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Lisa Su explains why GPUs from $AMD & $NVDA will continue to dominate AI accelerators for years since developers need programmability & flexibility that ASICs like $GOOGL TPUs can’t match. Even as ASICs improve for inference, GPUs remain the default platform.

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78,771 просмотров • 9 месяцев назад •via X (Twitter)

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$AMD will be a multi-trillion $ company. The AI accelerator TAM will exceed $1T by 2030, and the market will only keep growing. As Lisa Su says: “We’re in a market where you can think of computing as equating to intelligence. If you can get more intelligence, why wouldn’t you want more computing?” While big tech like $GOOGL with TPUs and $AMZN with Trainium are building their own ASICs, GPUs—especially $NVDA and $AMD —are expected to dominate, representing 70–75% of the AI accelerator market by the end of 2030.

$AMD will be a multi-trillion $ company. The AI accelerator TAM will exceed $1T by 2030, and the market will only keep growing. As Lisa Su says: “We’re in a market where you can think of computing as equating to intelligence. If you can get more intelligence, why wouldn’t you want more computing?” While big tech like $GOOGL with TPUs and $AMZN with Trainium are building their own ASICs, GPUs—especially $NVDA and $AMD —are expected to dominate, representing 70–75% of the AI accelerator market by the end of 2030.

Daniel

45,899 просмотров • 10 месяцев назад

Peter Thiel says the quiet part loud: AI chips will get commoditized. This is why I avoid $NVDA. It made an immense amount of money marking up its GPUs by 10x and others had to pay as alternatives weren’t good enough. Now they are getting good enough. $AMD has already caught up with $NVDA in hardware performance, and ASICS are proving unexpectedly competitive due to their efficiency. $GOOG trained Gemini 3 solely on TPUs, and Anthropic is running a significant part of its training and inference workloads on Trainium clusters. As alternatives become even stronger, $NVDA margins and volumes will erode, and profits will shift from hardware to the application layer.

Peter Thiel says the quiet part loud: AI chips will get commoditized. This is why I avoid $NVDA. It made an immense amount of money marking up its GPUs by 10x and others had to pay as alternatives weren’t good enough. Now they are getting good enough. $AMD has already caught up with $NVDA in hardware performance, and ASICS are proving unexpectedly competitive due to their efficiency. $GOOG trained Gemini 3 solely on TPUs, and Anthropic is running a significant part of its training and inference workloads on Trainium clusters. As alternatives become even stronger, $NVDA margins and volumes will erode, and profits will shift from hardware to the application layer.

Oguz Erkan

461,874 просмотров • 9 месяцев назад

Groq founder Jonathan Ross explains why their architecture is better for inference than GPUs. Inference doesn't require as much compute power as training. What matters in inference is speed, cost, and energy consumption. $NVDA was going to lose market share as AI workloads shift to inference. They saw this and made a highly strategic move by striking a deal with Groq. $NVDA is already dominating training, and now it's also well-positioned to dominate inference.

Groq founder Jonathan Ross explains why their architecture is better for inference than GPUs. Inference doesn't require as much compute power as training. What matters in inference is speed, cost, and energy consumption. $NVDA was going to lose market share as AI workloads shift to inference. They saw this and made a highly strategic move by striking a deal with Groq. $NVDA is already dominating training, and now it's also well-positioned to dominate inference.

Oguz Erkan

97,186 просмотров • 9 месяцев назад

$AMD is becoming a two-engine AI story with EPYC CPUs for the growing compute needed around agentic workloads and Instinct GPUs for hyperscalers that need a credible alternative to $NVDA. The CPU side still looks underappreciated as AI agents require more high-core-count server compute and 2nm Venice should help AMD keep taking share as $INTC works through roadmap and manufacturing challenges. The bridge is Helios where AMD can package EPYC CPUs, Instinct GPUs and Pensando networking into a full rack-scale AI platform backed by real demand from $META, $ORCL and OpenAI.

$AMD is becoming a two-engine AI story with EPYC CPUs for the growing compute needed around agentic workloads and Instinct GPUs for hyperscalers that need a credible alternative to $NVDA. The CPU side still looks underappreciated as AI agents require more high-core-count server compute and 2nm Venice should help AMD keep taking share as $INTC works through roadmap and manufacturing challenges. The bridge is Helios where AMD can package EPYC CPUs, Instinct GPUs and Pensando networking into a full rack-scale AI platform backed by real demand from $META, $ORCL and OpenAI.

Shay Boloor

89,204 просмотров • 3 месяцев назад

Jensen Huang $NVDA says the AI infrastructure buildout will continue to grow exponentially for the next 7–8 years before reaching a level that will need to be sustained. $AMZN, $META, $GOOGL, and $MSFT are expected to spend around ~$700B in capex in 2026, up ~60% vs 2025. Growth rates like this are likely to persist for several more years. Betting on chipmakers like $NVDA, $AMD, and $AVGO remains one of the safest and most compelling plays.

Jensen Huang $NVDA says the AI infrastructure buildout will continue to grow exponentially for the next 7–8 years before reaching a level that will need to be sustained. $AMZN, $META, $GOOGL, and $MSFT are expected to spend around ~$700B in capex in 2026, up ~60% vs 2025. Growth rates like this are likely to persist for several more years. Betting on chipmakers like $NVDA, $AMD, and $AVGO remains one of the safest and most compelling plays.

Daniel

31,591 просмотров • 8 месяцев назад

$NVDA CEO Jensen Huang said it clearly AI data centers need gigawatts of power. Power availability decides where they get built & GPUs do nothing if the grid cannot feed them. Value is moving to the companies that build & control power for AI.

$NVDA CEO Jensen Huang said it clearly AI data centers need gigawatts of power. Power availability decides where they get built & GPUs do nothing if the grid cannot feed them. Value is moving to the companies that build & control power for AI.

Shay Boloor

362,255 просмотров • 11 месяцев назад

Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.

Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.

Brian Roemmele

87,244 просмотров • 2 лет назад

Mark Zuckerberg says “the value of even an H100 at this point is higher than it was last year” as AI compute remains scarce challenging the idea that older $NVDA GPUs quickly lose their value. Thats also why Meta benefits from owning its data centers because it can keep using GPUs long after they're fully depreciated instead of paying cloud providers year after year. The same logic extends to $NBIS, $CRWV and $IREN because if GPUs remain productive longer than expected then they can generate more revenue from the same hardware without needing to replace it as quickly.

Mark Zuckerberg says “the value of even an H100 at this point is higher than it was last year” as AI compute remains scarce challenging the idea that older $NVDA GPUs quickly lose their value. Thats also why Meta benefits from owning its data centers because it can keep using GPUs long after they're fully depreciated instead of paying cloud providers year after year. The same logic extends to $NBIS, $CRWV and $IREN because if GPUs remain productive longer than expected then they can generate more revenue from the same hardware without needing to replace it as quickly.

Shay Boloor

85,749 просмотров • 1 день назад

NeurochainAI is the all-in-one platform for developers to build AI dApps including GPU DePIN, Data Layer, and AI model layer. But what does it actually mean? AI is a $1.8 trillion industry, and NeurochainAI is tapping into 1% of that - as an infrastructure of interconnected GPUs, AI models, and data. Even simpler? Think of us as Ethereum for AI🧠🌐

NeurochainAI is the all-in-one platform for developers to build AI dApps including GPU DePIN, Data Layer, and AI model layer. But what does it actually mean? AI is a $1.8 trillion industry, and NeurochainAI is tapping into 1% of that - as an infrastructure of interconnected GPUs, AI models, and data. Even simpler? Think of us as Ethereum for AI🧠🌐

Neurochain.AI

106,135 просмотров • 2 лет назад

First time I have seen $NVDA's Jensen having some issues in explaining $NVDA's moat vs ASICs like $GOOGL TPUs and $AMZN Trainium. Why does Anthropic use TPUs and Trainium? Jensen: "Anthropic is a unique instance, not a trend" I really got to tip my hat to Dwarkesh Patel; he is one of the rare interviewers who first gets extremely well-prepared for an interview and, secondly, is true to his viewers more than the guests and asks tough questions, even though the interviewee might not like that.

First time I have seen $NVDA's Jensen having some issues in explaining $NVDA's moat vs ASICs like $GOOGL TPUs and $AMZN Trainium. Why does Anthropic use TPUs and Trainium? Jensen: "Anthropic is a unique instance, not a trend" I really got to tip my hat to Dwarkesh Patel; he is one of the rare interviewers who first gets extremely well-prepared for an interview and, secondly, is true to his viewers more than the guests and asks tough questions, even though the interviewee might not like that.

Rihard Jarc

348,640 просмотров • 6 месяцев назад

BULLISH AMD: The CPU and GPU Boom Materializes $AMD 1) Guidance of 100%+ datacenter revenue growth 2) Agentic AI shifts workload from GPUs to CPUs 3) Significant profit margin expansion underway 4) AMD leads in desktop powered compute for AI AI has spend years positioning itself for the moment that AI compute shifts from datacenters to work and home desktops. I believe that moment is upon us. AMD's datacenter growth is spectacular at the very same time, and no other semiconductor has the ability to expand profit margins to multiply earnings like AMD in my opinion.

BULLISH AMD: The CPU and GPU Boom Materializes $AMD 1) Guidance of 100%+ datacenter revenue growth 2) Agentic AI shifts workload from GPUs to CPUs 3) Significant profit margin expansion underway 4) AMD leads in desktop powered compute for AI AI has spend years positioning itself for the moment that AI compute shifts from datacenters to work and home desktops. I believe that moment is upon us. AMD's datacenter growth is spectacular at the very same time, and no other semiconductor has the ability to expand profit margins to multiply earnings like AMD in my opinion.

John Tinsman

61,810 просмотров • 27 дней назад

$NVDA CEO Jensen Huang is saying there’s no bubble because AI manufactures intelligence in real time which means every query consumes fresh compute & forces the system to scale its GPU base as usage grows. That is why demand for GPUs & power keeps accelerating & why this looks like a genuine industrial build-out rather than a bubble.

$NVDA CEO Jensen Huang is saying there’s no bubble because AI manufactures intelligence in real time which means every query consumes fresh compute & forces the system to scale its GPU base as usage grows. That is why demand for GPUs & power keeps accelerating & why this looks like a genuine industrial build-out rather than a bubble.

Shay Boloor

405,773 просмотров • 11 месяцев назад

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!

Mike

711,006 просмотров • 1 год назад

HOW AI IS ACTUALLY BEING BUILT (Cheat Sheet) Jenson Huang CEO of NVDA just walked through the stack in this order. These are the companies to know for each layer. ENERGY Nuclear / Baseload • $CEG • $BWXT • $SMR • $OKLO • $LEU • $UUUU Gas / Power Supply • $TLN • $VST • $NRG • $ET CHIPS GPUs / Accelerators • $NVDA • $AMD Networking / Custom Silicon • $AVGO • $MRVL Memory / Storage • $MU • $WDC • $STX Architecture / Tools • $ARM • $ASML • $LRCX • $KLAC INFRASTRUCTURE Data Centers / Real Assets • $EQIX • $DLR • $IRM Power, Cooling & Electrical • $VRT • $ETN • $HUBB • $PWR HPC / AI-First Infrastructure • $NBIS • $APLD • $IREN • $RIOT • $CIFR MODELS Foundation / Platform Models • $MSFT • $GOOGL • $META Cloud Distribution • $AMZN • $ORCL APPLICATIONS Enterprise / SaaS • $CRM • $NOW • $SNOW • $DDOG • $MDB Creative / Productivity • $ADBE • $INTU Defense / Automation • $PLTR • $KTOS • $AVAV Robotics / Physical AI • $TSLA • $SYM • $SERV • $RR

HOW AI IS ACTUALLY BEING BUILT (Cheat Sheet) Jenson Huang CEO of NVDA just walked through the stack in this order. These are the companies to know for each layer. ENERGY Nuclear / Baseload • $CEG • $BWXT • $SMR • $OKLO • $LEU • $UUUU Gas / Power Supply • $TLN • $VST • $NRG • $ET CHIPS GPUs / Accelerators • $NVDA • $AMD Networking / Custom Silicon • $AVGO • $MRVL Memory / Storage • $MU • $WDC • $STX Architecture / Tools • $ARM • $ASML • $LRCX • $KLAC INFRASTRUCTURE Data Centers / Real Assets • $EQIX • $DLR • $IRM Power, Cooling & Electrical • $VRT • $ETN • $HUBB • $PWR HPC / AI-First Infrastructure • $NBIS • $APLD • $IREN • $RIOT • $CIFR MODELS Foundation / Platform Models • $MSFT • $GOOGL • $META Cloud Distribution • $AMZN • $ORCL APPLICATIONS Enterprise / SaaS • $CRM • $NOW • $SNOW • $DDOG • $MDB Creative / Productivity • $ADBE • $INTU Defense / Automation • $PLTR • $KTOS • $AVAV Robotics / Physical AI • $TSLA • $SYM • $SERV • $RR

Justin Banks

31,507 просмотров • 8 месяцев назад

Gavin Baker (Gavin Baker) says the disaggregation of inference can extend GPU useful lives from 3-4 years to 10-15. That may single-handedly save private credit and reduce the financing rates for GPUs, which will drive demand and help finance the build-out. "The disaggregation of prefill and inference is going to be amazing for the useful lives of GPU and may single-handedly save private credit. Private credit is in pain from these SaaS loans. But there's a lot of private credit in GPUs too. They were underwriting that to 3-4. The disaggregation of inference means that these GPUs are going to have 10 or 15-year lives. The AI skeptics are like, "Oh, these companies are all cooking their books. The useful life of a GPU is only a year or two. The useful life of a CPU is only four years because the rapid technological change." No. What rapid technological change has done with the disaggregation of prefill and inference is you can put a Cerebras system or Groq LPUs effectively in front of a Hopper or even an Ampere, use that Hopper and Ampere for prefill, and extend the useful life of that GPU until it melts. This is going to be really good for the whole private credit industry. It's gonna help finance the AI build-out. Because if you can start to finance GPUs at 5% or 6% instead of – I think CoreWeave's lowest financing was low sevens – that actually mathematically changes the cost to finance this build-out."

Gavin Baker (Gavin Baker) says the disaggregation of inference can extend GPU useful lives from 3-4 years to 10-15. That may single-handedly save private credit and reduce the financing rates for GPUs, which will drive demand and help finance the build-out. "The disaggregation of prefill and inference is going to be amazing for the useful lives of GPU and may single-handedly save private credit. Private credit is in pain from these SaaS loans. But there's a lot of private credit in GPUs too. They were underwriting that to 3-4. The disaggregation of inference means that these GPUs are going to have 10 or 15-year lives. The AI skeptics are like, "Oh, these companies are all cooking their books. The useful life of a GPU is only a year or two. The useful life of a CPU is only four years because the rapid technological change." No. What rapid technological change has done with the disaggregation of prefill and inference is you can put a Cerebras system or Groq LPUs effectively in front of a Hopper or even an Ampere, use that Hopper and Ampere for prefill, and extend the useful life of that GPU until it melts. This is going to be really good for the whole private credit industry. It's gonna help finance the AI build-out. Because if you can start to finance GPUs at 5% or 6% instead of – I think CoreWeave's lowest financing was low sevens – that actually mathematically changes the cost to finance this build-out."

Invest Like the Best

211,336 просмотров • 4 месяцев назад

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026

Mike

145,992 просмотров • 4 месяцев назад

$AMZN AWS CEO: “We invest on 5-year commitments from customers, so we have great visibility for revenues and when they’ll land.” AWS also recently implied that server investments pay off in under 3 years, so we are looking at 2 years of pure returns after investment is recovered. They also depreciate chips in 5 years, so every year the equipment stays useful beyond 5 years substantially add to operating margins. Plus, these chips will likely be rented at a premium on the current contract prices as demand far outstrips supply. Rental rates for even 6-year old A100 chips are up 20% YTD. And note that all these economics will get even better as most servers shift to ASICs from third party accelerators. Can’t imagine a more bullish scenario for hyperscalers. $AMZN $MSFT $GOOG $ORCL

$AMZN AWS CEO: “We invest on 5-year commitments from customers, so we have great visibility for revenues and when they’ll land.” AWS also recently implied that server investments pay off in under 3 years, so we are looking at 2 years of pure returns after investment is recovered. They also depreciate chips in 5 years, so every year the equipment stays useful beyond 5 years substantially add to operating margins. Plus, these chips will likely be rented at a premium on the current contract prices as demand far outstrips supply. Rental rates for even 6-year old A100 chips are up 20% YTD. And note that all these economics will get even better as most servers shift to ASICs from third party accelerators. Can’t imagine a more bullish scenario for hyperscalers. $AMZN $MSFT $GOOG $ORCL

Oguz Erkan

29,884 просмотров • 2 месяцев назад

$AMD MI400 series & Sam Altman OpenAI Sam Alman praised MI400 series GPUs and MI450, and stated “The memory architecture is great for inference [and] for training as well.” and a "Game changer" for frontier AI Models Ok we have Elon Musk xAI , Mark Zuckerberg $META, Sam Alman OpenAI , Tareq Amin( CEO of Saudi), Satya Nadella $MSFT, Andy Jassy $AMZN, Larry Ellison $ORCL and many CEOs from large to small provided positive feedbacks on MI400 series rack solution, which is expected to deliver up to 10x inference performance over piror generations. And short sellers/bears are still beating down on $AMD H2 2025 and 2026? AMD will hit new ATH before year end. Not Financial Advice!

$AMD MI400 series & Sam Altman OpenAI Sam Alman praised MI400 series GPUs and MI450, and stated “The memory architecture is great for inference [and] for training as well.” and a "Game changer" for frontier AI Models Ok we have Elon Musk xAI , Mark Zuckerberg $META, Sam Alman OpenAI , Tareq Amin( CEO of Saudi), Satya Nadella $MSFT, Andy Jassy $AMZN, Larry Ellison $ORCL and many CEOs from large to small provided positive feedbacks on MI400 series rack solution, which is expected to deliver up to 10x inference performance over piror generations. And short sellers/bears are still beating down on $AMD H2 2025 and 2026? AMD will hit new ATH before year end. Not Financial Advice!

Mike

89,098 просмотров • 1 год назад