliers to meet qualification, cost and availability requirements over the full product lifecycle. |
| E3.S / E3.L | E3.S / E3.L represents a relevant purchasing and deployment pathway within this market. Commercial attractiveness depends on how well it aligns with customer architecture, supply-chain constraints and required performance. | Growth is linked to design-in activity, installed-base transitions and the ability of suppliers to meet qualification, cost and availability requirements over the full product lifecycle. |
Regional Analysis
North America leads the enterprise ssd for ai market in 2025, while regional growth patterns differ according to manufacturing concentration, customer investment cycles, regulation, product mix and supply-chain access. The most important regional distinction is not simply market size; it is the mechanism that converts local investment and end-market demand into qualified purchases.
How does regional demand differ across the enterprise ssd for ai market?
Regional demand for enterprise ssd for ai is structurally uneven because manufacturing assets, design centers, downstream customers and qualification ecosystems are not distributed evenly. Asia Pacific typically has the deepest electronics manufacturing base, North America combines technology development with capital investment, Europe emphasizes reliability and industrial requirements, and emerging regions are more dependent on imported systems. These differences influence vendor selection, inventory policy, service expectations and the pace at which new technologies are adopted.
| Region | Market position | Growth outlook | Demand profile | Supplier-selection gate |
|---|---|---|---|---|
| North America | Largest | Very strong | Hyperscale AI and cloud led | GPU-storage balance, power efficiency and qualification |
| Asia Pacific | Manufacturing leader | Very strong | NAND and SSD production led | NAND cost, controller supply and AI-server demand |
| Europe | Large enterprise market | Strong | Cloud, sovereign AI and HPC led | Data sovereignty and infrastructure cost |
| South America | Emerging | Moderate-strong | Cloud and enterprise AI led | Capex and imported hardware |
| Middle East & Africa | Developing | Strong from smaller base | Sovereign AI and cloud led | Accelerator availability and data-center build-out |
Detailed Regional Blocks
Competitive Landscape
Competition in the enterprise ssd for ai market is determined by technical qualification, manufacturing consistency, customer relationships and the ability to support product lifecycles. Price matters, but it rarely overrides reliability, integration effort or switching cost once a supplier has been designed into a production platform.
The competitive field includes Samsung Electronics, Micron Technology, KIOXIA, SK hynix / Solidigm, Western Digital / SanDisk, Seagate Technology, alongside other specialized suppliers. Market share can shift when a vendor gains access to a new platform, expands qualified capacity or introduces a technology that materially improves system performance. Because customer validation can be lengthy, incumbent suppliers often benefit from continuity, while challengers need a clear technical or supply-chain advantage to displace an approved source.
Competitive strategy is increasingly multi-dimensional. Leading suppliers invest in process or product development, application engineering and regional support at the same time, while customers seek second-source options where technically feasible. This creates a market in which differentiation is sustained through documented performance, manufacturing yield, quality systems, responsiveness and the ability to support future roadmap transitions without forcing unnecessary redesigns.
| Company | Competitive position | Primary basis of competition |
|---|---|---|
| Samsung Electronics | Established global / strategic supplier | Competes through product capability, qualification, account access and supply execution. |
| Micron Technology | Established global / strategic supplier | Competes through product capability, qualification, account access and supply execution. |
| KIOXIA | Established global / strategic supplier | Competes through product capability, qualification, account access and supply execution. |
| SK hynix / Solidigm | Specialist / regional competitor | Competes through product capability, qualification, account access and supply execution. |
| Western Digital / SanDisk | Specialist / regional competitor | Competes through product capability, qualification, account access and supply execution. |
| Seagate Technology | Specialist / regional competitor | Competes through product capability, qualification, account access and supply execution. |
| Kingston Technology | Focused challenger / niche participant | Competes through product capability, qualification, account access and supply execution. |
| Intel | Focused challenger / niche participant | Competes through product capability, qualification, account access and supply execution. |
Production Capacity Analysis
Enterprise SSD capacity depends on NAND wafer output, controller silicon, DRAM cache, firmware validation and enterprise-grade packaging. Vertical integration gives leading NAND vendors greater control over cost and product tuning.
Asia Pacific dominates NAND production, while North America is a major controller, system and demand center. AI-specific supply can tighten when premium NAND, high-end controllers or qualification slots are constrained even if commodity flash is available.
Capacity should therefore be evaluated on a qualified-output basis rather than as nominal installed capability. A supplier may possess equipment or production lines that appear suitable but still require customer-specific validation, process tuning, test correlation or upstream material qualification. This distinction becomes especially important during demand spikes, when headline capacity can overstate the volume that is immediately available for a particular application, technology generation or reliability class.
Market Dynamics
The enterprise ssd for ai market is being shaped by rising performance requirements and technology transitions on the demand side, while qualification complexity, supply concentration and cost discipline constrain the pace of adoption. The balance of these forces favors suppliers that can solve a measurable system problem without increasing integration risk.
Demand expansion is not uniform across all product variants or customer groups. New technology creates attractive design opportunities, but buyers typically require a clear economic or performance case before changing a qualified architecture. At the same time, supply-chain localization, capacity investment and tighter quality requirements are changing procurement behavior. As a result, growth is likely to concentrate around applications where performance upgrades, manufacturing scale and lifecycle value reinforce one another.
Market Drivers
Four forces provide the strongest support for demand in the enterprise ssd for ai market: performance-driven technology upgrades, expansion of relevant downstream applications, investment in manufacturing or infrastructure, and the need for more resilient supply. Their importance varies by segment, but each creates a direct reason for customers to qualify or purchase additional solutions.
| Driver | Impact | Commercial mechanism |
|---|---|---|
| AI models increase storage throughput requirements | High | Large datasets, checkpoints and RAG indexes create sustained high-bandwidth storage traffic. |
| PCIe generations keep raising host bandwidth | High | PCIe 5.0 and 6.0 enable faster data movement into GPU and CPU memory. |
| Higher capacities improve rack density | Medium-High | Dense QLC and TLC drives reduce the number of devices and servers required for large data pools. |
| GPU utilization makes storage latency more expensive | Medium-High | Under-fed accelerators waste expensive compute capacity, justifying premium storage. |
AI models increase storage throughput requirements
Large datasets, checkpoints and RAG indexes create sustained high-bandwidth storage traffic. The commercial effect is strongest where customers can link the technology change to higher throughput, lower power, improved reliability, reduced lifecycle cost or access to a new end-market requirement.
PCIe generations keep raising host bandwidth
PCIe 5.0 and 6.0 enable faster data movement into GPU and CPU memory. The commercial effect is strongest where customers can link the technology change to higher throughput, lower power, improved reliability, reduced lifecycle cost or access to a new end-market requirement.
Higher capacities improve rack density
Dense QLC and TLC drives reduce the number of devices and servers required for large data pools. The commercial effect is strongest where customers can link the technology change to higher throughput, lower power, improved reliability, reduced lifecycle cost or access to a new end-market requirement.
GPU utilization makes storage latency more expensive
Under-fed accelerators waste expensive compute capacity, justifying premium storage. The commercial effect is strongest where customers can link the technology change to higher throughput, lower power, improved reliability, reduced lifecycle cost or access to a new end-market requirement.
Market Restraints
Growth is moderated by qualification burden, cyclical purchasing, technology substitution and supply-chain risk. These restraints do not eliminate demand, but they can extend sales cycles, limit supplier switching and reduce the speed at which technically superior solutions convert into production revenue.
| Restraint | Impact | Commercial consequence |
|---|---|---|
| Enterprise NAND pricing is cyclical | High | Flash supply-demand swings can create procurement uncertainty and margin pressure. |
| Qualification cycles are long | High | Mission-critical AI systems require validation of firmware, endurance and performance consistency. |
| Power density can become limiting | Medium | Very fast SSDs can consume substantial power and add heat inside GPU-dense servers. |
| DRAM and object storage compete for data placement | Medium | Architects balance SSD cost against memory caching and cheaper capacity tiers. |
Enterprise NAND pricing is cyclical
Flash supply-demand swings can create procurement uncertainty and margin pressure. Suppliers can mitigate part of this pressure through stronger qualification support, transparent lifecycle planning, dual-source strategies and better alignment between product roadmaps and customer production schedules.
Qualification cycles are long
Mission-critical AI systems require validation of firmware, endurance and performance consistency. Suppliers can mitigate part of this pressure through stronger qualification support, transparent lifecycle planning, dual-source strategies and better alignment between product roadmaps and customer production schedules.
Power density can become limiting
Very fast SSDs can consume substantial power and add heat inside GPU-dense servers. Suppliers can mitigate part of this pressure through stronger qualification support, transparent lifecycle planning, dual-source strategies and better alignment between product roadmaps and customer production schedules.
DRAM and object storage compete for data placement
Architects balance SSD cost against memory caching and cheaper capacity tiers. Suppliers can mitigate part of this pressure through stronger qualification support, transparent lifecycle planning, dual-source strategies and better alignment between product roadmaps and customer production schedules.
Market Opportunities
The most attractive opportunities in the enterprise ssd for ai market occur where a technology transition creates measurable value and where suppliers can enter before the customer architecture is locked. Early engagement, application-specific engineering and credible supply planning can convert these windows into durable design positions.
PCIe 6.0 AI storage
Next-generation GPU servers can use much higher host bandwidth. The opportunity is strongest for suppliers that can demonstrate performance with production-relevant data, provide engineering support during qualification and maintain a roadmap that gives customers confidence in long-term availability.
CXL and expanded memory hierarchies
Flash can serve new tiers between DRAM and traditional storage in selected architectures. The opportunity is strongest for suppliers that can demonstrate performance with production-relevant data, provide engineering support during qualification and maintain a roadmap that gives customers confidence in long-term availability.
High-capacity QLC enterprise SSDs
AI data lakes and vector stores favor dense low-power flash capacity. The opportunity is strongest for suppliers that can demonstrate performance with production-relevant data, provide engineering support during qualification and maintain a roadmap that gives customers confidence in long-term availability.
GPU-direct storage paths
Reducing CPU mediation can improve accelerator data ingestion and system efficiency. The opportunity is strongest for suppliers that can demonstrate performance with production-relevant data, provide engineering support during qualification and maintain a roadmap that gives customers confidence in long-term availability.
Supply Chain Analysis
NAND flash and controller silicon
TLC or QLC NAND and high-performance controllers determine capacity, latency and endurance. Value capture at this stage depends on quality consistency, technical differentiation and the difficulty of substitution. Bottlenecks can propagate downstream when qualification or material compatibility prevents customers from shifting quickly to an alternative supplier.
Enterprise SSD assembly and firmware
Drives are assembled with power-loss protection, telemetry, security and quality-of-service firmware. Value capture at this stage depends on quality consistency, technical differentiation and the difficulty of substitution. Bottlenecks can propagate downstream when qualification or material compatibility prevents customers from shifting quickly to an alternative supplier.
Server qualification
OEMs and cloud operators validate performance, thermals, endurance and management features. Value capture at this stage depends on quality consistency, technical differentiation and the difficulty of substitution. Bottlenecks can propagate downstream when qualification or material compatibility prevents customers from shifting quickly to an alternative supplier.
AI infrastructure deployment
SSDs feed training data, model checkpoints, vector databases and inference pipelines. Value capture at this stage depends on quality consistency, technical differentiation and the difficulty of substitution. Bottlenecks can propagate downstream when qualification or material compatibility prevents customers from shifting quickly to an alternative supplier.
Recent Developments
Recent developments show how technology roadmaps, manufacturing investment and standards are changing the competitive requirements of the enterprise ssd for ai market. The entries below are selected for their direct relevance to product capability, capacity, qualification or downstream adoption rather than for general corporate activity.
Micron’s data-center SSD portfolio includes the 9650 PCIe Gen6 product, positioned at up to twice the performance of PCIe Gen5 drives for AI systems, alongside the 9550 Gen5 family. Source
Micron continues to position the 9550 NVMe SSD for demanding AI workloads with up to 14.0 GB/s sequential read, 10.0 GB/s write and capacities to 30.72 TB. Source
KIOXIA continues enterprise PCIe 5.0 SSD commercialization through the CM7 family, supporting modern server form factors and high-performance data-center workloads. Source
Report Scope & Segmentation
| Report attribute | Coverage |
|---|---|
| Market | Enterprise SSD for AI |
| Base Year | 2025 |
| Estimated Year | 2026 |
| Forecast Period | 2026–2034 |
| 2025 Market Size | USD 527.0 million |
| 2026 Estimated Size | USD 571.1 million |
| 2034 Forecast Size | USD 1.09 billion |
| CAGR (2026–2034) | 8.4% |
| Largest Market in 2025 | North America |
| By Type | PCIe 4.0 SSDs, PCIe 5.0 SSDs, PCIe 6.0 & Emerging SSDs |
| By Application | AI Training, AI Inference, Vector Databases & RAG, HPC & Real-Time Analytics |
| By Capacity | Below 8 TB, 8–30.72 TB, Above 30.72 TB |
| By Form Factor | U.2 / U.3, E1.S / E1.L, E3.S / E3.L |
| Companies Profiled | Samsung Electronics, Micron Technology, KIOXIA, SK hynix / Solidigm, Western Digital / SanDisk, Seagate Technology, Kingston Technology, Intel |
Frequently Asked Questions
What was the enterprise ssd for ai market size in 2025?
The global enterprise ssd for ai market was valued at USD 527.0 million in 2025. The 2025 value is the base-year reference used throughout this overview and provides the starting point for comparing the 2026 estimate and the 2034 forecast.
What is the projected enterprise ssd for ai market size in 2034?
The global market is projected to reach USD 1.09 billion by 2034. The forecast reflects the interaction of downstream adoption, technology transitions, production investment and the qualification requirements that shape commercial conversion across the market.
What is the CAGR of the enterprise ssd for ai market from 2026 to 2034?
The enterprise ssd for ai market is expected to expand at a 8.4% CAGR during 2026–2034. Growth will not be uniform across every product or region because customer qualification, capital cycles, supply constraints and application-specific performance requirements differ materially.
Which region is the largest enterprise ssd for ai market?
North America is the largest regional market in 2025. Its position is supported by the concentration of relevant manufacturing, customers, technology ecosystems and downstream demand, although the precise commercial mechanism differs by country and application.
How is the enterprise ssd for ai market segmented by type?
By type, the market is segmented into PCIe 4.0 SSDs, PCIe 5.0 SSDs, PCIe 6.0 & Emerging SSDs. These categories address different performance, cost and integration requirements, so supplier selection depends on application fit, qualification history and lifecycle economics rather than a single specification.
What are the main applications for enterprise ssd for ai?
By application, the market covers AI Training, AI Inference, Vector Databases & RAG, HPC & Real-Time Analytics. Demand in each application is linked to platform launches, capacity expansion, equipment replacement and the degree to which the technology improves system performance or manufacturing economics.
What are the principal growth drivers for the enterprise ssd for ai market?
Key drivers include ai models increase storage throughput requirements and pcie generations keep raising host bandwidth. These forces increase demand when customers can translate technical improvements into higher performance, lower cost, greater reliability or access to new applications.
What are the main restraints in the enterprise ssd for ai market?
Important restraints include enterprise nand pricing is cyclical and qualification cycles are long. The resulting qualification burden, purchasing cyclicality and technology risk can extend sales cycles and limit rapid supplier substitution even when new solutions offer clear technical advantages.
Which companies participate in the enterprise ssd for ai market?
Companies profiled in this market include Samsung Electronics, Micron Technology, KIOXIA, SK hynix / Solidigm, Western Digital / SanDisk, Seagate Technology, Kingston Technology, Intel. Competition centers on qualification, product or process performance, manufacturing consistency, customer support and the ability to maintain supply across the required product lifecycle.
What should buyers evaluate when selecting a enterprise ssd for ai supplier?
Buyers should evaluate demonstrated performance, qualification history, manufacturing consistency, lifecycle support, supply resilience and integration cost. A technically strong product can still face adoption barriers if it increases redesign risk, lacks regional support or cannot meet the customer’s production and quality requirements.
Research Sources & Evidence Base
View research sources used for this overview.
- Micron Technology. 9550 NVMe SSD — AI workload positioning, Gen5 performance and capacity.
- Micron Technology. Data Center SSD Portfolio — PCIe Gen6 and Gen5 enterprise SSD roadmap for AI and cloud.
- KIOXIA. CM7 Enterprise SSD — PCIe 5.0 enterprise SSD platform and data-center form factors.
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