[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  FatumOpes [@D27357](/creator/twitter/D27357) on x XXX followers Created: 2025-07-24 17:04:40 UTC 🥇 $MU Micron has secured first order for volume production of SOCAMM for NVIDIA ahead of its competitors, $SSUN Samsung and $HY9H SK Hynix, who were also involved in co-development. NVIDIA is set to order between XXXXXXX and XXXXXXX SOCAMM modules for its 2025 AI systems. Compared to HBM, SOCAMM is cheaper (25–33% of HBM's cost), uses less power, and is modular — but not as high in raw bandwidth as HBM. It fits niches where “moderate” bandwidth with high capacity and efficiency matters. SOCAMM stands for System on Chip Advanced Memory Module. It's a new type of memory designed to meet the specific demands of certain AI applications, particularly where low power consumption and a compact form factor are crucial. Think of SOCAMM as a highly advanced, modular memory building block specifically engineered for the AI era. How SOCAMM Works (Simply Explained): Combination of Technologies: SOCAMM is a blend of two existing memory technologies: LPDDR (Low-Power Double Data Rate): This is a type of DRAM (working memory) known for its low power consumption, typically used in mobile devices like smartphones. CAMM (Compression Attached Memory Module): This is a newer, compact, and modular form factor for memory, designed to replace traditional, larger memory modules (like SO-DIMMs or RDIMMs). Modular Design: Unlike conventional memory that is often soldered directly onto the motherboard, SOCAMM is a removable module. This means it can be easily swapped out or upgraded when needed, similar to a RAM stick in a laptop, but in a much more compact design. "System on Chip" (SoC) Integration: The name "System on Chip Advanced Memory Module" indicates that this memory works closely with the main processor (the "System on Chip," e.g., a CPU or GPU). It's even being developed with co-packaging technology to integrate the processor and memory into a single unit, minimizing the distance data has to travel. Advanced Manufacturing: SOCAMM's development relies on advanced manufacturing processes like 3D packaging (vertically stacking chips) and Through-Silicon Vias (TSVs) (tiny vertical connections through the chip) to maximize performance and minimize footprint. Why is SOCAMM Important for AI? SOCAMM is crucial for certain AI applications because it offers the following benefits: -High Performance with Low Power Consumption: AI applications demand significant computing power. SOCAMM offers XXX times more bandwidth while reducing size and power consumption by a third compared to standard server RDIMM modules. This is especially important for energy-efficient AI data centers and devices. -Compact Form Factor: Its space-saving design enables the development of "palm-sized PCs with high AI capabilities" and more compact AI servers or workstations. -Efficient Data Transfer: It's designed to increase data transfer speeds and reduce latency, which is essential for training AI models with trillions of parameters and for real-time AI applications. -Modular Upgradability: The modular design facilitates easier upgrades and maintenance, improving the lifespan and adaptability of AI systems. -Cost-Effectiveness: SOCAMM is expected to be more cost-effective than conventional DRAM using SO-DIMMs, which could promote broader adoption of AI hardware. SOCAMM and HBM: Complementary, Not Replacement It is critical to understand that SOCAMM is not intended to replace High Bandwidth Memory (HBM) for the highest-end AI chips. HBM still holds a significant advantage for AI applications sensitive to extreme memory bandwidth, particularly for GPUs like NVIDIA's Blackwell. Instead, SOCAMM is a complementary solution for other types of AI platforms. It aims to make systems that currently use soldered LPDDR memory (like NVIDIA's Grace platforms or certain AI PCs) more modular and efficient. It expands the spectrum of AI memory solutions to meet the diverse requirements of the growing AI ecosystem. Who is Developing SOCAMM? NVIDIA has been leading the development of SOCAMM in collaboration with major memory manufacturers: Samsung Electronics, SK Hynix, and Micron Technology. Micron has secured approval for volume production ahead of its competitors, Samsung and SK Hynix, marking a significant early success for Micron in this specific, emerging standard. #AIMemory #AI #Micron #SKHynix #Samsung #NVIDIA XXX engagements  **Related Topics** [coins ai](/topic/coins-ai) [$000660ks](/topic/$000660ks) [$hy9h](/topic/$hy9h) [samsung](/topic/samsung) [$ssun](/topic/$ssun) [$mu](/topic/$mu) [stocks technology](/topic/stocks-technology) [$nvda](/topic/$nvda) [Post Link](https://x.com/D27357/status/1948429137040797906)
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FatumOpes @D27357 on x XXX followers
Created: 2025-07-24 17:04:40 UTC
🥇 $MU Micron has secured first order for volume production of SOCAMM for NVIDIA ahead of its competitors, $SSUN Samsung and $HY9H SK Hynix, who were also involved in co-development. NVIDIA is set to order between XXXXXXX and XXXXXXX SOCAMM modules for its 2025 AI systems.
Compared to HBM, SOCAMM is cheaper (25–33% of HBM's cost), uses less power, and is modular — but not as high in raw bandwidth as HBM. It fits niches where “moderate” bandwidth with high capacity and efficiency matters.
SOCAMM stands for System on Chip Advanced Memory Module. It's a new type of memory designed to meet the specific demands of certain AI applications, particularly where low power consumption and a compact form factor are crucial.
Think of SOCAMM as a highly advanced, modular memory building block specifically engineered for the AI era.
How SOCAMM Works (Simply Explained): Combination of Technologies: SOCAMM is a blend of two existing memory technologies: LPDDR (Low-Power Double Data Rate): This is a type of DRAM (working memory) known for its low power consumption, typically used in mobile devices like smartphones. CAMM (Compression Attached Memory Module): This is a newer, compact, and modular form factor for memory, designed to replace traditional, larger memory modules (like SO-DIMMs or RDIMMs).
Modular Design: Unlike conventional memory that is often soldered directly onto the motherboard, SOCAMM is a removable module. This means it can be easily swapped out or upgraded when needed, similar to a RAM stick in a laptop, but in a much more compact design.
"System on Chip" (SoC) Integration: The name "System on Chip Advanced Memory Module" indicates that this memory works closely with the main processor (the "System on Chip," e.g., a CPU or GPU). It's even being developed with co-packaging technology to integrate the processor and memory into a single unit, minimizing the distance data has to travel.
Advanced Manufacturing: SOCAMM's development relies on advanced manufacturing processes like 3D packaging (vertically stacking chips) and Through-Silicon Vias (TSVs) (tiny vertical connections through the chip) to maximize performance and minimize footprint.
Why is SOCAMM Important for AI?
SOCAMM is crucial for certain AI applications because it offers the following benefits:
-High Performance with Low Power Consumption: AI applications demand significant computing power. SOCAMM offers XXX times more bandwidth while reducing size and power consumption by a third compared to standard server RDIMM modules. This is especially important for energy-efficient AI data centers and devices.
-Compact Form Factor: Its space-saving design enables the development of "palm-sized PCs with high AI capabilities" and more compact AI servers or workstations.
-Efficient Data Transfer: It's designed to increase data transfer speeds and reduce latency, which is essential for training AI models with trillions of parameters and for real-time AI applications.
-Modular Upgradability: The modular design facilitates easier upgrades and maintenance, improving the lifespan and adaptability of AI systems.
-Cost-Effectiveness: SOCAMM is expected to be more cost-effective than conventional DRAM using SO-DIMMs, which could promote broader adoption of AI hardware.
SOCAMM and HBM: Complementary, Not Replacement
It is critical to understand that SOCAMM is not intended to replace High Bandwidth Memory (HBM) for the highest-end AI chips. HBM still holds a significant advantage for AI applications sensitive to extreme memory bandwidth, particularly for GPUs like NVIDIA's Blackwell.
Instead, SOCAMM is a complementary solution for other types of AI platforms. It aims to make systems that currently use soldered LPDDR memory (like NVIDIA's Grace platforms or certain AI PCs) more modular and efficient. It expands the spectrum of AI memory solutions to meet the diverse requirements of the growing AI ecosystem.
Who is Developing SOCAMM?
NVIDIA has been leading the development of SOCAMM in collaboration with major memory manufacturers: Samsung Electronics, SK Hynix, and Micron Technology. Micron has secured approval for volume production ahead of its competitors, Samsung and SK Hynix, marking a significant early success for Micron in this specific, emerging standard.
#AIMemory #AI #Micron #SKHynix #Samsung #NVIDIA
XXX engagements
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