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Microsoft’s MacBook Pro competitor is here: Surface Laptop Ultra - 15" Mini-LED touchscreen display - Up to 2000nits of peak HDR brightness - Large haptic touchpad - Nvidia RTX Spark (20-core ARM CPU) - Blackwell GPU (6144 CUDA Cores) - Up to 128GB unified memory - 1 petaflop of... show more

Shishir

27,835 subscribers

783,464 次观看 • 3 个月前 •via X (Twitter)

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35 条评论

James O'Reilly 的头像

James O'Reilly3 个月前

- up to 15 minutes of battery life

Jj 的头像

Jj3 个月前

will it try to run unscheduled update mid agent tasks?

Karsten Samaschke 的头像

Karsten Samaschke3 个月前

It is a f*cking Windows machine. Unless it runs Linux, I am genuinely bored.

Baud 的头像

Baud3 个月前

Awesome release. Now make everyone immediately remove Microsoft Windows and install Linux.

Sebastian Aaltonen 的头像

Sebastian Aaltonen3 个月前

Half the memory bandwidth of M5 Max. Will not compete against the top end 40-core GPU model, but will be interesting against the mid tier MacBook Pros.

Ivan ➜ dooing.ai 的头像

Ivan ➜ dooing.ai3 个月前

The problem is that it comes with Windows installed.

Dejan Velimirovic 的头像

Dejan Velimirovic3 个月前

All of that, and then cripple it with Windows.

TheVallance 的头像

TheVallance3 个月前

Bought a surface book 2 win 10. Can't upgrade to win11. €2000 garbaged by 1 os update. Never again. Runs faster than out the box on linux now.

muku 的头像

muku3 个月前

At this point apple should introduce a touch screen laptop

rafalee 的头像

rafalee3 个月前

2 main things: - why the hell they are so bad with names, there is 0 consistency with names, surface laptop ultra WTF, how you add “laptop” to the name of a laptop? what is surface? why so fucking complicated name, why not MicroBook A1 idk 🤷🏻‍♂️ something smaller and easy to say. - why so shady on the price, show the fucking price

Jordan Huang 的头像

Jordan Huang3 个月前

If it runs Windows it doesn’t matter how good the hardware is. Windows is unusable.

Wissam Benhaddad 的头像

Wissam Benhaddad3 个月前

All of that to run on a shitty OS. Such a waste

Ⓒⓡⓨⓟⓣⓞⓝⓘⓐⓝ 的头像

Ⓒⓡⓨⓟⓣⓞⓝⓘⓐⓝ3 个月前

It will overheat and battery won't last more than 6 hours.

RKal 的头像

RKal3 个月前

Even if at the end the specs sound better, I would stick with macs, software is so much better. Their efficiency is underrated! I hate windows in any form for some reason!

Riccardo Tagliavia 的头像

Riccardo Tagliavia3 个月前

People ar Microsoft must be excited! They will be able to use 50% of the cpu now to keep capturing your data, screenshots and use your computer to process them instead of their datacenters

Martín López Arango 的头像

Martín López Arango3 个月前

So let me get this straight! Windows users hates apple computers because they all got ARM based processors that can’t be modified. But now they love it because Nvidia manage to create one processor than can compete with Apple sillicon. 🤡🤡

A² 的头像

A²3 个月前

With Microsoft Windows 11 ;) It will be funny to see the hardware bricked by a faulty update.

coconutTrader 的头像

coconutTrader3 个月前

The problem with windows is not hardware. It is software and hardware/software deep integration.

Domi 的头像

Domi3 个月前

Calling a Windows laptop a MacBook competitor is wild. Maybe if it ran Linux. Not Windows.

Jason Kneen 的头像

Jason Kneen3 个月前

WHAT it runs macOS? Oh wait...

King Troll 的头像

King Troll3 个月前

LOL its still windows! Itll take 30 seconds for the task manager to load, and 10 seconds to sort the list, search is useless and itll randomly reboot for updates closing all your work. Not to mention constant changes and spyware telemetry.

Basil Rari 的头像

Basil Rari3 个月前

I am a user of Windows on Arm Snapdragon X elite with the Surface Laptop 7. I'm fucking stuck here. They dont even allow me to Charge my Laptop past 80 now. So yeah expectations very low for this one. Unless it runs Linux Day 0

DS 的头像

DS3 个月前

Will now successfully enter hibernate mode after closing the lid 40% of the time (up from 20%!), and sometimes even wake up when you open it!

Juice Lab UI/UX & Development Agency 的头像

Juice Lab UI/UX & Development Agency3 个月前

MacBook Pro finally getting some real competition. 🔥

Kosta Milovanovic 的头像

Kosta Milovanovic3 个月前

Looks like a cool MacBook clone. After owning 5 surface laptops and then switching to an M1 Max, I’m never going back to Windows. Idk how I got deluded into thinking Windows was ever better. And for AI stuff, it’s just such a bad OS.

OOM 的头像

OOM3 个月前

If they get the pricing right windows might be the only turd in the room

Till Felippi 的头像

Till Felippi3 个月前

Unfortunately it still runs on Windows, so...

Kommatose 的头像

Kommatose3 个月前

No pricing yet. But likely this is the $3k and up price range. There’s already high windows laptops, but much larger form factors than this with lower battery life. The biggest limitation here is just simply the windows os. It’s not nearly as polished as macOS. It will be a good machine but it won’t be game breaking really. They’re just playing catchup currently

JC 的头像

JC3 个月前

It doesn't matter what hardware they have if the operating system is Windows...

Het Mehta 的头像

Het Mehta3 个月前

What will we do about Windows OS?

Singularity City 的头像

Singularity City3 个月前

It will probably have to sell one of your organs to buy it! Also anyone need a spleen? 😂

Matt Wolfgang 的头像

Matt Wolfgang3 个月前

I don't trust them. We had a mixed fleet of Surfacebooks and Lenovo X1 Carbons before our last hardware refresh, both with i7s and 8GB RAM. The Surfacebooks were in a constant state of thermal throttling. It took less than 10 minutes to image an X1, meanwhile it took half an hour to image a Surfacebook.

Reformed Sinner 的头像

Reformed Sinner3 个月前

Windows 11? No thanks

Patrick Kelly-Weller 的头像

Patrick Kelly-Weller3 个月前

Still running Windows though...

Wilson Samiano 的头像

Wilson Samiano3 个月前

And just like the Macbook, it's bootloader is locked down and force you to use CoPilot. No thanks! You pay $2000 but Microsoft still owns it. LOL

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NEW: Microsoft just unveiled its most powerful Surface ever. Surface Laptop Ultra is a 15-inch NVIDIA-powered laptop built to run local AI models, creative work, and gaming. • NVIDIA RTX Spark chip with up to 20 Arm CPU cores • Blackwell GPU with 6,144 CUDA cores • Up to 128GB unified memory • Runs up to 120B parameter AI models locally • 15-inch mini-LED display with 2,000 nits peak brightness • Dual-fan cooling with 2.5x the thermal capacity of Surface Laptop 7 This could be a serious AI workstation.

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Ritwik Pavan

93,474 次观看 • 3 个月前

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Chubby♨️

65,993 次观看 • 3 个月前

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Grummz

30,573 次观看 • 3 个月前

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Windows Central

13,939 次观看 • 3 个月前

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Santiago

15,307 次观看 • 3 个月前

$NVDA is entering the consumer PC chip market with RTX Spark, an $ARM based CPU/GPU/AI chip for Windows laptops and mini-PCs. The flagship version includes 20 CPU cores, 6,144 GPU cores, and up to 128GB of unified LPDDR5X memory, aimed at local AI agents, creators, developers, and gaming. Nvidia says 30+ laptops and 10+ desktops are already in development with partners including Microsoft, Dell, HP, Asus, Lenovo, MSI, Acer, and Gigabyte.

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Wall St Engine

109,479 次观看 • 3 个月前

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YanXbt

45,924 次观看 • 3 个月前

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Yusuf Mehdi

14,563 次观看 • 3 个月前

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Shishir

333,685 次观看 • 3 个月前

.NVIDIA AI kindly sent me a DGX Spark! As soon as I got back from SF, I unboxed it and wanted to share a first look with you. It measures just 15 × 15 × 5.05 cm and weighs 1.2 kg. Inside: • NVIDIA GB10 Grace Blackwell Superchip • 128 GB of unified memory shared by the CPU and GPU • Up to 1 petaflop of theoretical AI compute at FP4 precision with sparsity According to NVIDIA, this little machine can run models with up to 200 billion parameters locally with suitable quantization, and fine-tune models with up to 70 billion parameters using memory-efficient methods such as QLoRA. You can use it to fine-tune models on your own data, customize image generation, and build AI assistants that run locally. I’m looking forward to trying some of those workflows myself. Thanks again to the NVIDIA team for sending this over (NVIDIA RTX Spark)! Once I’m back home, I’ll record some hands-on demos of interesting use cases and share what I learn with you.

.NVIDIA AI kindly sent me a DGX Spark! As soon as I got back from SF, I unboxed it and wanted to share a first look with you. It measures just 15 × 15 × 5.05 cm and weighs 1.2 kg. Inside: • NVIDIA GB10 Grace Blackwell Superchip • 128 GB of unified memory shared by the CPU and GPU • Up to 1 petaflop of theoretical AI compute at FP4 precision with sparsity According to NVIDIA, this little machine can run models with up to 200 billion parameters locally with suitable quantization, and fine-tune models with up to 70 billion parameters using memory-efficient methods such as QLoRA. You can use it to fine-tune models on your own data, customize image generation, and build AI assistants that run locally. I’m looking forward to trying some of those workflows myself. Thanks again to the NVIDIA team for sending this over (NVIDIA RTX Spark)! Once I’m back home, I’ll record some hands-on demos of interesting use cases and share what I learn with you.

Chubby♨️

129,604 次观看 • 4 天前

JENSEN HUANG JUST HELD UP A CHIP THAT KILLS YOUR ENTIRE AI BILL nvidia and microsoft just announced the rtx spark at computex. 1 petaflop of ai on a chip you can hold in one hand. 128gb unified memory. 20 core cpu. full nvidia stack. most people spend $200 a month on chatgpt, claude, midjourney and cursor. that is $2,400 a year renting intelligence from someone else. this chip runs all of it locally for $5 in electricity per month. developers are already offering local ai setups to small businesses for $500 a month. ten clients. $5,000 a month. one chip doing all the work. the people who move on this in 2025 will look very far ahead by 2027. save this now.

JENSEN HUANG JUST HELD UP A CHIP THAT KILLS YOUR ENTIRE AI BILL nvidia and microsoft just announced the rtx spark at computex. 1 petaflop of ai on a chip you can hold in one hand. 128gb unified memory. 20 core cpu. full nvidia stack. most people spend $200 a month on chatgpt, claude, midjourney and cursor. that is $2,400 a year renting intelligence from someone else. this chip runs all of it locally for $5 in electricity per month. developers are already offering local ai setups to small businesses for $500 a month. ten clients. $5,000 a month. one chip doing all the work. the people who move on this in 2025 will look very far ahead by 2027. save this now.

winkle.

16,657 次观看 • 3 个月前

NVIDIA just handed every solo creator and freelancer an unfair advantage. Jensen Huang walked on stage and announced RTX Spark. An ARM-based laptop chip that nobody saw coming. They called it the most power efficient PC chip ever built. 20 cores. Blackwell graphics. 6144 CUDA cores. Up to 128GB of LPDDR5X memory. But forget the spec sheet for a second. Here is what actually matters. RTX Spark is built to run AI models locally. No cloud subscription. No API costs. No waiting on a server somewhere. Everything runs directly on the laptop at full speed. That changes the math completely for anyone using AI to make money. The guy generating 3D assets in Blender with Claude his renders now take minutes instead of hours. More projects per day. More income per week. The girl producing AI kids content for YouTube local rendering means no upload wait times, no generation limits, no monthly fees eating into her margins. The freelancer building websites and automating outreach every AI tool in his stack now runs faster and cheaper than before. 30 laptops from Asus, Dell, Lenovo, MSI and others. Available this fall. For years the barrier was hardware. You needed an expensive setup to run serious AI workflows locally. NVIDIA just put that power inside a thin laptop anyone can carry anywhere. The people who already figured out how to monetize AI are about to move twice as fast. The people who haven’t started yet just ran out of excuses. Save this.

NVIDIA just handed every solo creator and freelancer an unfair advantage. Jensen Huang walked on stage and announced RTX Spark. An ARM-based laptop chip that nobody saw coming. They called it the most power efficient PC chip ever built. 20 cores. Blackwell graphics. 6144 CUDA cores. Up to 128GB of LPDDR5X memory. But forget the spec sheet for a second. Here is what actually matters. RTX Spark is built to run AI models locally. No cloud subscription. No API costs. No waiting on a server somewhere. Everything runs directly on the laptop at full speed. That changes the math completely for anyone using AI to make money. The guy generating 3D assets in Blender with Claude his renders now take minutes instead of hours. More projects per day. More income per week. The girl producing AI kids content for YouTube local rendering means no upload wait times, no generation limits, no monthly fees eating into her margins. The freelancer building websites and automating outreach every AI tool in his stack now runs faster and cheaper than before. 30 laptops from Asus, Dell, Lenovo, MSI and others. Available this fall. For years the barrier was hardware. You needed an expensive setup to run serious AI workflows locally. NVIDIA just put that power inside a thin laptop anyone can carry anywhere. The people who already figured out how to monetize AI are about to move twice as fast. The people who haven’t started yet just ran out of excuses. Save this.

Shelpid.WI3M

27,829 次观看 • 3 个月前

FOUR 600W GPUS. 384GB OF GDDR7. AND DELL MADE THE INSTALL LOOK EASIER THAN BUILDING A GAMING PC. this is a Dell PowerEdge XE7740 receiving four NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. each card brings: 96GB GDDR7 ECC 24,064 CUDA cores 4 PFLOPS FP4 up to 600W but the most satisfying part is the cable management. every GPU power lead is already routed, labeled and held beside its assigned slot. drop in the card. lock the retention hardware. connect one 16-pin plug. no loose bundle hanging across the airflow path and no guessing which cable belongs where. the four-card build totals 384GB of GPU memory and 16 PFLOPS FP4. the 4U XE7740 supports up to eight of these GPUs, doubling that to 768GB and 32 PFLOPS FP4. honest line: the memory remains split between separate cards, and four GPUs can still pull up to 2.4kW. Dell made a rack-scale AI server this dense without turning the next GPU swap into cable surgery. bookmark & watch today ↓

FOUR 600W GPUS. 384GB OF GDDR7. AND DELL MADE THE INSTALL LOOK EASIER THAN BUILDING A GAMING PC. this is a Dell PowerEdge XE7740 receiving four NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. each card brings: 96GB GDDR7 ECC 24,064 CUDA cores 4 PFLOPS FP4 up to 600W but the most satisfying part is the cable management. every GPU power lead is already routed, labeled and held beside its assigned slot. drop in the card. lock the retention hardware. connect one 16-pin plug. no loose bundle hanging across the airflow path and no guessing which cable belongs where. the four-card build totals 384GB of GPU memory and 16 PFLOPS FP4. the 4U XE7740 supports up to eight of these GPUs, doubling that to 768GB and 32 PFLOPS FP4. honest line: the memory remains split between separate cards, and four GPUs can still pull up to 2.4kW. Dell made a rack-scale AI server this dense without turning the next GPU swap into cable surgery. bookmark & watch today ↓

Grimmer

26,693 次观看 • 1 个月前

Xiaomi announces the Xring O3. The Xiaomi 18 Fold wide foldable and Xiaomi Mi Pad 9 Pro Max are launching with this chip next month, i.e., September 2026. Xring O3: - 10 cores CPU 2 x 4.35 GHz C1-Ultra 4 x 3.68 GHz C1-Premium 4 x 3.15 GHz C1-Pro - 16-core G2-Ultra NX GPU - 4-core low power NPU with 200 TOPS - TSMC 3nm node - Scores 5.22 million on AnTuTu - LPDDR6 RAM support - Geekbench score 3,945 single-core and 15,221 multi-core Xring O100: - 6nm wafer-level vertical stacking (wafer on wafer) - AI accelerator chip for large-scale edge computing - Ultra-high bandwidth of 1.22 TB/s - Edge inference speed up to 330 TPS Xring D100: - 3nm AI chip for intelligent autonomous driving with high computing power - 20-core high-performance CPU - 16-core high-performance NPU - 160GB unified memory that can deploy ultra-large models with up to 200B parameters

Xiaomi announces the Xring O3. The Xiaomi 18 Fold wide foldable and Xiaomi Mi Pad 9 Pro Max are launching with this chip next month, i.e., September 2026. Xring O3: - 10 cores CPU 2 x 4.35 GHz C1-Ultra 4 x 3.68 GHz C1-Premium 4 x 3.15 GHz C1-Pro - 16-core G2-Ultra NX GPU - 4-core low power NPU with 200 TOPS - TSMC 3nm node - Scores 5.22 million on AnTuTu - LPDDR6 RAM support - Geekbench score 3,945 single-core and 15,221 multi-core Xring O100: - 6nm wafer-level vertical stacking (wafer on wafer) - AI accelerator chip for large-scale edge computing - Ultra-high bandwidth of 1.22 TB/s - Edge inference speed up to 330 TPS Xring D100: - 3nm AI chip for intelligent autonomous driving with high computing power - 20-core high-performance CPU - 16-core high-performance NPU - 160GB unified memory that can deploy ultra-large models with up to 200B parameters

Abhishek Yadav

22,049 次观看 • 1 个月前

AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

starmex

981,550 次观看 • 3 个月前

Brand new SeedSigner Plus with smartcard! Quick unboxing & demo of creating a seed, saving to a Seedkeeper card and restoring from that card. This is the ultimate format IMO: - large screen - great to hold in hands - easy clicky buttons - but especially the fact that we longer have to manually transcribe QR codes is such a huge win for its already superior user experience. & Oh yeah, USB-C power! & spring loaded SD-card slot I’ve been dreaming of this for quite a while and finally it’s here. After having worked with +15 hardware wallets and ending up hating most of them mostly because of firmware & overal user experience. This really hits the mark! Comes preassembled & with Satochip Seedkeeper card, available at Bitsaga

Rob | Bitsaga.be

10,420 次观看 • 8 个月前

THIS GUY WIRED GMKTEC EVO-X2 BOXES, A $40 USB-C BRICK AND AN RTX 3090 INTO A $6,800/MONTH OFFLINE AI FARM it looks like a messy rack with purple cables, mini pcs and a gpu lying on the desk. but the real point is brutal: one $1,800 gmktec evo-x2 can replace a $440/month ai stack, then the rack turns it into a local backend. the evo-x2 runs amd’s ryzen ai max+ 395 with 128gb of unified memory shared between cpu, gpu and npu. on linux, that gives up to 110gb of usable vram for local models without renting cloud compute. that is why the setup gets weird. qwen3-coder 30b, llama 3.3 70b, deepseek v3 and 200b-class models can run from boxes smaller than consoles. the rtx 3090 becomes a 24gb side node, not the whole system. the $40 anker nano ii 65w brick is the funny part. cheap power, tiny machines, one old gpu and local inference. the whole stack starts looking less like a workstation and more like a private ai grid. the subscription math is ugly. claude code max is $200/month, chatgpt pro is $200/month, cursor is $20/month and gemini adds another $20. that is $5,280/year before api usage even starts. point claude code at localhost, pull models through ollama and run agents all day: code reviews, scraping, transcription, research reports and client automations. 3 clients at $2,200/month turns this into a $6,800/month backend. bookmark this and read the article below

THIS GUY WIRED GMKTEC EVO-X2 BOXES, A $40 USB-C BRICK AND AN RTX 3090 INTO A $6,800/MONTH OFFLINE AI FARM it looks like a messy rack with purple cables, mini pcs and a gpu lying on the desk. but the real point is brutal: one $1,800 gmktec evo-x2 can replace a $440/month ai stack, then the rack turns it into a local backend. the evo-x2 runs amd’s ryzen ai max+ 395 with 128gb of unified memory shared between cpu, gpu and npu. on linux, that gives up to 110gb of usable vram for local models without renting cloud compute. that is why the setup gets weird. qwen3-coder 30b, llama 3.3 70b, deepseek v3 and 200b-class models can run from boxes smaller than consoles. the rtx 3090 becomes a 24gb side node, not the whole system. the $40 anker nano ii 65w brick is the funny part. cheap power, tiny machines, one old gpu and local inference. the whole stack starts looking less like a workstation and more like a private ai grid. the subscription math is ugly. claude code max is $200/month, chatgpt pro is $200/month, cursor is $20/month and gemini adds another $20. that is $5,280/year before api usage even starts. point claude code at localhost, pull models through ollama and run agents all day: code reviews, scraping, transcription, research reports and client automations. 3 clients at $2,200/month turns this into a $6,800/month backend. bookmark this and read the article below

Gipp 🦅

20,031 次观看 • 3 个月前

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Milk Road AI

11,819 次观看 • 3 个月前

NVIDIA'S BIGGEST COMPETITOR AMD BUILT A BOX THAT RUNS A 235B MODEL AND KILLS $200 OPENAI AND $200 CLAUDE CODE FOR $9 A MONTH amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on a single piece of silicon with 128gb of unified memory the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months ollama installs in one command and claude code points at localhost so nothing leaves the machine and nothing costs per request bookmark this and read the article below

NVIDIA'S BIGGEST COMPETITOR AMD BUILT A BOX THAT RUNS A 235B MODEL AND KILLS $200 OPENAI AND $200 CLAUDE CODE FOR $9 A MONTH amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on a single piece of silicon with 128gb of unified memory the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months ollama installs in one command and claude code points at localhost so nothing leaves the machine and nothing costs per request bookmark this and read the article below

starmex

93,580 次观看 • 3 个月前

So what is the new Commodore 64 Ultimate and what do you get in the box? Everything you need to know is below... OVERVIEW • The Commodore 64 Ultimate comes in 3 varieties: Basic Beige, Starlight Edition, and Founders Edition. • This is the first official Commodore 64 released in over 30 years!!!! COMPATIBILITY • Works with your original Commodore 64 gear (99% compatible). • Supports cartridges, datasette, and 1541 disk drives. • Plug in your datasette, 1541 disk drive, and cartridges and play your favourite games. DISPLAY OPTIONS • Supports both HDMI and CRT. WHAT'S IN THE BOX? • Spiral-bound User Guide • Power Supply • Quick Start User Guide • HDMI cable • Commodore 64 Ultimate (of course) • Stickers • 64GB USB drive with 50+ fully licensed games POWER SUPPLY • 12V, 100-240V with worldwide adapters • Usable in any corner of the Earth CONNECTIONS • 3x USB-A 2.0 • 1x USB-C • Internal MicroSD • HDMI • 8-pin DIN • 3.5mm audio headphone jack • Optical S/PDIF • Ethernet (100Mbps) • Wi-Fi (Built-in) • Cartridge Port • Datasette Port • Disk Drive Port • 2x DB-9 joystick ports • User Port • Power (12V DC) TECH SPECS • Core: AMD Xilinx Artix-7 FPGA • Memory: 128MB DDR2 RAM, 16MB NOR flash • Video: ○ 1080p @ 50Hz (PAL) or 60Hz (NTSC) ○ HDMI-certified ○ Virtually zero-lag ○ DVI-compatible via HDMI ○ Analog via DIN-8: CVBS, S-Video, or RGB • Audio: ○ 2 × SID sockets (6581/8580) with auto voltage and filter detection ○ UltiSID octal core FPGA SID emulation ○ SID-TAP header • Controls: ○ Rocker switch (power, reset, menu, freeze) • Storage & Compatibility: ○ USB thumb drives: FAT, FAT32, exFAT ○ File formats: .D64, .D71, .D81, .G64, .T64, .TAP, .PRG, .ROM, and more ○ C64 OS support coming soon ○ Original Commodore 64 ROMs included under license or via guided ROM setup on first boot ○ ISO-9660 image support ○ Integrated Ultimate-II+ functionality with tape emulation and DMA loader • Keyboard: ○ 66-key mechanical ○ Gateron Pro 3.0 55g switches ○ Original C64 layout and shapes ○ Full NKRO, USB plug ○ 70 RGB LEDs ○ Industry standard stabilizers and wires The Commodore 64 Ultimate is well, ultimate! Pre-order from their website COMMODORE (dot) NET

So what is the new Commodore 64 Ultimate and what do you get in the box? Everything you need to know is below... OVERVIEW • The Commodore 64 Ultimate comes in 3 varieties: Basic Beige, Starlight Edition, and Founders Edition. • This is the first official Commodore 64 released in over 30 years!!!! COMPATIBILITY • Works with your original Commodore 64 gear (99% compatible). • Supports cartridges, datasette, and 1541 disk drives. • Plug in your datasette, 1541 disk drive, and cartridges and play your favourite games. DISPLAY OPTIONS • Supports both HDMI and CRT. WHAT'S IN THE BOX? • Spiral-bound User Guide • Power Supply • Quick Start User Guide • HDMI cable • Commodore 64 Ultimate (of course) • Stickers • 64GB USB drive with 50+ fully licensed games POWER SUPPLY • 12V, 100-240V with worldwide adapters • Usable in any corner of the Earth CONNECTIONS • 3x USB-A 2.0 • 1x USB-C • Internal MicroSD • HDMI • 8-pin DIN • 3.5mm audio headphone jack • Optical S/PDIF • Ethernet (100Mbps) • Wi-Fi (Built-in) • Cartridge Port • Datasette Port • Disk Drive Port • 2x DB-9 joystick ports • User Port • Power (12V DC) TECH SPECS • Core: AMD Xilinx Artix-7 FPGA • Memory: 128MB DDR2 RAM, 16MB NOR flash • Video: ○ 1080p @ 50Hz (PAL) or 60Hz (NTSC) ○ HDMI-certified ○ Virtually zero-lag ○ DVI-compatible via HDMI ○ Analog via DIN-8: CVBS, S-Video, or RGB • Audio: ○ 2 × SID sockets (6581/8580) with auto voltage and filter detection ○ UltiSID octal core FPGA SID emulation ○ SID-TAP header • Controls: ○ Rocker switch (power, reset, menu, freeze) • Storage & Compatibility: ○ USB thumb drives: FAT, FAT32, exFAT ○ File formats: .D64, .D71, .D81, .G64, .T64, .TAP, .PRG, .ROM, and more ○ C64 OS support coming soon ○ Original Commodore 64 ROMs included under license or via guided ROM setup on first boot ○ ISO-9660 image support ○ Integrated Ultimate-II+ functionality with tape emulation and DMA loader • Keyboard: ○ 66-key mechanical ○ Gateron Pro 3.0 55g switches ○ Original C64 layout and shapes ○ Full NKRO, USB plug ○ 70 RGB LEDs ○ Industry standard stabilizers and wires The Commodore 64 Ultimate is well, ultimate! Pre-order from their website COMMODORE (dot) NET

Commodore Computer Museum 🕹

37,234 次观看 • 1 年前