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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 Aufrufe • vor 3 Monaten •via X (Twitter)

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35 Kommentare

Profilbild von James O'Reilly

James O'Reillyvor 3 Monaten

- up to 15 minutes of battery life

Profilbild von Jj

Jjvor 3 Monaten

will it try to run unscheduled update mid agent tasks?

Profilbild von Karsten Samaschke

Karsten Samaschkevor 3 Monaten

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

Profilbild von Baud

Baudvor 3 Monaten

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

Profilbild von Sebastian Aaltonen

Sebastian Aaltonenvor 3 Monaten

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.

Profilbild von Ivan ➜ dooing.ai

Ivan ➜ dooing.aivor 3 Monaten

The problem is that it comes with Windows installed.

Profilbild von Dejan Velimirovic

Dejan Velimirovicvor 3 Monaten

All of that, and then cripple it with Windows.

Profilbild von TheVallance

TheVallancevor 3 Monaten

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.

Profilbild von muku

mukuvor 3 Monaten

At this point apple should introduce a touch screen laptop

Profilbild von rafalee

rafaleevor 3 Monaten

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

Profilbild von Jordan Huang

Jordan Huangvor 3 Monaten

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

Profilbild von Wissam Benhaddad

Wissam Benhaddadvor 3 Monaten

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

Profilbild von Ⓒⓡⓨⓟⓣⓞⓝⓘⓐⓝ

Ⓒⓡⓨⓟⓣⓞⓝⓘⓐⓝvor 3 Monaten

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

Profilbild von RKal

RKalvor 3 Monaten

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!

Profilbild von Riccardo Tagliavia

Riccardo Tagliaviavor 3 Monaten

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

Profilbild von Martín López Arango

Martín López Arangovor 3 Monaten

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. 🤡🤡

Profilbild von A²

A²vor 3 Monaten

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

Profilbild von coconutTrader

coconutTradervor 3 Monaten

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

Profilbild von Domi

Domivor 3 Monaten

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

Profilbild von Jason Kneen

Jason Kneenvor 3 Monaten

WHAT it runs macOS? Oh wait...

Profilbild von King Troll

King Trollvor 3 Monaten

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.

Profilbild von Basil Rari

Basil Rarivor 3 Monaten

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

Profilbild von DS

DSvor 3 Monaten

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!

Profilbild von Juice Lab UI/UX & Development Agency

Juice Lab UI/UX & Development Agencyvor 3 Monaten

MacBook Pro finally getting some real competition. 🔥

Profilbild von Kosta Milovanovic

Kosta Milovanovicvor 3 Monaten

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.

Profilbild von OOM

OOMvor 3 Monaten

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

Profilbild von Till Felippi

Till Felippivor 3 Monaten

Unfortunately it still runs on Windows, so...

Profilbild von Kommatose

Kommatosevor 3 Monaten

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

Profilbild von JC

JCvor 3 Monaten

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

Profilbild von Het Mehta

Het Mehtavor 3 Monaten

What will we do about Windows OS?

Profilbild von Singularity City

Singularity Cityvor 3 Monaten

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

Profilbild von Matt Wolfgang

Matt Wolfgangvor 3 Monaten

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.

Profilbild von Reformed Sinner

Reformed Sinnervor 3 Monaten

Windows 11? No thanks

Profilbild von Patrick Kelly-Weller

Patrick Kelly-Wellervor 3 Monaten

Still running Windows though...

Profilbild von Wilson Samiano

Wilson Samianovor 3 Monaten

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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Chubby♨️

129,604 Aufrufe • vor 4 Tagen

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 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 1 Monat

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 Aufrufe • vor 1 Monat

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 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 8 Monaten

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 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 1 Jahr