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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

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783,005 views • 2 months ago •via X (Twitter)

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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.

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.

Ritwik Pavan

93,474 views • 2 months ago

First hands-on with Microsoft’s new Surface Laptop Ultra. Microsoft is clearly positioning this as a new class of creator and AI laptop, powered by new NVIDIA silicon with an RTX GPU built for local AI, creative workflows, and gaming. A few standout specs: -New NVIDIA chip with RTX GPU -Up to 1 petaflop of AI compute -Up to 128GB unified memory -15-inch mini-LED PixelSense Ultra touchscreen -3:2 aspect ratio -262 PPI -Up to 2,000 nits peak HDR brightness -Less than 18mm thick

First hands-on with Microsoft’s new Surface Laptop Ultra. Microsoft is clearly positioning this as a new class of creator and AI laptop, powered by new NVIDIA silicon with an RTX GPU built for local AI, creative workflows, and gaming. A few standout specs: -New NVIDIA chip with RTX GPU -Up to 1 petaflop of AI compute -Up to 128GB unified memory -15-inch mini-LED PixelSense Ultra touchscreen -3:2 aspect ratio -262 PPI -Up to 2,000 nits peak HDR brightness -Less than 18mm thick

Chubby♨️

65,993 views • 2 months ago

Nvidia announces the new RTX Spark, a new platform powered by the NX1 CPU, and shows off Spark laptops running 007 First Light and Forza 6. The CPU has 20 ARM based cores and a Blackwell RTX GPU with 6144 CUDA Cores. This is the same core count as a 5070, but with 128GB of unified LPDDR5X RAM memory sitting in the same package as the CPU and GPU. The entire Nvidia software stack is available, particularly CUDA, vital for AI. Nvidia's new laptops will likely be ideal for running local LLMs be cause the unified memory means you can load models up to 120-180B parameters (quantized). These laptops are expected to ship later this year and could become strong competitors to high-end MacBooks and even Mac Studios for local AI workloads, thanks to CUDA support and unified memory. Price is unannounced.

Nvidia announces the new RTX Spark, a new platform powered by the NX1 CPU, and shows off Spark laptops running 007 First Light and Forza 6. The CPU has 20 ARM based cores and a Blackwell RTX GPU with 6144 CUDA Cores. This is the same core count as a 5070, but with 128GB of unified LPDDR5X RAM memory sitting in the same package as the CPU and GPU. The entire Nvidia software stack is available, particularly CUDA, vital for AI. Nvidia's new laptops will likely be ideal for running local LLMs be cause the unified memory means you can load models up to 120-180B parameters (quantized). These laptops are expected to ship later this year and could become strong competitors to high-end MacBooks and even Mac Studios for local AI workloads, thanks to CUDA support and unified memory. Price is unannounced.

Grummz

30,573 views • 2 months ago

Surface Laptop Ultra is the closest Microsoft has ever come to building a no‑compromise performance machine, and the details are interesting to say the least. A full NVIDIA RTX Spark platform, up to 128GB of unified memory, a 15‑inch mini‑LED display hitting 2,000 nits, native Arm gaming progress, and a cooling system built for sustained heavy workloads. What Microsoft and NVIDIA are doing together here raises real questions about where Windows on Arm goes next and how this shifts expectations for pro‑grade laptops. #Surface (1/2)

Surface Laptop Ultra is the closest Microsoft has ever come to building a no‑compromise performance machine, and the details are interesting to say the least. A full NVIDIA RTX Spark platform, up to 128GB of unified memory, a 15‑inch mini‑LED display hitting 2,000 nits, native Arm gaming progress, and a cooling system built for sustained heavy workloads. What Microsoft and NVIDIA are doing together here raises real questions about where Windows on Arm goes next and how this shifts expectations for pro‑grade laptops. #Surface (1/2)

Windows Central

13,939 views • 2 months ago

We are getting 2 new devices from NVIDIA and Microsoft: 1. The DGX Station, with GB300 superchip and up to 748GB of memory. 2. The RTX Spark laptop, with 1 petaflop of AI performance and 128GB of unified memory. The hardware here is pretty bonkers! Think about it this way:

We are getting 2 new devices from NVIDIA and Microsoft: 1. The DGX Station, with GB300 superchip and up to 748GB of memory. 2. The RTX Spark laptop, with 1 petaflop of AI performance and 128GB of unified memory. The hardware here is pretty bonkers! Think about it this way:

Santiago

15,204 views • 2 months ago

$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.

$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.

Wall St Engine

109,479 views • 2 months ago

NVIDIA JUST FEATURED HERMES AGENT AT COMPUTEX 2026. Hermes is integrating with NVIDIA OpenShell and Microsoft security primitives for secure on-device agents on Windows. Dillon Rolnick, CEO of Nous Research: "RTX Spark and NVIDIA OpenShell give Hermes users a powerful and secure environment for agents to run and work alongside you. You realize you're buying a full-fledged assistant, not a typical laptop." RTX Spark specs: → 1 petaflop AI performance → 20-core Grace CPU (built with MediaTek) → Blackwell GPU, 6,144 CUDA cores → 5th gen Tensor Cores with FP4 precision → up to 128GB LPDDR5X unified memory, 300 GB/s → NVLink C2C chip-to-chip interconnect → runs 120B parameter LLMs locally → up to 1M token context window on device → 2x inference speed via multi-token prediction RTX Spark devices ship Fall 2026 from ASUS, Dell, HP, Lenovo, Microsoft Surface, MSI. full NVIDIA press release in the comments. full Hermes setup guide in the article 👇

NVIDIA JUST FEATURED HERMES AGENT AT COMPUTEX 2026. Hermes is integrating with NVIDIA OpenShell and Microsoft security primitives for secure on-device agents on Windows. Dillon Rolnick, CEO of Nous Research: "RTX Spark and NVIDIA OpenShell give Hermes users a powerful and secure environment for agents to run and work alongside you. You realize you're buying a full-fledged assistant, not a typical laptop." RTX Spark specs: → 1 petaflop AI performance → 20-core Grace CPU (built with MediaTek) → Blackwell GPU, 6,144 CUDA cores → 5th gen Tensor Cores with FP4 precision → up to 128GB LPDDR5X unified memory, 300 GB/s → NVLink C2C chip-to-chip interconnect → runs 120B parameter LLMs locally → up to 1M token context window on device → 2x inference speed via multi-token prediction RTX Spark devices ship Fall 2026 from ASUS, Dell, HP, Lenovo, Microsoft Surface, MSI. full NVIDIA press release in the comments. full Hermes setup guide in the article 👇

YanXbt

45,924 views • 2 months ago

Big day for Windows with Microsoft and NVIDIA announcing the next generation of Windows PCs powered by NVIDIA RTX Spark. These devices bring together performance, efficiency, graphics, and on-device AI in thin-and-light designs that make powerful experiences feel faster. RTX Spark delivers 1 petaflop of AI performance, industry-leading performance-per-watt, full stack NVIDIA AI and RTX graphics technology, and up to 128GB of unified memory. And the new Surface Laptop Ultra shows what happens when that power is thoughtfully applied. Creators and developers can move faster, push further, and build richer experiences locally on Windows, without compromise.

Big day for Windows with Microsoft and NVIDIA announcing the next generation of Windows PCs powered by NVIDIA RTX Spark. These devices bring together performance, efficiency, graphics, and on-device AI in thin-and-light designs that make powerful experiences feel faster. RTX Spark delivers 1 petaflop of AI performance, industry-leading performance-per-watt, full stack NVIDIA AI and RTX graphics technology, and up to 128GB of unified memory. And the new Surface Laptop Ultra shows what happens when that power is thoughtfully applied. Creators and developers can move faster, push further, and build richer experiences locally on Windows, without compromise.

Yusuf Mehdi

14,487 views • 2 months ago

Dell has announced its MacBook Neo competitor: the new XPS 13 starting at $699 - 13.4” 2.5K 120Hz Touch Display - 500 nits brightness - 100% DCI-P3 + Anti-Glare - Full CNC aluminum build - 12.7mm thin | ~1kg - Backlit keyboard - Intel Core 5 320 (Wildcat Lake) - 8GB RAM + 512GB SSD - 52Wh battery - 2x USB-C 3.2 Gen 2 ports - Windows Hello - Quad speakers - Wi-Fi 7 | No 3.5mm jack $599 for students. Available starting June. A more powerful version with Intel Core Ultra 7 355, up to 32GB RAM and 1TB storage is coming later this summer.

Dell has announced its MacBook Neo competitor: the new XPS 13 starting at $699 - 13.4” 2.5K 120Hz Touch Display - 500 nits brightness - 100% DCI-P3 + Anti-Glare - Full CNC aluminum build - 12.7mm thin | ~1kg - Backlit keyboard - Intel Core 5 320 (Wildcat Lake) - 8GB RAM + 512GB SSD - 52Wh battery - 2x USB-C 3.2 Gen 2 ports - Windows Hello - Quad speakers - Wi-Fi 7 | No 3.5mm jack $599 for students. Available starting June. A more powerful version with Intel Core Ultra 7 355, up to 32GB RAM and 1TB storage is coming later this summer.

Shishir

333,685 views • 2 months ago

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 views • 2 months ago

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

27,789 views • 2 months ago

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 views • 19 days ago

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

21,771 views • 4 days ago

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

980,353 views • 2 months ago

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 views • 7 months ago

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 views • 2 months ago

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

93,465 views • 2 months ago

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 views • 2 months ago

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 views • 1 year ago

AMD CEO Lisa Su just killed Nvidia’s $4,000 AI box with a $1,499 lunchbox. She walked on stage, held it in one hand, and ran a 235 billion parameter model live. No data center. No cloud. No rented GPU. The chip inside is something nobody saw coming. AMD’s Ryzen AI Max+ 395 is the first x86 silicon where CPU and GPU share the same 128GB of memory. That single trick lets a desktop run models that used to need a server rack. Out of those 128GB, Linux hands the GPU 110GB to play with. For context, an RTX 5090 gives you 32GB. A 4090 gives you 24. This box gives you more than three times either of them, in a chassis the size of a thick paperback. The benchmark that broke the room: this chip beat an Nvidia RTX 5080 by more than 3x on DeepSeek R1 inference. A $1,499 lunchbox outrunning a $1,000 discrete graphics card on a real AI workload. Nvidia spent a decade convincing the world you needed their hardware for serious AI. AMD just put that on a desk for half the price. Here is what nobody is telling you. A heavy AI user right now pays $200 for Claude Code Max, $200 for ChatGPT Pro, $20 for Cursor, $20 for Gemini. That is $5,280 a year leaving your account. The box pays itself off in 9 months and then runs free for the rest of its life. Install Ollama. Pull Qwen3 235B. Point Claude Code at localhost. Same interface you already use, except now nothing leaves your machine, nothing costs per request, and no company throttles your usage at 3am when you finally have time to build. This is the moment every AI subscription becomes optional. Lawyers stop fearing OpenAI leaks. Developers stop watching the token meter. Founders stop renting H100s for prototypes that never ship because the bill scared them. The first thousand people to figure this out will own the next two years of private AI consulting. Save this, and read the full breakdown article below you are watching the next shift hit before everyone else does.

AMD CEO Lisa Su just killed Nvidia’s $4,000 AI box with a $1,499 lunchbox. She walked on stage, held it in one hand, and ran a 235 billion parameter model live. No data center. No cloud. No rented GPU. The chip inside is something nobody saw coming. AMD’s Ryzen AI Max+ 395 is the first x86 silicon where CPU and GPU share the same 128GB of memory. That single trick lets a desktop run models that used to need a server rack. Out of those 128GB, Linux hands the GPU 110GB to play with. For context, an RTX 5090 gives you 32GB. A 4090 gives you 24. This box gives you more than three times either of them, in a chassis the size of a thick paperback. The benchmark that broke the room: this chip beat an Nvidia RTX 5080 by more than 3x on DeepSeek R1 inference. A $1,499 lunchbox outrunning a $1,000 discrete graphics card on a real AI workload. Nvidia spent a decade convincing the world you needed their hardware for serious AI. AMD just put that on a desk for half the price. Here is what nobody is telling you. A heavy AI user right now pays $200 for Claude Code Max, $200 for ChatGPT Pro, $20 for Cursor, $20 for Gemini. That is $5,280 a year leaving your account. The box pays itself off in 9 months and then runs free for the rest of its life. Install Ollama. Pull Qwen3 235B. Point Claude Code at localhost. Same interface you already use, except now nothing leaves your machine, nothing costs per request, and no company throttles your usage at 3am when you finally have time to build. This is the moment every AI subscription becomes optional. Lawyers stop fearing OpenAI leaks. Developers stop watching the token meter. Founders stop renting H100s for prototypes that never ship because the bill scared them. The first thousand people to figure this out will own the next two years of private AI consulting. Save this, and read the full breakdown article below you are watching the next shift hit before everyone else does.

AdiiX

3,394,390 views • 2 months ago