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Creator of High Bandwidth Memory: "How much HBM a country possesses determines national AI hegemony and the competitiveness of AI companies. Memory determines which is better: Google Gemini, OpenAI, or Anthropic Claude."

Fireside Alpha

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98,087 просмотров • 3 месяцев назад •via X (Twitter)

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OpenAI founder Greg Brockman said that future GDP growth will be driven by how much compute a country can deploy as AI agents begin running for hours or days for every person. He added that scaling this world requires billions of GPUs which is why OpenAI is optimizing inference on $AMD MI455 around bandwidth & HBM memory per dollar rather than raw peak specs.

OpenAI founder Greg Brockman said that future GDP growth will be driven by how much compute a country can deploy as AI agents begin running for hours or days for every person. He added that scaling this world requires billions of GPUs which is why OpenAI is optimizing inference on $AMD MI455 around bandwidth & HBM memory per dollar rather than raw peak specs.

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We just launched a new primitive for AI Walrus Memory gives your agents persistent, portable memory that you own, and it drops straight into the platforms you already build on. Anthropic Claude, OpenAI ChatGPT, and Google Gemini, you name it plus native MCP and SDKs, added in a few lines of code LIVE TODAY

We just launched a new primitive for AI Walrus Memory gives your agents persistent, portable memory that you own, and it drops straight into the platforms you already build on. Anthropic Claude, OpenAI ChatGPT, and Google Gemini, you name it plus native MCP and SDKs, added in a few lines of code LIVE TODAY

Adeniyi.sui

46,276 просмотров • 3 месяцев назад

SK Hynix's Hoshik Kim admits high bandwidth memory "is not the answer" to the memory-wall problem, and SK is working on different solutions to solve the bottleneck. "No, actually, I kind of agree with Pat. So HBM is not the answer." [SK Hynix is admitting that HBM is a bad memory?] "Nothing is a bad memory. It's a good memory, but it's not the final answer. So actually, HBM will not solve the memory-wall problem. Memory-wall problem is an inherent AI problem, which you cannot avoid." "So we try our best to provide higher bandwidth with this memory, with HBM technology even though it has some limitation, as Pat already mentioned, with thermal, IOs, cost, a lot of stuff. "But that's the only thing we got right now to help ease this memory-wall problem, memory-bottleneck problem. But at the same time, we are working in different directions to solve this memory-wall problem with different solutions."

SK Hynix's Hoshik Kim admits high bandwidth memory "is not the answer" to the memory-wall problem, and SK is working on different solutions to solve the bottleneck. "No, actually, I kind of agree with Pat. So HBM is not the answer." [SK Hynix is admitting that HBM is a bad memory?] "Nothing is a bad memory. It's a good memory, but it's not the final answer. So actually, HBM will not solve the memory-wall problem. Memory-wall problem is an inherent AI problem, which you cannot avoid." "So we try our best to provide higher bandwidth with this memory, with HBM technology even though it has some limitation, as Pat already mentioned, with thermal, IOs, cost, a lot of stuff. "But that's the only thing we got right now to help ease this memory-wall problem, memory-bottleneck problem. But at the same time, we are working in different directions to solve this memory-wall problem with different solutions."

Fireside Alpha

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Persistent memory is the Achilles heel of AI. Engramme’s Large Memory Models (LMMs) empower every app with persistent memory. Google solved search. OpenAI solved language. Engramme solved memory. Join beta:

Persistent memory is the Achilles heel of AI. Engramme’s Large Memory Models (LMMs) empower every app with persistent memory. Google solved search. OpenAI solved language. Engramme solved memory. Join beta:

Engramme

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$PLTR is one of the cleanest ways to play the AI cycle because it benefits from the entire infrastructure buildout without financing the data centers, chips, memory, power or networking underneath it. Palantir is also emerging as one of the biggest sovereign AI winners by building the control layer that determines what AI understands, which proprietary data it can access and what decisions or actions it can influence inside an organization. OpenAI and Anthropic create the intelligence while Palantir connects it to permissions, workflows and real operating systems where measurable economic value is produced.

$PLTR is one of the cleanest ways to play the AI cycle because it benefits from the entire infrastructure buildout without financing the data centers, chips, memory, power or networking underneath it. Palantir is also emerging as one of the biggest sovereign AI winners by building the control layer that determines what AI understands, which proprietary data it can access and what decisions or actions it can influence inside an organization. OpenAI and Anthropic create the intelligence while Palantir connects it to permissions, workflows and real operating systems where measurable economic value is produced.

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Micron's Sanjay Mehrotra on memory: "Memory is no longer a component in a system, memory is the strategic infrastructure for AI. It is no longer a commodity, it is a high value. Without memory, you cannot make AI smarter, cannot make it faster, you cannot scale up AI...AI is driving a whole hierarchy of memory requirements from HBM to DRAM to SSDs." Via CNBC $MU $DRAM $EWY $SNDK

Micron's Sanjay Mehrotra on memory: "Memory is no longer a component in a system, memory is the strategic infrastructure for AI. It is no longer a commodity, it is a high value. Without memory, you cannot make AI smarter, cannot make it faster, you cannot scale up AI...AI is driving a whole hierarchy of memory requirements from HBM to DRAM to SSDs." Via CNBC $MU $DRAM $EWY $SNDK

P Equity Research 📰

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Teaming up with SanDisk to build the next standard for AI inference. As a full-stack AI memory creator, SK hynix is launching the global standardization of #HBF with Sandisk & Open Compute Project (OCP). HBF is the new memory layer bridging the gap between #HBM and #SSD. #SKhynix

Teaming up with SanDisk to build the next standard for AI inference. As a full-stack AI memory creator, SK hynix is launching the global standardization of #HBF with Sandisk & Open Compute Project (OCP). HBF is the new memory layer bridging the gap between #HBM and #SSD. #SKhynix

SK hynix

194,727 просмотров • 7 месяцев назад

the most important thing about walrus memory is it works everywhere you're already building Anthropic Claude, OpenAI ChatGPT, and Google Gemini, with native mcp support and sdks for python, typescript and javascript plus, it's fully open source go start building

the most important thing about walrus memory is it works everywhere you're already building Anthropic Claude, OpenAI ChatGPT, and Google Gemini, with native mcp support and sdks for python, typescript and javascript plus, it's fully open source go start building

Adeniyi.sui

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$MU CEO Sanjay Mehrotra says AI is turning memory from a commodity into “strategic infrastructure.” AI cannot scale without more HBM DRAM and SSD capacity which is making memory far more valuable across the entire compute stack.

$MU CEO Sanjay Mehrotra says AI is turning memory from a commodity into “strategic infrastructure.” AI cannot scale without more HBM DRAM and SSD capacity which is making memory far more valuable across the entire compute stack.

Shay Boloor

194,460 просмотров • 1 месяц назад

Speaking of spot on. Gavin Gavin Baker was asked in early Jan'25 his prediction for best performing asset class of the year (as @jason references in latest The All-In Podcast): "I think the companies that make high bandwidth memory. Going back to "we're going to run out of compute," it's actually a pretty shocking stat: high bandwidth memory is a bigger part of NVIDIA's COGS on GPUs than Taiwan Semi is. And today there's two companies that can make it, SK Hynix and Micron. We'll see if Samsung gets their act together. But HBM, it's in NVIDIA GPUs, AMD GPUs, Amazon Trainiums. And particularly in a world of test-time compute and inference being so important, high bandwidth memory is arguably more important than it ever was. And it's been sold out for the last two years. So high bandwidth memory would be my pick."

Speaking of spot on. Gavin Gavin Baker was asked in early Jan'25 his prediction for best performing asset class of the year (as @jason references in latest The All-In Podcast): "I think the companies that make high bandwidth memory. Going back to "we're going to run out of compute," it's actually a pretty shocking stat: high bandwidth memory is a bigger part of NVIDIA's COGS on GPUs than Taiwan Semi is. And today there's two companies that can make it, SK Hynix and Micron. We'll see if Samsung gets their act together. But HBM, it's in NVIDIA GPUs, AMD GPUs, Amazon Trainiums. And particularly in a world of test-time compute and inference being so important, high bandwidth memory is arguably more important than it ever was. And it's been sold out for the last two years. So high bandwidth memory would be my pick."

Fireside Alpha

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AI inference is memory-bound more often than compute-bound. High Bandwidth Compute (HBC) flips the traditional model by moving compute to the data, helping unlock dramatic gains in memory bandwidth and efficiency. Learn more:

AI inference is memory-bound more often than compute-bound. High Bandwidth Compute (HBC) flips the traditional model by moving compute to the data, helping unlock dramatic gains in memory bandwidth and efficiency. Learn more:

Qualcomm

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$MU hit a $1T valuation today because the market is waking up to the idea that memory may actually become one of the core infrastructure layers of the AI economy. The catalyst was SK Hynix unveiling iHBM which is a new cooling technology for next-gen HBM but the bigger point is that token growth, AI factories, inference demand & next-gen accelerators all require more HBM per system. More HBM per accelerator means more memory dollar content & with only three major HBM suppliers in the world the entire memory complex is getting re-rated.

$MU hit a $1T valuation today because the market is waking up to the idea that memory may actually become one of the core infrastructure layers of the AI economy. The catalyst was SK Hynix unveiling iHBM which is a new cooling technology for next-gen HBM but the bigger point is that token growth, AI factories, inference demand & next-gen accelerators all require more HBM per system. More HBM per accelerator means more memory dollar content & with only three major HBM suppliers in the world the entire memory complex is getting re-rated.

Shay Boloor

306,107 просмотров • 4 месяцев назад

$MU $00060 $00530 $TSM Evolution of the AI Memory Hierarchy: HBM, DRAM, & SRAM Overview and Market Analysis The video outlines the strategic shift of modern computing toward a memory-centric architecture driven by the demands of generative AI. It explains that the industry is moving away from a traditional commodity model to a tightly coupled hierarchy of specialized technologies, where High Bandwidth Memory (HBM) and advanced packaging act as primary bottlenecks for performance. Leading manufacturers like SK hynix, Samsung, and Micron are navigating complex transitions to HBM4 and CXL-based expansion to overcome data movement constraints. Beyond the memory producers, the ecosystem relies on integration enablers like TSMC for packaging and specialized equipment providers for high-precision manufacturing. Ultimately, the analysis suggests that memory intensity and power efficiency have become the decisive factors in the success of gigawatt-scale data centers.

$MU $00060 $00530 $TSM Evolution of the AI Memory Hierarchy: HBM, DRAM, & SRAM Overview and Market Analysis The video outlines the strategic shift of modern computing toward a memory-centric architecture driven by the demands of generative AI. It explains that the industry is moving away from a traditional commodity model to a tightly coupled hierarchy of specialized technologies, where High Bandwidth Memory (HBM) and advanced packaging act as primary bottlenecks for performance. Leading manufacturers like SK hynix, Samsung, and Micron are navigating complex transitions to HBM4 and CXL-based expansion to overcome data movement constraints. Beyond the memory producers, the ecosystem relies on integration enablers like TSMC for packaging and specialized equipment providers for high-precision manufacturing. Ultimately, the analysis suggests that memory intensity and power efficiency have become the decisive factors in the success of gigawatt-scale data centers.

TheValueist

23,901 просмотров • 9 месяцев назад

Discovered Materials' Akash Ramdas argues the next chip scaling frontier isn't smaller transistors, it's how many you can stack on top of each other: HBM is already 12 high DRAM going to 16 high "We're starting to do this thing where we're taking a logic chip and then putting memory on top of it. That's where materials can make a difference, because now you're actually thermally limited. The thing that's preventing your scaling is no longer how small is your transistor, but how many transistors can you put on top of each other?" "The high bandwidth memory you'd have seen is a stack of dynamic random access memory. It's a 12-high dynamic random access memory stack right now. One of the roadmaps is to make this even higher, 16-high, and this is how you deal with memory bottlenecks in inference or training." "The next stage of that is putting that HBM on top of a logic die. Both these technologies are going to be partially enabled by new materials."

Discovered Materials' Akash Ramdas argues the next chip scaling frontier isn't smaller transistors, it's how many you can stack on top of each other: HBM is already 12 high DRAM going to 16 high "We're starting to do this thing where we're taking a logic chip and then putting memory on top of it. That's where materials can make a difference, because now you're actually thermally limited. The thing that's preventing your scaling is no longer how small is your transistor, but how many transistors can you put on top of each other?" "The high bandwidth memory you'd have seen is a stack of dynamic random access memory. It's a 12-high dynamic random access memory stack right now. One of the roadmaps is to make this even higher, 16-high, and this is how you deal with memory bottlenecks in inference or training." "The next stage of that is putting that HBM on top of a logic die. Both these technologies are going to be partially enabled by new materials."

MTS

17,566 просмотров • 1 месяц назад

The Father of HBM, Kim Jung-ho says physical AI is really a memory problem, because a self-driving car can't react faster than its memory can recall what it learned, and the stakes are: it cannot be a second late on the brakes. "Physical AI is memory AI." "The car crashes as it drives, remembers it all, and learns from it." "What's harder is the learning and reasoning must be real-time. A self-driving car can't hit the brakes a second late." "So here not just capacity, but speed matters too. Memory's speed." "Future memory will lead the AI revolution, and there it's speed and capacity. Speed is bandwidth, capacity is stacking. That's how I see it." ______ More from his lecture here:

The Father of HBM, Kim Jung-ho says physical AI is really a memory problem, because a self-driving car can't react faster than its memory can recall what it learned, and the stakes are: it cannot be a second late on the brakes. "Physical AI is memory AI." "The car crashes as it drives, remembers it all, and learns from it." "What's harder is the learning and reasoning must be real-time. A self-driving car can't hit the brakes a second late." "So here not just capacity, but speed matters too. Memory's speed." "Future memory will lead the AI revolution, and there it's speed and capacity. Speed is bandwidth, capacity is stacking. That's how I see it." ______ More from his lecture here:

Fireside Alpha

80,530 просмотров • 1 месяц назад

ELON MUSK: "How often is the most ironic outcome the most likely? Look at the names of AI companies. Midjourney is not mid. Stability AI is unstable. OpenAI is closed. Anthropic is misanthropic."

ELON MUSK: "How often is the most ironic outcome the most likely? Look at the names of AI companies. Midjourney is not mid. Stability AI is unstable. OpenAI is closed. Anthropic is misanthropic."

DogeDesigner

88,194 просмотров • 5 месяцев назад

The heads of AI companies OpenAI and Anthropic refused to hold hands in a group photo at the opening of a major AI summit in India. The rivalry between Sam Altman of OpenAI and Dario Amodei of Anthropic escalated this year when the latter ran attack ads during the Super Bowl.

The heads of AI companies OpenAI and Anthropic refused to hold hands in a group photo at the opening of a major AI summit in India. The rivalry between Sam Altman of OpenAI and Dario Amodei of Anthropic escalated this year when the latter ran attack ads during the Super Bowl.

Al Jazeera English

27,810 просмотров • 7 месяцев назад