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ICT Algorithmic Price Delivery 👁 Eye Training Drill 📉GBPJPY 💎High probability BMS #theicd

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Market Maker Trading

67,473 subscribers

46,849 次观看 • 2 年前 •via X (Twitter)

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8 条评论

The ICT Academy 的头像
The ICT Academy2 年前

Learn here:

The Professor 的头像
The Professor2 年前

That's a hell of a big trap

Alisher Khan 的头像
Alisher Khan2 年前

🤌🏼

Jossi 👨‍💻👨‍💻 ✨✨ 的头像
Jossi 👨‍💻👨‍💻 ✨✨2 年前

Please what date is this

sonu 的头像
sonu2 年前

How you took this -ob in 5m chart brother....?

Nayla D. Logia 的头像
Nayla D. Logia2 年前

Wow 💚💚💚 thanks for sharing

Dream Boy 的头像
Dream Boy2 年前

@downvideobot

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Dennis Chibuikem2 年前

@Savevidnow

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ICT private mentorship videos & forum discussions

15,561 次观看 • 1 年前

Most traders still think the edge is the entry. It’s not. Today both probability models aligned with the same objective BEFORE price even got there. London Probability Map: 73.5% probability to sweep the LOW first. Price delivered exactly there. New York Probability Map: 72.8% probability to sweep the LOW first. Another textbook A+ setup. Another delivery. Trade entry in both cases? Basic ICT iFVG execution in the direction of the more probable side. That’s the part most traders miss. The iFVG is not the edge. The probabilities are. When you already know where price is statistically more likely to go first… execution becomes calmer, cleaner and far less emotional. Would your trading change if you started every session already knowing where the odds were leaning first? #NQ #ICT
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Most traders still think the edge is the entry. It’s not. Today both probability models aligned with the same objective BEFORE price even got there. London Probability Map: 73.5% probability to sweep the LOW first. Price delivered exactly there. New York Probability Map: 72.8% probability to sweep the LOW first. Another textbook A+ setup. Another delivery. Trade entry in both cases? Basic ICT iFVG execution in the direction of the more probable side. That’s the part most traders miss. The iFVG is not the edge. The probabilities are. When you already know where price is statistically more likely to go first… execution becomes calmer, cleaner and far less emotional. Would your trading change if you started every session already knowing where the odds were leaning first? #NQ #ICT

Herman Trading

14,145 次观看 • 1 个月前

ICT EXPLAINS CHANGE IN THE STATE OF DELIVERY (CISD)💎💎 Bullish CISD - A close above the opening price of a down closed candle Bearish CISD - A close below the opening price of an up closed candle 🧠CISD🧠 - Occurs at the inception of EVERY price run - Occurs every single day, in every market " This is only 1 form of my bullish order block" - ICT How I find the most success using this logic👇👇 - Establish a HTF draw on liquidity - Look for LTF CISD at liquidity aligned with that HTF draw on liquidity/order flow THIS HAS THE MOST PRECISION ONCE YOU ARE ALIGNED WITH WHERE PRICE IS GOING HTF!!
3:14

ICT EXPLAINS CHANGE IN THE STATE OF DELIVERY (CISD)💎💎 Bullish CISD - A close above the opening price of a down closed candle Bearish CISD - A close below the opening price of an up closed candle 🧠CISD🧠 - Occurs at the inception of EVERY price run - Occurs every single day, in every market " This is only 1 form of my bullish order block" - ICT How I find the most success using this logic👇👇 - Establish a HTF draw on liquidity - Look for LTF CISD at liquidity aligned with that HTF draw on liquidity/order flow THIS HAS THE MOST PRECISION ONCE YOU ARE ALIGNED WITH WHERE PRICE IS GOING HTF!!

DanDowdTrading

48,079 次观看 • 2 年前

☢️"9:20-9:40 HYDRA macro" +4r "Turtle Soup" delivery on #DowJones What's the benefit of watching trade management recordings? 1. you will understand how to expect the price delivery 2. what pro traders think before executing 3. How they manage there SL & Trade 4. How the setup will form 5. what type of targets are High probability 6. Confidence 7. Patience Last but not least 8. How to align time+Price this is what providing value to the community looks like :)
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☢️"9:20-9:40 HYDRA macro" +4r "Turtle Soup" delivery on #DowJones What's the benefit of watching trade management recordings? 1. you will understand how to expect the price delivery 2. what pro traders think before executing 3. How they manage there SL & Trade 4. How the setup will form 5. what type of targets are High probability 6. Confidence 7. Patience Last but not least 8. How to align time+Price this is what providing value to the community looks like :)

HYDRA

32,401 次观看 • 2 年前

What Makes an A+++ Setup (According to a $291K Trader) A setup is the specific market condition where all your criteria align for a high-probability trade entry. Most traders don't understand the criteria that makes a solid entry. They take every setup that looks "good enough" — and end up with 1:2 or 1:3 risk-to-reward ratios, grinding for small wins. Here's the difference: An A+++ setup allowed my student Said to make $86,000 in one day from just 3 trades (with 1:18, 1:7, and 1:10 R/R). And $291k over the last 1.5 years. If there’s a mediocre setup, he won’t take it. Some weeks he only trades twice because he's waiting for perfection. In the 2-minute clip below, my student Said walks through the 5 elements that must align for an A+++ setup: 1) Inverse Fair Value Gap — An imbalance price needs to fill 2) Inducement — Liquidity sweep that triggers early traders 3) Imbalance — Gap in price delivery that draws price back 4) Protected Level — Previous inducement + break of structure creates an internal floor/ceiling 5) Break of Structure — Confirmation that one side is in control Said also looks for setups where you have TWO protected levels, not just one. This gives him the confidence to enter at the end of the fair value gap without waiting for additional confirmation. Stop loss goes just below the protected level — tight risk, massive reward potential. — This is just scratching the surface of what Said shared. In the full 1-hour interview, we also dove into: • The exact 3 trades that made him $86K in a single day (with timestamped chart breakdowns) • How he combined two different strategies into one profitable system • Why he still backtests 1-1.5 hours daily even after making $100K/month Just comment "INTERVIEW" and I'll DM you the full interview in the next few minutes.
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What Makes an A+++ Setup (According to a $291K Trader) A setup is the specific market condition where all your criteria align for a high-probability trade entry. Most traders don't understand the criteria that makes a solid entry. They take every setup that looks "good enough" — and end up with 1:2 or 1:3 risk-to-reward ratios, grinding for small wins. Here's the difference: An A+++ setup allowed my student Said to make $86,000 in one day from just 3 trades (with 1:18, 1:7, and 1:10 R/R). And $291k over the last 1.5 years. If there’s a mediocre setup, he won’t take it. Some weeks he only trades twice because he's waiting for perfection. In the 2-minute clip below, my student Said walks through the 5 elements that must align for an A+++ setup: 1) Inverse Fair Value Gap — An imbalance price needs to fill 2) Inducement — Liquidity sweep that triggers early traders 3) Imbalance — Gap in price delivery that draws price back 4) Protected Level — Previous inducement + break of structure creates an internal floor/ceiling 5) Break of Structure — Confirmation that one side is in control Said also looks for setups where you have TWO protected levels, not just one. This gives him the confidence to enter at the end of the fair value gap without waiting for additional confirmation. Stop loss goes just below the protected level — tight risk, massive reward potential. — This is just scratching the surface of what Said shared. In the full 1-hour interview, we also dove into: • The exact 3 trades that made him $86K in a single day (with timestamped chart breakdowns) • How he combined two different strategies into one profitable system • Why he still backtests 1-1.5 hours daily even after making $100K/month Just comment "INTERVIEW" and I'll DM you the full interview in the next few minutes.

The Trading Geek (Brad Goh)

15,338 次观看 • 6 个月前

#NewPaper The first microscope, invented in the 16th century, was designed to unlock the secrets of the microscopic world. Today, as many fields become increasingly data-driven, there is a pressing need for new types of microscopes---tools that help us zoom in, explore, and understand complex data. We call these tools "algorithmic microscopes." Introducing the Vendiscope: The first algorithmic microscope for data collections. 🔬 The Vendiscope maximizes the probability-weighted Vendi Score of a dataset to assign a weight to each element in the collection. This weight represents a data point's contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We use them to zoom in on datasets across three domains: biology, materials science, & AI. 🧬 Biology: We used the Vendiscope on the protein universe, which contains nearly 250 million proteins. We found that nearly 200 million of the proteins are near-duplicates of each other and that AlphaFold fails on proteins that contribute most to the diversity of the protein universe. (See GIF below). 🪜 Materials Science: We used the Vendiscope on the Materials Project database, which contains 170K materials as of today. We found that 85% of crystals with formation energy data are near-duplicates of each other and that ML models for materials property prediction struggle with materials that contribute most to diversity. 🤖 Artificial Intelligence: We applied the Vendiscope to CIFAR-10, a benchmark dataset containing 50K images. We found duplicates. We applied the Vendiscope to analyze state-of-the-art generative models trained on this dataset. We found the best generative models memorize training data, as is known in the AI literature. However, we can do more with the Vendiscope and characterize the type of samples that get memorized. We found that data points contributing least to diversity are more prone to memorization by these generative models. 🧠 "Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science, providing a systematic and scalable way to identify duplicates and outliers, as well as pinpointing samples prone to memorization and those that models may struggle to predict---even before training." 💫 "The Vendiscope provides a unified framework for analyzing complex data at scale. Researchers, engineers, and data auditors can use the Vendiscope to audit datasets, identify potential biases, and refine data collection practices. For AI ethicists, the Vendiscope offers a critical lens to understand how models interact with data, particularly in the context of bias, memorization, and data fairness, enabling better mitigation strategies to prevent undesirable outcomes in AI deployment. For scientists, the Vendiscope represents a new companion in the discovery process." #VendiScoring #AlgorithmicMicroscopy Link to paper: Authors: Amey Pasarkar (Amey Pasarkar) and Adji Bousso Dieng (@adjiboussodieng)
0:30

#NewPaper The first microscope, invented in the 16th century, was designed to unlock the secrets of the microscopic world. Today, as many fields become increasingly data-driven, there is a pressing need for new types of microscopes---tools that help us zoom in, explore, and understand complex data. We call these tools "algorithmic microscopes." Introducing the Vendiscope: The first algorithmic microscope for data collections. 🔬 The Vendiscope maximizes the probability-weighted Vendi Score of a dataset to assign a weight to each element in the collection. This weight represents a data point's contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We use them to zoom in on datasets across three domains: biology, materials science, & AI. 🧬 Biology: We used the Vendiscope on the protein universe, which contains nearly 250 million proteins. We found that nearly 200 million of the proteins are near-duplicates of each other and that AlphaFold fails on proteins that contribute most to the diversity of the protein universe. (See GIF below). 🪜 Materials Science: We used the Vendiscope on the Materials Project database, which contains 170K materials as of today. We found that 85% of crystals with formation energy data are near-duplicates of each other and that ML models for materials property prediction struggle with materials that contribute most to diversity. 🤖 Artificial Intelligence: We applied the Vendiscope to CIFAR-10, a benchmark dataset containing 50K images. We found duplicates. We applied the Vendiscope to analyze state-of-the-art generative models trained on this dataset. We found the best generative models memorize training data, as is known in the AI literature. However, we can do more with the Vendiscope and characterize the type of samples that get memorized. We found that data points contributing least to diversity are more prone to memorization by these generative models. 🧠 "Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science, providing a systematic and scalable way to identify duplicates and outliers, as well as pinpointing samples prone to memorization and those that models may struggle to predict---even before training." 💫 "The Vendiscope provides a unified framework for analyzing complex data at scale. Researchers, engineers, and data auditors can use the Vendiscope to audit datasets, identify potential biases, and refine data collection practices. For AI ethicists, the Vendiscope offers a critical lens to understand how models interact with data, particularly in the context of bias, memorization, and data fairness, enabling better mitigation strategies to prevent undesirable outcomes in AI deployment. For scientists, the Vendiscope represents a new companion in the discovery process." #VendiScoring #AlgorithmicMicroscopy Link to paper: Authors: Amey Pasarkar (Amey Pasarkar) and Adji Bousso Dieng (@adjiboussodieng)

Vertaix® (AI & Science)

34,762 次观看 • 1 年前

🚩🚩 Watching "The Big Short" in 2025 is dejavu... The 2008 housing bubble feels eerily relevant with today's sky-high stock & housing valuations 🤌 In 08, the crisis stemmed from a massively overvalued housing market fueled by risky subprime loans, lax regulation, and greed Banks bundled junk mortgages into "safe" securities, creating the massive housing bubble... Nothing has really changed. It's just a facade. TODAY: 📈📉 The Fed is warning about overleveraged hedge funds. - Michael Burry is betting on Palantir dropping in price with puts worth nearly $1 billion = 66% of his firms AUM. -Stocks are screaming "overvalued" in every way. - The S&P 500 is 63% above its long-term trend -The Warren Buffett Indicator is at a record 217%+ - Goldman & Morgan Stanley predict 20% corrections soon. 🏠🏠 Housing is bubbly and overvalued from the 2020 free money, low interest rates, and bidding wars. -Sales are crashing while prices stall. - Experts call the market "frozen" with subdued growth -Not as leveraged as '08, but affordability's tanked with higher rates. -Speculative bubbles (AI/tech stocks now vs. housing then) - Many expert warnings of crashes with scary similarities -Fed stopping QT and beginning easing amid softening economy - 1 million+ fewer jobs created in 2024 than stated by the Biden admin per revisions to 2024's fake numbers - falling new-job numbers for 2025 - Growing Repo market borrowing from desperate banks (again) -Volatility rising on potential meltdowns 2025's mess is more driven by toxic derivatives in the stock market, lax regulation, and greed... 🤌 it's just a different flavor of 2008. And still, impunity for banks and big players persists... sound familiar? Nothing changed. It's time for change - 🔥LIT🔥XCHANGE🔥 🚨 Buckle up and repost if you're ready for the housing and stock market corrections 💎 🙌
3:18

🚩🚩 Watching "The Big Short" in 2025 is dejavu... The 2008 housing bubble feels eerily relevant with today's sky-high stock & housing valuations 🤌 In 08, the crisis stemmed from a massively overvalued housing market fueled by risky subprime loans, lax regulation, and greed Banks bundled junk mortgages into "safe" securities, creating the massive housing bubble... Nothing has really changed. It's just a facade. TODAY: 📈📉 The Fed is warning about overleveraged hedge funds. - Michael Burry is betting on Palantir dropping in price with puts worth nearly $1 billion = 66% of his firms AUM. -Stocks are screaming "overvalued" in every way. - The S&P 500 is 63% above its long-term trend -The Warren Buffett Indicator is at a record 217%+ - Goldman & Morgan Stanley predict 20% corrections soon. 🏠🏠 Housing is bubbly and overvalued from the 2020 free money, low interest rates, and bidding wars. -Sales are crashing while prices stall. - Experts call the market "frozen" with subdued growth -Not as leveraged as '08, but affordability's tanked with higher rates. -Speculative bubbles (AI/tech stocks now vs. housing then) - Many expert warnings of crashes with scary similarities -Fed stopping QT and beginning easing amid softening economy - 1 million+ fewer jobs created in 2024 than stated by the Biden admin per revisions to 2024's fake numbers - falling new-job numbers for 2025 - Growing Repo market borrowing from desperate banks (again) -Volatility rising on potential meltdowns 2025's mess is more driven by toxic derivatives in the stock market, lax regulation, and greed... 🤌 it's just a different flavor of 2008. And still, impunity for banks and big players persists... sound familiar? Nothing changed. It's time for change - 🔥LIT🔥XCHANGE🔥 🚨 Buckle up and repost if you're ready for the housing and stock market corrections 💎 🙌

The Butcher of Wall Street | Marcel Kalinovic

505,935 次观看 • 8 个月前

Something interesting is going to be released soon... ✨ Kitt The Inner Circle Trader "If I had to trade only one model for the rest of my life, considering everything I've publicly disclosed, my choice would be either the second stage of re-distribution in an MMSM or the second stage of re-accumulation in an MMBM. With either of these, I believe I could consistently generate substantial profits without the need to explore alternative strategies. These models rely on specific components of both the buy and sell sides of the market curve, which are directly interconnected. This isn't a matter of identifying support and resistance levels; it's about understanding the logic of order flow. In the case of the market maker sell model, I focus on identifying a pool of liquidity beneath the initial consolidation. When I spot this sellside opportunity, I patiently await a reversal. This reversal should lead to a drop of at least 50% from the smart money's reversal point down to the sellside liquidity. If it achieves this, and then begins to rally once more, I'll look to correlate it with the other side of the curve, where the market previously rallied before reversing. This will provide me with an array that initially signaled a bullish trend but now acts as a reversal indicator. This marks the second stage of distribution or redistribution, and it usually happens swiftly, pushing prices towards the sellside. In essence, I'm waiting for a unicorn setup, where all the pieces align perfectly, and I have everything in my favor. I'll risk 5% on such a trade. This approach involves re-accumulation, where the sellside drops down to 50%, and then I match it with another array to capture the reversal. Now, picture a market maker model involving a consolidation phase where relative equal lows are formed, followed by an upward rally, possibly forming a consolidation that resembles a bull flag pattern. Subsequently, it rallies out of that consolidation. Sometimes, it may create a second stage of re-accumulation as it trades towards a premium array level—a level I consider a liquidity draw. If I'm feeling bullish, I'd aim for that level. I don't necessarily need to be there at the exact moment; I might spot the opportunity later and act accordingly. If it's reacting off of a level, that should offer sellside. So, you know where sellside delivery. The market should drop down. So, I'm anticipating price reacting and reversing at the smart money reversal once it starts to break down. If it goes back up a little bit, that's the smart money reversal. Low risk sell is the next stage and then they'll drop. When we reach the low-risk sell, it's important that the drop reaches at least 50% of the total range from the smart money reversal to the sellside I'm targeting. As long as it accomplishes this, I have confidence that the subsequent rally will reach a premium array on the left side of the curve before the market makes its high and reverses. Why would it do that? Because it's part of a larger continuation. So when and how would I determine when it's going to fail ,that first leg of re-distribution on the sell side, if it doesn't pierce 50% of that range from the smart money reversal down to the sell side liquidity. If it doesn't do that, then it's not going to go down there. It's going to be a continuation of reverse and go the other way." #ict #ICT
6:37

Something interesting is going to be released soon... ✨ Kitt The Inner Circle Trader "If I had to trade only one model for the rest of my life, considering everything I've publicly disclosed, my choice would be either the second stage of re-distribution in an MMSM or the second stage of re-accumulation in an MMBM. With either of these, I believe I could consistently generate substantial profits without the need to explore alternative strategies. These models rely on specific components of both the buy and sell sides of the market curve, which are directly interconnected. This isn't a matter of identifying support and resistance levels; it's about understanding the logic of order flow. In the case of the market maker sell model, I focus on identifying a pool of liquidity beneath the initial consolidation. When I spot this sellside opportunity, I patiently await a reversal. This reversal should lead to a drop of at least 50% from the smart money's reversal point down to the sellside liquidity. If it achieves this, and then begins to rally once more, I'll look to correlate it with the other side of the curve, where the market previously rallied before reversing. This will provide me with an array that initially signaled a bullish trend but now acts as a reversal indicator. This marks the second stage of distribution or redistribution, and it usually happens swiftly, pushing prices towards the sellside. In essence, I'm waiting for a unicorn setup, where all the pieces align perfectly, and I have everything in my favor. I'll risk 5% on such a trade. This approach involves re-accumulation, where the sellside drops down to 50%, and then I match it with another array to capture the reversal. Now, picture a market maker model involving a consolidation phase where relative equal lows are formed, followed by an upward rally, possibly forming a consolidation that resembles a bull flag pattern. Subsequently, it rallies out of that consolidation. Sometimes, it may create a second stage of re-accumulation as it trades towards a premium array level—a level I consider a liquidity draw. If I'm feeling bullish, I'd aim for that level. I don't necessarily need to be there at the exact moment; I might spot the opportunity later and act accordingly. If it's reacting off of a level, that should offer sellside. So, you know where sellside delivery. The market should drop down. So, I'm anticipating price reacting and reversing at the smart money reversal once it starts to break down. If it goes back up a little bit, that's the smart money reversal. Low risk sell is the next stage and then they'll drop. When we reach the low-risk sell, it's important that the drop reaches at least 50% of the total range from the smart money reversal to the sellside I'm targeting. As long as it accomplishes this, I have confidence that the subsequent rally will reach a premium array on the left side of the curve before the market makes its high and reverses. Why would it do that? Because it's part of a larger continuation. So when and how would I determine when it's going to fail ,that first leg of re-distribution on the sell side, if it doesn't pierce 50% of that range from the smart money reversal down to the sell side liquidity. If it doesn't do that, then it's not going to go down there. It's going to be a continuation of reverse and go the other way." #ict #ICT

LumiTraders

387,225 次观看 • 2 年前

Thank you, Cde Vera, for exposing the betrayal. Every Zimbabwean must listen to this truth—Mnangagwa and ZANU-PF have abandoned the liberation cause and turned it into a personal business empire. As Cde Faith Chananda (Comrade Vera) bravely narrates, the heroes who shed blood for this country are now reduced to beggars—handed bicycles, rotten food hampers, and empty slogans in the name of “empowerment.” Meanwhile, Mnangagwa and his Zvigananda cartel loot billions, buy loyalty with cars and cash, and sow division to keep their corrupt grip on power. Let us be clear: Mnangagwa is not a liberator. He is a corrupt rogue, a national disgrace who spits on the sacrifices of the fallen. He has betrayed war veterans, betrayed the people, and betrayed the nation. While millions live in poverty, without jobs, without proper healthcare, without roads or clean water, he plots to steal more time in power through his criminal 2030 agenda. To the war veterans: you carried this nation on your backs. Do not let bicycles and food hampers be the price of your blood and sacrifice. You fought for freedom and dignity—do not allow Mnangagwa to mock you with crumbs while he builds his empire. To those still in ZANU-PF with a conscience: the party you joined to liberate Zimbabwe has been hijacked by Mnangagwa’s greed. If you remain silent while he tampers with the Constitution to extend his term beyond 2028, then you are complicit in destroying the very ideals of the struggle. The 2030 agenda is not development—it is dictatorship. It is the death of the Constitution, the betrayal of the liberation, and the enslavement of future generations. Mnangagwa must be stopped now. War veterans, ZANU-PF insiders, and all patriotic Zimbabweans must rise and declare openly: this rogue has failed, this rogue is corrupt, this rogue will not rewrite our Constitution to cling to power. Zimbabwe’s liberation cannot end in bicycles and rotten food. It must end in justice, dignity, and true freedom for the people. ZANU PF Nick Mangwana President of Zimbabwe Presidential Communications Zimbabwe 🇿🇼 dj steve easy tiger CCC Southend 🇿🇼🟨🇬🇧 LynneM 💕💝💎 Change Radio Amnesty International Zimbabwe 𝐂𝐫𝐢𝐦𝐞 𝐖𝐚𝐭𝐜𝐡 𝐙𝐖 The Mirror Masvingo TheNewsHawks Zimbabwe Third Eye News🇿🇼 👁 The Independent Parliament of Zimbabwe Varakashi4ED Mash West Updates Varakashi4ED Zimbabwe Freedom of Expression Henry Itayi Makambe Pauline Kaseke Guzha Spencer Shorayi Padare-Enkundleni 🇿🇼 ZANU PF PATRIOTS 🇿🇼 @ZanuPFYouthLeag Ali Naka Commentary African Fadzayi Mahere🇿🇼 HON Job Wiwa Sikhala Sabhuku Temba P. Mliswa Hon Lt Gen (Rtd) Amb. AN Sanyatwe Oppah Muchinguri-Kashiri (ZANUPF Nat. Chairman) Dr A. Mutambudzi ZanuPF Treasurer General - Patrick Chinamasa TENDAI BITI Thabani Mpofu TEDIOUS MUSINACHIREVO Promise Mkwananzi Cde Chibage TINO Citizens Change Champion Chibaya
21:00

Thank you, Cde Vera, for exposing the betrayal. Every Zimbabwean must listen to this truth—Mnangagwa and ZANU-PF have abandoned the liberation cause and turned it into a personal business empire. As Cde Faith Chananda (Comrade Vera) bravely narrates, the heroes who shed blood for this country are now reduced to beggars—handed bicycles, rotten food hampers, and empty slogans in the name of “empowerment.” Meanwhile, Mnangagwa and his Zvigananda cartel loot billions, buy loyalty with cars and cash, and sow division to keep their corrupt grip on power. Let us be clear: Mnangagwa is not a liberator. He is a corrupt rogue, a national disgrace who spits on the sacrifices of the fallen. He has betrayed war veterans, betrayed the people, and betrayed the nation. While millions live in poverty, without jobs, without proper healthcare, without roads or clean water, he plots to steal more time in power through his criminal 2030 agenda. To the war veterans: you carried this nation on your backs. Do not let bicycles and food hampers be the price of your blood and sacrifice. You fought for freedom and dignity—do not allow Mnangagwa to mock you with crumbs while he builds his empire. To those still in ZANU-PF with a conscience: the party you joined to liberate Zimbabwe has been hijacked by Mnangagwa’s greed. If you remain silent while he tampers with the Constitution to extend his term beyond 2028, then you are complicit in destroying the very ideals of the struggle. The 2030 agenda is not development—it is dictatorship. It is the death of the Constitution, the betrayal of the liberation, and the enslavement of future generations. Mnangagwa must be stopped now. War veterans, ZANU-PF insiders, and all patriotic Zimbabweans must rise and declare openly: this rogue has failed, this rogue is corrupt, this rogue will not rewrite our Constitution to cling to power. Zimbabwe’s liberation cannot end in bicycles and rotten food. It must end in justice, dignity, and true freedom for the people. ZANU PF Nick Mangwana President of Zimbabwe Presidential Communications Zimbabwe 🇿🇼 dj steve easy tiger CCC Southend 🇿🇼🟨🇬🇧 LynneM 💕💝💎 Change Radio Amnesty International Zimbabwe 𝐂𝐫𝐢𝐦𝐞 𝐖𝐚𝐭𝐜𝐡 𝐙𝐖 The Mirror Masvingo TheNewsHawks Zimbabwe Third Eye News🇿🇼 👁 The Independent Parliament of Zimbabwe Varakashi4ED Mash West Updates Varakashi4ED Zimbabwe Freedom of Expression Henry Itayi Makambe Pauline Kaseke Guzha Spencer Shorayi Padare-Enkundleni 🇿🇼 ZANU PF PATRIOTS 🇿🇼 @ZanuPFYouthLeag Ali Naka Commentary African Fadzayi Mahere🇿🇼 HON Job Wiwa Sikhala Sabhuku Temba P. Mliswa Hon Lt Gen (Rtd) Amb. AN Sanyatwe Oppah Muchinguri-Kashiri (ZANUPF Nat. Chairman) Dr A. Mutambudzi ZanuPF Treasurer General - Patrick Chinamasa TENDAI BITI Thabani Mpofu TEDIOUS MUSINACHIREVO Promise Mkwananzi Cde Chibage TINO Citizens Change Champion Chibaya

Tatenda C.K, Hungwe

36,029 次观看 • 9 个月前

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).
9:00

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).

TheValueist

76,046 次观看 • 6 个月前

FULL TRANSCRIPT OF ELON'S CYBERCAB AND ROBOVAN PRESENTATION 00:00 Welcome 01:16 Cybercab & Future of transportation 04:33 Cost 05:53 Timeline 07:13 Self-driving technology 10:05 Inductive charging 10:24 The cities of the future 11:04 Robovan 12:13 Optimus Welcome Welcome to the We, Robot party. We have quite a show for you tonight. I think you're going to like it. As you can see, I just arrived in the Robotaxi, the Cybercab. And there's 20 more where that came from. So they've been traveling, there's no people in them. As you can see, the car is just going by with no people. We have 50 fully autonomous cars here tonight. So you'll see model Y's and the Cybercabs, all driverless. You'll be able to take a ride in the Cybercab. There's no steering wheel or pedals. So I hope this goes well, we'll find out. You see a lot of sci-fi movies where the future is dark and dismal, where it's not a future you want to be in. So, you know, I love Blade Runner, but I don't know if we want that future. We want that duster he's wearing, but not the bleak apocalypse. We want to have a fun, exciting future that, if you could look in a crystal ball and see the future, you'd be like, yes, I wish I could be there now. That's what we want. Cybercab & Future of transportation So, when we think about transport today, there's a lot of pain that we take for granted, that we think is normal. Like having to drive around LA in 3 hours of traffic. Yeah, people that live in LA, I mean, you know, try to get from Pasadena to El Segundo during rush hour. You can fly to another city faster than you can get to LA. And you have to drive the whole way, unless you're in a Tesla. Of course, our Tesla already does quite well at this supervised self-driving. So, supervised full self-driving is actually working quite well. I'm sure there's people in the crowd who are using that. So, we'll move from supervised full self-driving to unsupervised full self-driving where the car, you could fall asleep and wake up at your destination. But there's also a challenge for a lot of people that cars cost too much. I mean, when you factor in everything that goes into a car and the car insurance and the car payments, storage of the car, it's very expensive. You say, like, how many hours a week are cars used? Your average passenger car is only used about 10 hours a week out of 168 hours. So, the vast majority of the time cars are just doing nothing. But if they're autonomous, they could be used, I don't know, five times more, maybe ten times more. So you could actually, for the same car, would have five times as much value, maybe ten times as much value. There's 168 hours in the week, and like I said, only ten of them are used for driving. And then, a bunch of those hours are looking for a parking spot, which can be pretty annoying at times. So, with autonomy, you get your time back. This is a very big deal. So it's not just, it'll save lives, like a lot of lives and prevent injuries. I think we'll see autonomous cars become ten times safer than a human. I mean, if you think of times past where there used to be an elevator operator in every elevator but once in a while, they get tired and accidentally shear somebody in half. Now, we have automated elevators. You just get an elevator and you press a button and you don't even think about it and it just takes you to the floor. And if you did see an elevator operator with a big relay switch, you'd be like, that's weird. That's how cars will be. And it's not just the lives saved in injuries, but if you think about the cumulative time that people spend in a car and the time that they will get back that they can now spend, well, I guess, on their phones or watching a movie or doing work or whatever you want to do you can think of the car in autonomous world as being like just little lounge. You're just sitting in a comfortable little lounge and you can do whatever you want while you're in this comfortable little lounge. And when you get out, you will be at your destination. So, yeah, it's gonna be awesome. Cost So, in fact, I think the cost of autonomous transport will be so low that you can think of it like individualized mass transit. The average cost of a bus per mile for a city, not the ticket price, because that is subsidized, but the average price is about a dollar a mile, whereas the cost of Cybercab we think probably over time, the operating cost is probably going to be around twenty cents a mile. Including taxes and everything else, it probably ends up being 30 or 40 cents a mile. And you will be able to buy one. And we expect the cost to be below $30,000. And I think there'll be an interesting business model where, let's say somebody is an Uber or Lyft driver today where they can actually sort of manage a fleet of cars and like, sort of manage, I don't know, 10, 20 cars and just take care of them. Like a shepherd tends their flock. You have a little flock of cars and you're the shepherd and you take care of your flock of cars. I think that would be pretty cool. I think it's going to be a glorious future. It's going to be really something special. Timeline We do expect actually to start fully autonomous unsupervised FSD in Texas and California next year. And that's obviously, that's with the Model 3 and Model Y. And then we expect to be in production with the Cybercab, which is really highly optimized for autonomous transport in probably, I tend to be a little optimistic with time frames, but in 2026. So, yeah, before 2027, let me put it that way. And we'll make this vehicle in very high volume. But well, before that, you will experience a robotic taxi via the Model 3 and Model Y program and model S and X, too. But the Model 3 and Y will achieve unsupervised full self-driving with permission, in wherever regulators essentially approve it. In the US, and then to follow outside the US. And Cybertruck, too. All our cars are basically, all cars that we make. Let's not get nuanced here. Self-driving technology One of the reasons why the computer can be so much better than a person is that we have millions of cars that are training on driving. It's like living millions of lives simultaneously and seeing very unusual situations that a person in their entire lifetime would not see. With that amount of training data, it's obviously going to be much better than what a human could be because you can't live a million lives. And it's also, it can see in all directions simultaneously and it doesn't get tired or text or any of those things. So, it will naturally be, like I said 10, 20, 30 times safer than a human, just for all those reasons. And I want to emphasize that the solution that we have is, AI and vision. So, there's no expensive equipment needed. The Model 3 and Model Y and S and X that we make today will be capable of full autonomy, unsupervised. And that means that our cost of producing the vehicle is low. Now, we are going to actually over-spec the computer for the Cybercab. So, our AI 5 computer will be somewhat over-spec'd because I think there's actually also an opportunity, sort of like an Amazon Web Services, where if the car is driving for 50 hours a week, there's still over 100 hours left and there's a potential there to have a massive amount of distributed inference compute, where if you've got like a fleet of 100 million vehicles and a kilowatt of efficient inference compute, you have 100 gigawatts of compute, which is really quite substantial. And if it's there, you might as well use it so that I think will make sense. So, our autonomous future is here. As I said, we've got 50 Teslas driving autonomously. We're trying to give you a sense of what cities will be like in the future. And when you get in, you'll see like, it's really quite a wild experience to just be in a car with no steering wheel, no pedals, no controls, and it feels great. So we have enough vehicles here, so everyone should be able to try it out and experience the set that we've built here. It's a very big set. So it's like really we've used I don't know, 20, 30 acres or something like that. It's really big. So, it goes on, the ride's long. And we set it up to feel like a ride, like a park ride. So, it'll be cool and you'll get to experience it tonight. Inductive charging Something we're also doing is and it's really high time we did this is inductive charging. So, the robotaxi has no plug. It just goes over the inductive charger and charges. So, yeah, it's kind of how it should be. The cities of the future One of the things that is really interesting is how will this affect the cities that we live in. And when you drive around a city, or when the car drives you around the city, you'll see there's a lot of parking lots. There's parking lots everywhere, parking garages. What would happen if you have an autonomous world is that you can now turn parking lots into parks. And so, from we're taking the inglot out of parking lot. You're welcome. So, there's a lot of opportunity to create green space in the cities that we live in. So, like, that would be quite fantastic. Robovan Oh, and also, what happens if you need a vehicle that is bigger than a Model Y? The Robovan. We're going to make this and it's going to look like that. Now, can you imagine going down the streets and you see this coming towards you? That'd be sick. So this can carry up to 20 people, and it can also transport goods. You can configure it for goods transport within a city. Or transport of up to 20 people at a time. The Robovan is what's gonna solve for high density. If you want to take a sports team somewhere or you're looking to really get the cost of travel down to, I don't know, 5, 10 cents a mile, then you can use the Robovan. One of the things we want to do, and we've seen this with the Cybertruck, is we want to change the look of the roads. The future should look like the future. Optimus Speaking of robots. Everything we've developed for our cars, the batteries, power electronics, the advanced motors, gearboxes, the software, the AI inference computer, it all actually applies to a humanoid robot. The same techniques. It's just a robot with arms and legs instead of a robot with wheels. We've made a lot of progress with Optimus. And as you can see, we started up with someone in a robot suit. And then, we've progressed dramatically, year after year. So, if you extrapolate this, you're really going to have something spectacular, something that anyone could own. So, you can have your own personal R2-D2-C3PO. And I think at scale, this would cost something like, I don't know, $20,000, $30,000, probably less than a car is my prediction, long-term. It'll take us a minute to get to the long term. But fundamentally, at scale, the Optimus robot, you should be able to buy an Optimus robot for, I think, probably $20,000 to $30,000, long-term. And what can it do? It'll basically do anything you want. It can be a teacher or babysit your kids, it can walk your dog, mow your lawn, get the groceries, just be your friend, serve drinks whatever you can think of, it will do. And, yeah, it's going to be awesome. I think this will be the biggest product ever of any kind, because I think everyone of the 8 billion people of Earth, I think everyone's going to want their Optimus buddy. And there's going to be maybe two. And then, they'll be producing products and services. I predict, actually, provided we address risks of digital superintelligence, 80% probability of good outcome, look on the bright side, the cup is 80% full, the cost of products and services will decline dramatically. And basically, anyone will be able to have any products and services they want. It will be an age of abundance the likes of which people have not, almost no one has envisioned. It will be something special. So now, one of the things we wanted to show tonight was that Optimus is not a canned video. It's not walled off. The Optimus robots will walk among you. Please, please be nice to the Optimus robots. You'll be able to walk right up to them and they'll serve drinks at the bar. I mean, it's a wild experience just to have humanoid robots and they're there, you're just in front of you. So yeah, with that, let's party!
15:37

FULL TRANSCRIPT OF ELON'S CYBERCAB AND ROBOVAN PRESENTATION 00:00 Welcome 01:16 Cybercab & Future of transportation 04:33 Cost 05:53 Timeline 07:13 Self-driving technology 10:05 Inductive charging 10:24 The cities of the future 11:04 Robovan 12:13 Optimus Welcome Welcome to the We, Robot party. We have quite a show for you tonight. I think you're going to like it. As you can see, I just arrived in the Robotaxi, the Cybercab. And there's 20 more where that came from. So they've been traveling, there's no people in them. As you can see, the car is just going by with no people. We have 50 fully autonomous cars here tonight. So you'll see model Y's and the Cybercabs, all driverless. You'll be able to take a ride in the Cybercab. There's no steering wheel or pedals. So I hope this goes well, we'll find out. You see a lot of sci-fi movies where the future is dark and dismal, where it's not a future you want to be in. So, you know, I love Blade Runner, but I don't know if we want that future. We want that duster he's wearing, but not the bleak apocalypse. We want to have a fun, exciting future that, if you could look in a crystal ball and see the future, you'd be like, yes, I wish I could be there now. That's what we want. Cybercab & Future of transportation So, when we think about transport today, there's a lot of pain that we take for granted, that we think is normal. Like having to drive around LA in 3 hours of traffic. Yeah, people that live in LA, I mean, you know, try to get from Pasadena to El Segundo during rush hour. You can fly to another city faster than you can get to LA. And you have to drive the whole way, unless you're in a Tesla. Of course, our Tesla already does quite well at this supervised self-driving. So, supervised full self-driving is actually working quite well. I'm sure there's people in the crowd who are using that. So, we'll move from supervised full self-driving to unsupervised full self-driving where the car, you could fall asleep and wake up at your destination. But there's also a challenge for a lot of people that cars cost too much. I mean, when you factor in everything that goes into a car and the car insurance and the car payments, storage of the car, it's very expensive. You say, like, how many hours a week are cars used? Your average passenger car is only used about 10 hours a week out of 168 hours. So, the vast majority of the time cars are just doing nothing. But if they're autonomous, they could be used, I don't know, five times more, maybe ten times more. So you could actually, for the same car, would have five times as much value, maybe ten times as much value. There's 168 hours in the week, and like I said, only ten of them are used for driving. And then, a bunch of those hours are looking for a parking spot, which can be pretty annoying at times. So, with autonomy, you get your time back. This is a very big deal. So it's not just, it'll save lives, like a lot of lives and prevent injuries. I think we'll see autonomous cars become ten times safer than a human. I mean, if you think of times past where there used to be an elevator operator in every elevator but once in a while, they get tired and accidentally shear somebody in half. Now, we have automated elevators. You just get an elevator and you press a button and you don't even think about it and it just takes you to the floor. And if you did see an elevator operator with a big relay switch, you'd be like, that's weird. That's how cars will be. And it's not just the lives saved in injuries, but if you think about the cumulative time that people spend in a car and the time that they will get back that they can now spend, well, I guess, on their phones or watching a movie or doing work or whatever you want to do you can think of the car in autonomous world as being like just little lounge. You're just sitting in a comfortable little lounge and you can do whatever you want while you're in this comfortable little lounge. And when you get out, you will be at your destination. So, yeah, it's gonna be awesome. Cost So, in fact, I think the cost of autonomous transport will be so low that you can think of it like individualized mass transit. The average cost of a bus per mile for a city, not the ticket price, because that is subsidized, but the average price is about a dollar a mile, whereas the cost of Cybercab we think probably over time, the operating cost is probably going to be around twenty cents a mile. Including taxes and everything else, it probably ends up being 30 or 40 cents a mile. And you will be able to buy one. And we expect the cost to be below $30,000. And I think there'll be an interesting business model where, let's say somebody is an Uber or Lyft driver today where they can actually sort of manage a fleet of cars and like, sort of manage, I don't know, 10, 20 cars and just take care of them. Like a shepherd tends their flock. You have a little flock of cars and you're the shepherd and you take care of your flock of cars. I think that would be pretty cool. I think it's going to be a glorious future. It's going to be really something special. Timeline We do expect actually to start fully autonomous unsupervised FSD in Texas and California next year. And that's obviously, that's with the Model 3 and Model Y. And then we expect to be in production with the Cybercab, which is really highly optimized for autonomous transport in probably, I tend to be a little optimistic with time frames, but in 2026. So, yeah, before 2027, let me put it that way. And we'll make this vehicle in very high volume. But well, before that, you will experience a robotic taxi via the Model 3 and Model Y program and model S and X, too. But the Model 3 and Y will achieve unsupervised full self-driving with permission, in wherever regulators essentially approve it. In the US, and then to follow outside the US. And Cybertruck, too. All our cars are basically, all cars that we make. Let's not get nuanced here. Self-driving technology One of the reasons why the computer can be so much better than a person is that we have millions of cars that are training on driving. It's like living millions of lives simultaneously and seeing very unusual situations that a person in their entire lifetime would not see. With that amount of training data, it's obviously going to be much better than what a human could be because you can't live a million lives. And it's also, it can see in all directions simultaneously and it doesn't get tired or text or any of those things. So, it will naturally be, like I said 10, 20, 30 times safer than a human, just for all those reasons. And I want to emphasize that the solution that we have is, AI and vision. So, there's no expensive equipment needed. The Model 3 and Model Y and S and X that we make today will be capable of full autonomy, unsupervised. And that means that our cost of producing the vehicle is low. Now, we are going to actually over-spec the computer for the Cybercab. So, our AI 5 computer will be somewhat over-spec'd because I think there's actually also an opportunity, sort of like an Amazon Web Services, where if the car is driving for 50 hours a week, there's still over 100 hours left and there's a potential there to have a massive amount of distributed inference compute, where if you've got like a fleet of 100 million vehicles and a kilowatt of efficient inference compute, you have 100 gigawatts of compute, which is really quite substantial. And if it's there, you might as well use it so that I think will make sense. So, our autonomous future is here. As I said, we've got 50 Teslas driving autonomously. We're trying to give you a sense of what cities will be like in the future. And when you get in, you'll see like, it's really quite a wild experience to just be in a car with no steering wheel, no pedals, no controls, and it feels great. So we have enough vehicles here, so everyone should be able to try it out and experience the set that we've built here. It's a very big set. So it's like really we've used I don't know, 20, 30 acres or something like that. It's really big. So, it goes on, the ride's long. And we set it up to feel like a ride, like a park ride. So, it'll be cool and you'll get to experience it tonight. Inductive charging Something we're also doing is and it's really high time we did this is inductive charging. So, the robotaxi has no plug. It just goes over the inductive charger and charges. So, yeah, it's kind of how it should be. The cities of the future One of the things that is really interesting is how will this affect the cities that we live in. And when you drive around a city, or when the car drives you around the city, you'll see there's a lot of parking lots. There's parking lots everywhere, parking garages. What would happen if you have an autonomous world is that you can now turn parking lots into parks. And so, from we're taking the inglot out of parking lot. You're welcome. So, there's a lot of opportunity to create green space in the cities that we live in. So, like, that would be quite fantastic. Robovan Oh, and also, what happens if you need a vehicle that is bigger than a Model Y? The Robovan. We're going to make this and it's going to look like that. Now, can you imagine going down the streets and you see this coming towards you? That'd be sick. So this can carry up to 20 people, and it can also transport goods. You can configure it for goods transport within a city. Or transport of up to 20 people at a time. The Robovan is what's gonna solve for high density. If you want to take a sports team somewhere or you're looking to really get the cost of travel down to, I don't know, 5, 10 cents a mile, then you can use the Robovan. One of the things we want to do, and we've seen this with the Cybertruck, is we want to change the look of the roads. The future should look like the future. Optimus Speaking of robots. Everything we've developed for our cars, the batteries, power electronics, the advanced motors, gearboxes, the software, the AI inference computer, it all actually applies to a humanoid robot. The same techniques. It's just a robot with arms and legs instead of a robot with wheels. We've made a lot of progress with Optimus. And as you can see, we started up with someone in a robot suit. And then, we've progressed dramatically, year after year. So, if you extrapolate this, you're really going to have something spectacular, something that anyone could own. So, you can have your own personal R2-D2-C3PO. And I think at scale, this would cost something like, I don't know, $20,000, $30,000, probably less than a car is my prediction, long-term. It'll take us a minute to get to the long term. But fundamentally, at scale, the Optimus robot, you should be able to buy an Optimus robot for, I think, probably $20,000 to $30,000, long-term. And what can it do? It'll basically do anything you want. It can be a teacher or babysit your kids, it can walk your dog, mow your lawn, get the groceries, just be your friend, serve drinks whatever you can think of, it will do. And, yeah, it's going to be awesome. I think this will be the biggest product ever of any kind, because I think everyone of the 8 billion people of Earth, I think everyone's going to want their Optimus buddy. And there's going to be maybe two. And then, they'll be producing products and services. I predict, actually, provided we address risks of digital superintelligence, 80% probability of good outcome, look on the bright side, the cup is 80% full, the cost of products and services will decline dramatically. And basically, anyone will be able to have any products and services they want. It will be an age of abundance the likes of which people have not, almost no one has envisioned. It will be something special. So now, one of the things we wanted to show tonight was that Optimus is not a canned video. It's not walled off. The Optimus robots will walk among you. Please, please be nice to the Optimus robots. You'll be able to walk right up to them and they'll serve drinks at the bar. I mean, it's a wild experience just to have humanoid robots and they're there, you're just in front of you. So yeah, with that, let's party!

Mario Nawfal

241,051 次观看 • 1 年前