Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

NVIDIA MADE A $249 DESK CHIP THAT TURNS LOCAL LLMS INTO A TINY PRIVATE BACKEND jetson orin nano super is basically a pocket-size ai board: 67 TOPS, 1024-core ampere gpu, 8gb memory, 7-25w power draw. not a gaming pc, not a rented cloud box, just a small nvidia dev...

gippp69's profile picture

Gipp 🦅

7,037 subscribers

39,609 görüntüleme • 1 ay önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

NVIDIA SHIPS A 50 POUND AI BOARD FOR DATA CENTERS AND A $249 BOX FOR YOUR DESK. ONE COSTS $200/MONTH TO RENT, THE OTHER COSTS $5 IN ELECTRICITY TO OWN jensen huang held up a 50 pound a100 board on stage with 30,000 components and called it a technology marvel. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below
1:07

NVIDIA SHIPS A 50 POUND AI BOARD FOR DATA CENTERS AND A $249 BOX FOR YOUR DESK. ONE COSTS $200/MONTH TO RENT, THE OTHER COSTS $5 IN ELECTRICITY TO OWN jensen huang held up a 50 pound a100 board on stage with 30,000 components and called it a technology marvel. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

23,928 görüntüleme • 1 ay önce

NVIDIA QUIETLY DROPPED A $249 BOX THAT REPLACES YOUR $200/MONTH OPENAI SUBSCRIPTION WITH $2 IN ELECTRICITY it's called the jetson orin nano super. smaller than a wallet, runs at 25 watts, does 70 trillion ai operations per second. runs llama 3, mistral, gemma and deepseek locally with no api fees and no data leaving your house a developer running automations and coding assistants pays $200 a month to openai. the same workload on this box costs $2 a month in electricity and breaks even in 10 weeks install ollama with one command. change one line in your code. point it at localhost instead of openai. everything else works identically 7 billion parameter models handle 80% of what people use chatgpt for. summarization, drafting, coding, document q&a, automation pipelines. total monthly cost drops from $200 to $22 cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who set this up in 2025 are going to look very smart in 2027 bookmark this and read the article below
0:37

NVIDIA QUIETLY DROPPED A $249 BOX THAT REPLACES YOUR $200/MONTH OPENAI SUBSCRIPTION WITH $2 IN ELECTRICITY it's called the jetson orin nano super. smaller than a wallet, runs at 25 watts, does 70 trillion ai operations per second. runs llama 3, mistral, gemma and deepseek locally with no api fees and no data leaving your house a developer running automations and coding assistants pays $200 a month to openai. the same workload on this box costs $2 a month in electricity and breaks even in 10 weeks install ollama with one command. change one line in your code. point it at localhost instead of openai. everything else works identically 7 billion parameter models handle 80% of what people use chatgpt for. summarization, drafting, coding, document q&a, automation pipelines. total monthly cost drops from $200 to $22 cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who set this up in 2025 are going to look very smart in 2027 bookmark this and read the article below

starmex

2,351,387 görüntüleme • 1 ay önce

A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid
0:23

A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid

Gipp 🦅

456,889 görüntüleme • 1 ay önce

JENSEN HUANG PUT 1 PETAFLOP AND 128GB OF GPU MEMORY IN A $150,000 BOX. THE $249 JETSON RUNS THE SAME MODELS AND CUTS A $200/MONTH AI BILL TO $2 IN ELECTRICITY the $249 jetson orin nano super runs llama 3, mistral and deepseek locally at 67 trillion operations per second. zero api fees, zero data leaving your machine, $2 a month in electricity andrej karpathy says 99% of ai users are missing 7 basics that have nothing to do with the hardware. a proper config file, a /raw and /wiki folder, saved reference pages. the model stops guessing and starts helping most people pay $200 a month and still get bad output because the problem was never the model. it was the workflow around it. fix the system and a $249 box handles 80% of what you open chatgpt for the jetson breaks even in 6 weeks at $100/month api spend. after that every month is $98 back in your pocket. first year that's over $1,000 saved from a one-time $249 purchase the people building local ai infrastructure in 2026 with a proper workflow are going to look very far ahead in 2028 bookmark this and read the article below
0:33

JENSEN HUANG PUT 1 PETAFLOP AND 128GB OF GPU MEMORY IN A $150,000 BOX. THE $249 JETSON RUNS THE SAME MODELS AND CUTS A $200/MONTH AI BILL TO $2 IN ELECTRICITY the $249 jetson orin nano super runs llama 3, mistral and deepseek locally at 67 trillion operations per second. zero api fees, zero data leaving your machine, $2 a month in electricity andrej karpathy says 99% of ai users are missing 7 basics that have nothing to do with the hardware. a proper config file, a /raw and /wiki folder, saved reference pages. the model stops guessing and starts helping most people pay $200 a month and still get bad output because the problem was never the model. it was the workflow around it. fix the system and a $249 box handles 80% of what you open chatgpt for the jetson breaks even in 6 weeks at $100/month api spend. after that every month is $98 back in your pocket. first year that's over $1,000 saved from a one-time $249 purchase the people building local ai infrastructure in 2026 with a proper workflow are going to look very far ahead in 2028 bookmark this and read the article below

starmex

86,855 görüntüleme • 1 ay önce

JENSEN HUANG TOLD STANFORD THAT CLOUD AI WILL NEED 1,000 TIMES MORE ENERGY. THE $249 BOX HE BUILT FOR YOUR DESK USES 25 WATTS AND REPLACES A $200/MONTH AI STACK someone posted a 26 year old imac in airplane mode running a modern ai model with no internet. 1.7 million views in 24 hours. the same thing runs on a $249 box from nvidia the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below
2:09

JENSEN HUANG TOLD STANFORD THAT CLOUD AI WILL NEED 1,000 TIMES MORE ENERGY. THE $249 BOX HE BUILT FOR YOUR DESK USES 25 WATTS AND REPLACES A $200/MONTH AI STACK someone posted a 26 year old imac in airplane mode running a modern ai model with no internet. 1.7 million views in 24 hours. the same thing runs on a $249 box from nvidia the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

15,822 görüntüleme • 1 ay önce

JENSEN HUANG SOLD A $249 AI COMPUTER ON STAGE THAT KILLS YOUR $200/MONTH OPENAI BILL. THE VIDEO HAS 217,000 LIKES the box is called the jetson orin nano super. 70 trillion ai operations per second, 25 watts, smaller than a wallet. it runs llama 3, mistral, gemma and deepseek locally with no api fees and no data leaving your house a developer running automations and coding assistants pays $200 a month to openai. the same workload on this box costs $2 a month in electricity and breaks even on the hardware in 10 weeks you install ollama with one command. change one line in your code. point it at localhost instead of openai. everything else works identically 7 billion parameter models handle 80% of what people use chatgpt for. summarization, drafting, coding, document q&a, automation pipelines. the hard 20% you keep claude or gpt for. total monthly cost drops from $200 to $22 cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who set this up in 2025 are going to look very smart in 2027 bookmark this and read the article below
1:18

JENSEN HUANG SOLD A $249 AI COMPUTER ON STAGE THAT KILLS YOUR $200/MONTH OPENAI BILL. THE VIDEO HAS 217,000 LIKES the box is called the jetson orin nano super. 70 trillion ai operations per second, 25 watts, smaller than a wallet. it runs llama 3, mistral, gemma and deepseek locally with no api fees and no data leaving your house a developer running automations and coding assistants pays $200 a month to openai. the same workload on this box costs $2 a month in electricity and breaks even on the hardware in 10 weeks you install ollama with one command. change one line in your code. point it at localhost instead of openai. everything else works identically 7 billion parameter models handle 80% of what people use chatgpt for. summarization, drafting, coding, document q&a, automation pipelines. the hard 20% you keep claude or gpt for. total monthly cost drops from $200 to $22 cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who set this up in 2025 are going to look very smart in 2027 bookmark this and read the article below

starmex

1,995,664 görüntüleme • 1 ay önce

A 2TB HOME AI SUPERCOMPUTER CAN RUN MODELS MOST PEOPLE STILL RENT FROM THE CLOUD The video is not about a clean desk setup. It is a local AI rack: multiple mini-machines stitched together, 2TB of memory, enough compute to run heavyweight models like DeepSeek 671B without asking AWS for permission every hour. That matters because the cloud version of AI looks cheap until you use it like infrastructure. Agents running all day. Private docs. Long context. Local storage. Experiments. Fine-tuning. Automation. Suddenly the bill is not $20/month anymore. This setup is the opposite trade. Pay upfront, own the machine, keep the data local, and stop treating every serious AI workload like a subscription meter. The article is pointing at the same bigger shift: serious builders are moving from "which chatbot is best" to "where does my intelligence actually run." Cloud is convenient. Local compute is control. The next edge is not just better prompts. It is owning the box that keeps thinking when the browser tab is closed.
0:40

A 2TB HOME AI SUPERCOMPUTER CAN RUN MODELS MOST PEOPLE STILL RENT FROM THE CLOUD The video is not about a clean desk setup. It is a local AI rack: multiple mini-machines stitched together, 2TB of memory, enough compute to run heavyweight models like DeepSeek 671B without asking AWS for permission every hour. That matters because the cloud version of AI looks cheap until you use it like infrastructure. Agents running all day. Private docs. Long context. Local storage. Experiments. Fine-tuning. Automation. Suddenly the bill is not $20/month anymore. This setup is the opposite trade. Pay upfront, own the machine, keep the data local, and stop treating every serious AI workload like a subscription meter. The article is pointing at the same bigger shift: serious builders are moving from "which chatbot is best" to "where does my intelligence actually run." Cloud is convenient. Local compute is control. The next edge is not just better prompts. It is owning the box that keeps thinking when the browser tab is closed.

Nekt0

54,212 görüntüleme • 12 gün önce

A $1,800 MINI PC ON YOUR DESK JUST MADE $5,040 A YEAR IN AI SUBSCRIPTIONS POINTLESS Claude Code Max, ChatGPT Pro, Cursor. That's $420 a month just to keep the workflow alive. A GMKtec mini PC with 128GB of unified memory runs the same stack locally for a one time cost. Ollama, Linux, local endpoints, Claude Code pointed at your own machine. Every prompt stays on your desk. Nothing touches the cloud.
8:33

A $1,800 MINI PC ON YOUR DESK JUST MADE $5,040 A YEAR IN AI SUBSCRIPTIONS POINTLESS Claude Code Max, ChatGPT Pro, Cursor. That's $420 a month just to keep the workflow alive. A GMKtec mini PC with 128GB of unified memory runs the same stack locally for a one time cost. Ollama, Linux, local endpoints, Claude Code pointed at your own machine. Every prompt stays on your desk. Nothing touches the cloud.

0xMarioNawfal

50,730 görüntüleme • 24 gün önce

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
0:47

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 görüntüleme • 22 gün önce

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below
2:00

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below

John Doe

193,709 görüntüleme • 1 ay önce

$5,280 a year on AI subscriptions. Claude Max, ChatGPT Pro, Gemini, Cursor. $440 every month going to the cloud. a mini PC for $1,700 does the same thing on your desk. GMKtec EVO-X2 - a box the size of a hardcover book. inside it the chip that Lisa Su personally brought on stage at CES and signed with her name. 128GB of memory in one pool. runs models that cloud subscriptions behind $200 paywalls throttle during peak hours. here there are no limits - your hardware, your rules. NVIDIA's RTX 5080 at $1,000+ lost to this box by 3x on a real AI workload. a discrete GPU beaten by a mini PC the size of a lunchbox. electricity - $9 a month. pays for itself in 10 months. after that $440 every month stays in your pocket. the cloud made sense when nothing on a desk could compete. now it can.
0:32

$5,280 a year on AI subscriptions. Claude Max, ChatGPT Pro, Gemini, Cursor. $440 every month going to the cloud. a mini PC for $1,700 does the same thing on your desk. GMKtec EVO-X2 - a box the size of a hardcover book. inside it the chip that Lisa Su personally brought on stage at CES and signed with her name. 128GB of memory in one pool. runs models that cloud subscriptions behind $200 paywalls throttle during peak hours. here there are no limits - your hardware, your rules. NVIDIA's RTX 5080 at $1,000+ lost to this box by 3x on a real AI workload. a discrete GPU beaten by a mini PC the size of a lunchbox. electricity - $9 a month. pays for itself in 10 months. after that $440 every month stays in your pocket. the cloud made sense when nothing on a desk could compete. now it can.

Ventry

27,178 görüntüleme • 25 gün önce

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.
2:02

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 görüntüleme • 1 ay önce

TWO BOXES THE SIZE OF A MAC MINI JUST RAN A 235 BILLION PARAMETER MODEL ON A DESK It is two NVIDIA DGX Spark units linked by a single cable. A year ago a model this size meant renting a GPU cluster by the hour. Now it sits next to your monitor for around $8,000. Here is the twist most people miss. Linking them does not create one shared 256GB memory pool. The model is split across both boxes, and that is the only reason a 235B model fits at all. It answers at roughly 10 tokens per second, and both chips sit at just 74 degrees while sipping around 50 watts. Every token stays on the desk. Nothing touches a cloud, and nothing leaves the room. The ceiling for what you can run at home just jumped from 70B to 235B. Bookmark this & Watch it run ↓
1:21

TWO BOXES THE SIZE OF A MAC MINI JUST RAN A 235 BILLION PARAMETER MODEL ON A DESK It is two NVIDIA DGX Spark units linked by a single cable. A year ago a model this size meant renting a GPU cluster by the hour. Now it sits next to your monitor for around $8,000. Here is the twist most people miss. Linking them does not create one shared 256GB memory pool. The model is split across both boxes, and that is the only reason a 235B model fits at all. It answers at roughly 10 tokens per second, and both chips sit at just 74 degrees while sipping around 50 watts. Every token stays on the desk. Nothing touches a cloud, and nothing leaves the room. The ceiling for what you can run at home just jumped from 70B to 235B. Bookmark this & Watch it run ↓

slash1s

101,051 görüntüleme • 25 gün önce

This guy runs an Nvidia Spark from his desk and pulls in $8,000 a month The device is the size of a small speaker and sits right on his desk. Inside is a full Nvidia GPU running AI workloads 24/7. He rents out the compute power to companies that need local AI processing. They pay him a flat monthly fee to use it remotely. $8,000 lands in his account every single month. He just plugged it in and that was it. The $4,000 box paid for itself in two weeks. Now it prints $96,000 a year from a device smaller than a coffee machine. Companies are desperate for local compute and regular people are quietly supplying it. Save this before everyone figures out what he already knows.
0:42

This guy runs an Nvidia Spark from his desk and pulls in $8,000 a month The device is the size of a small speaker and sits right on his desk. Inside is a full Nvidia GPU running AI workloads 24/7. He rents out the compute power to companies that need local AI processing. They pay him a flat monthly fee to use it remotely. $8,000 lands in his account every single month. He just plugged it in and that was it. The $4,000 box paid for itself in two weeks. Now it prints $96,000 a year from a device smaller than a coffee machine. Companies are desperate for local compute and regular people are quietly supplying it. Save this before everyone figures out what he already knows.

winkle.

57,854 görüntüleme • 1 ay önce

THIS VETERAN DEVELOPER PUT 2 MAC MINIS ON HIS DESK FOR $1,198 AND TURNED HERMES INTO A LOCAL AI WORKSPACE THAT DOESN’T NEED A $210/MONTH AGENT STACK he is not flexing hardware. he is showing the part most people still miss: once Hermes runs locally, the laptop stops being a chat window and starts acting like a workspace with memory, tools and saved skills two Mac minis, one local setup, zero cloud agent dashboard. research tasks, summaries, client notes and workflow steps stay on his machine instead of disappearing every time a session ends most people keep paying for Claude, wrappers, API calls and automation tools just to repeat the same instructions every day. Hermes cuts the waste by saving the process once and pulling it back when the same job returns the math is ugly for cloud tools. $210/month turns into $2,520/year before you even count extra tokens. his version is hardware once, local workflows after, and no panic when rate limits hit the quiet winners will not be the people with the cleanest chatbot tab. it will be the people who own the machine, the memory and the workflow before everyone else realizes that is the product
2:51

THIS VETERAN DEVELOPER PUT 2 MAC MINIS ON HIS DESK FOR $1,198 AND TURNED HERMES INTO A LOCAL AI WORKSPACE THAT DOESN’T NEED A $210/MONTH AGENT STACK he is not flexing hardware. he is showing the part most people still miss: once Hermes runs locally, the laptop stops being a chat window and starts acting like a workspace with memory, tools and saved skills two Mac minis, one local setup, zero cloud agent dashboard. research tasks, summaries, client notes and workflow steps stay on his machine instead of disappearing every time a session ends most people keep paying for Claude, wrappers, API calls and automation tools just to repeat the same instructions every day. Hermes cuts the waste by saving the process once and pulling it back when the same job returns the math is ugly for cloud tools. $210/month turns into $2,520/year before you even count extra tokens. his version is hardware once, local workflows after, and no panic when rate limits hit the quiet winners will not be the people with the cleanest chatbot tab. it will be the people who own the machine, the memory and the workflow before everyone else realizes that is the product

Gipp 🦅

60,173 görüntüleme • 1 ay önce

A guy put an XFRA box outside his house and now gets paid $2,500 a month for doing nothing The box is about the size of an AC unit and mounts right on the wall. Inside it connects your home to a distributed AI compute network. Companies pay to run their AI workloads through it around the clock. He just plugged it in and that was it. Now $2,500 lands in his account every single month. That is $30,000 a year from a box he never touches. It is not Nvidia. It is not a government program. It is XFRA and regular homes are quietly becoming AI infrastructure. Every house a node. Every node earning. The people who move first lock in the best rates. Save this before your neighborhood figures it out before you.
0:33

A guy put an XFRA box outside his house and now gets paid $2,500 a month for doing nothing The box is about the size of an AC unit and mounts right on the wall. Inside it connects your home to a distributed AI compute network. Companies pay to run their AI workloads through it around the clock. He just plugged it in and that was it. Now $2,500 lands in his account every single month. That is $30,000 a year from a box he never touches. It is not Nvidia. It is not a government program. It is XFRA and regular homes are quietly becoming AI infrastructure. Every house a node. Every node earning. The people who move first lock in the best rates. Save this before your neighborhood figures it out before you.

winkle.

16,624 görüntüleme • 1 ay önce

This girl built a $24/year AI visual setup while most creators are still overpaying $1,800 to $4,080 a year for APIs, cloud rendering and visual tools. And no, this isn’t a pre-render or a paid filter hiding in the cloud. It’s live on her laptop right now. The phone camera feeds into TouchDesigner, the movement gets turned into particles, and the whole visual reacts in real time. Most people build this the expensive way. $50 to $180/month in API usage, $40 to $100/month for cloud rendering, another $60/month for visual software, then a “small” creative workflow quietly turns into a $3,000+/year habit. Her stack cuts through almost all of that. The phone becomes the input. TouchDesigner handles the visual layer. Ollama runs the local model. The laptop does the loop without sending every experiment to someone else’s server. No cloud GPU. No API meter. No subscription chain. No project that dies when the billing page fails. Just a normal machine, a camera feed, a node graph and a local model running visuals people still assume need a paid stack behind them. The numbers are the insane part. $0 for Ollama. $0 for TouchDesigner non-commercial. 8GB+ RAM is enough to start. Around $2/month if you count electricity. That’s $24/year instead of a stack that can hit $4,080/year. The effect looks like something a studio would sell. The setup is just one girl refusing to rent the whole process every month.
0:21

This girl built a $24/year AI visual setup while most creators are still overpaying $1,800 to $4,080 a year for APIs, cloud rendering and visual tools. And no, this isn’t a pre-render or a paid filter hiding in the cloud. It’s live on her laptop right now. The phone camera feeds into TouchDesigner, the movement gets turned into particles, and the whole visual reacts in real time. Most people build this the expensive way. $50 to $180/month in API usage, $40 to $100/month for cloud rendering, another $60/month for visual software, then a “small” creative workflow quietly turns into a $3,000+/year habit. Her stack cuts through almost all of that. The phone becomes the input. TouchDesigner handles the visual layer. Ollama runs the local model. The laptop does the loop without sending every experiment to someone else’s server. No cloud GPU. No API meter. No subscription chain. No project that dies when the billing page fails. Just a normal machine, a camera feed, a node graph and a local model running visuals people still assume need a paid stack behind them. The numbers are the insane part. $0 for Ollama. $0 for TouchDesigner non-commercial. 8GB+ RAM is enough to start. Around $2/month if you count electricity. That’s $24/year instead of a stack that can hit $4,080/year. The effect looks like something a studio would sell. The setup is just one girl refusing to rent the whole process every month.

Gipp 🦅

23,556 görüntüleme • 1 ay önce

a guy put a local AI coding stack on an Android phone Termux on the screen Ollama underneath a local model answering from the device that tiny terminal is the shift. two years ago, local LLMs felt like punishment fans screaming bad models one token per second cloud looked inevitable now the same idea runs almost anywhere. - Raspberry Pi for tiny chat - MacBook Air for daily writing - RTX 3090 for serious inference - LM Studio when you want the clean GUI - Ollama when you want apps to connect the point is not replacing GPT-5.1 or Opus 4.8 on the hardest tasks. the point is owning the boring 80%: - drafts - summaries - code help - private docs - offline workflows no token anxiety no rate-limit ceiling no sensitive files leaving the machine cloud AI became the premium layer. local AI became the workbench.
0:26

a guy put a local AI coding stack on an Android phone Termux on the screen Ollama underneath a local model answering from the device that tiny terminal is the shift. two years ago, local LLMs felt like punishment fans screaming bad models one token per second cloud looked inevitable now the same idea runs almost anywhere. - Raspberry Pi for tiny chat - MacBook Air for daily writing - RTX 3090 for serious inference - LM Studio when you want the clean GUI - Ollama when you want apps to connect the point is not replacing GPT-5.1 or Opus 4.8 on the hardest tasks. the point is owning the boring 80%: - drafts - summaries - code help - private docs - offline workflows no token anxiety no rate-limit ceiling no sensitive files leaving the machine cloud AI became the premium layer. local AI became the workbench.

Myttle

27,108 görüntüleme • 1 ay önce

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.
0:50

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

3,093,344 görüntüleme • 25 gün önce

people think running AI locally requires: → $3,000 MacBook Pro → RTX 4090 → $20/month cloud subscription nvidia just dropped a $249 computer. 67 TOPS. runs llama 3.1-8B locally. no internet. no API. no monthly fee. ever. smaller than your router. costs the same as AirPods. runs the same models you pay $240/year to access via ChatGPT. the local AI era just got a price tag. $249.
5:54

people think running AI locally requires: → $3,000 MacBook Pro → RTX 4090 → $20/month cloud subscription nvidia just dropped a $249 computer. 67 TOPS. runs llama 3.1-8B locally. no internet. no API. no monthly fee. ever. smaller than your router. costs the same as AirPods. runs the same models you pay $240/year to access via ChatGPT. the local AI era just got a price tag. $249.

self.dll

321,125 görüntüleme • 1 ay önce