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Micron discontinues 2GB GDDR7 chips, shifts focus to higher-density memory for AI

🔄 Updated 3h ago
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Key points

  • Micron ended production of 2GB GDDR7 chips.
  • These chips were used in some Nvidia RTX 50 cards.
  • Micron shifts to 3GB GDDR7 for AI GPUs.
  • Decision driven by higher profit margins in AI memory.

Micron Ends 2GB GDDR7 Production

Micron has ceased the production and sale of its 2GB (16Gb) GDDR7 memory chips. This includes both the 28 Gbps and 32 Gbps versions. These chips are now listed as "End of Life" on Micron's website, with product pages no longer returning results.

Limited Impact on Consumer GPUs

Micron began supplying 2GB GDDR7 to Nvidia earlier this year, but its chips were only used in a few models, such as retail variants of the GeForce RTX 5060. Samsung and SK Hynix supply the majority of 2GB GDDR7 modules for the standard RTX 50 lineup, minimizing the risk of immediate shortages due to Micron's exit. Micron's uncompetitive position in the 2GB segment likely contributed to this decision.

Shift to Higher-Density AI Memory

The company is now focusing on higher-density 3GB (24Gb) GDDR7 chips. While these larger packages are more expensive and less practical for consumer RTX 50 GPUs, they offer higher profit margins. These 3GB chips are utilized in Nvidia's high-end RTX PRO Blackwell GPUs, which are designed for generative and agentic AI workloads.

Industry Trend Towards AI Prioritization

Micron's decision aligns with a broader trend among memory suppliers to prioritize AI-facing technologies over consumer products. The company previously exited its Crucial consumer memory and SSD business, citing surging AI data center demand as a reason to focus on larger strategic customers in faster-growing segments.

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Reporting from

Micron has discontinued production and sales of its 2GB GDDR7 memory chips, including 28 Gbps and 32 Gbps variants. The company is pivoting to higher-density 3GB GDDR7 chips, which are more profitable and used in AI-focused GPUs, reflecting a broader industry trend of prioritizing AI memory solutions.