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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...

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Gipp 🦅

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39,609 просмотров • 3 месяцев назад •via X (Twitter)

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THIS GUY OPENED A TINY PC AND SHOWED HOW A $1,800 GMKTEC EVO-X2 TURNS INTO A LOCAL AI SERVER not a cloud dashboard. not another $200/mo subscription. just one box on a desk, upgraded with storage, cleaner airflow, faster networking, and pointed at real local models. the numbers are what make it interesting. Claude Code Max is $200/mo, ChatGPT Pro is $200/mo, Cursor is another $20/mo. that is $5,040/year just to rent tools most people use for coding, research, summaries, and agents. EVO-X2 flips the setup because it has 128GB unified memory, Ryzen AI Max+ 395, and up to 110GB usable as VRAM on Linux. enough room for Qwen3-Coder 30B, Llama 70B, and 200B-class local models when speed is not the bottleneck. so the workflow becomes simple: buy the box once, install Linux, run the model locally, connect the CLI, and let the machine handle jobs from your own network instead of sending every request to the cloud. this is where the real edge starts. overnight code audits, private document search, local RAG, scraping agents, transcription, file cleanup, all running while the token meter stays at zero. frontier models still win for the hardest prompts. but for daily automation, coding, research, and private workflows, local AI is already moving from nerd setup to obvious business math. the next flex won’t be “which AI plan do you pay for?” it’ll be “what’s running under your desk?”
1:09

THIS GUY OPENED A TINY PC AND SHOWED HOW A $1,800 GMKTEC EVO-X2 TURNS INTO A LOCAL AI SERVER not a cloud dashboard. not another $200/mo subscription. just one box on a desk, upgraded with storage, cleaner airflow, faster networking, and pointed at real local models. the numbers are what make it interesting. Claude Code Max is $200/mo, ChatGPT Pro is $200/mo, Cursor is another $20/mo. that is $5,040/year just to rent tools most people use for coding, research, summaries, and agents. EVO-X2 flips the setup because it has 128GB unified memory, Ryzen AI Max+ 395, and up to 110GB usable as VRAM on Linux. enough room for Qwen3-Coder 30B, Llama 70B, and 200B-class local models when speed is not the bottleneck. so the workflow becomes simple: buy the box once, install Linux, run the model locally, connect the CLI, and let the machine handle jobs from your own network instead of sending every request to the cloud. this is where the real edge starts. overnight code audits, private document search, local RAG, scraping agents, transcription, file cleanup, all running while the token meter stays at zero. frontier models still win for the hardest prompts. but for daily automation, coding, research, and private workflows, local AI is already moving from nerd setup to obvious business math. the next flex won’t be “which AI plan do you pay for?” it’ll be “what’s running under your desk?”

Gipp 🦅

13,271 просмотров • 2 месяцев назад

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

24,107 просмотров • 3 месяцев назад

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

2,357,579 просмотров • 3 месяцев назад

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 🦅

458,361 просмотров • 2 месяцев назад

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

87,065 просмотров • 3 месяцев назад

A $170 AI bill in 10 days is not a cloud problem. It is a routing problem. Most devs send every task to the most expensive brain available. summaries small refactors docs scripts log parsing boilerplate That is how the bill gets stupid. The Mac Mini M4 trade is simple: run routine work locally keep private code on your desk pay once for hardware save frontier models for hard calls Local AI does not beat Claude at everything. It does not need to. It only needs to replace the boring prompts that never needed a frontier model in the first place. Cloud AI stays. The default route changes. Full breakdown below ↓
1:01

A $170 AI bill in 10 days is not a cloud problem. It is a routing problem. Most devs send every task to the most expensive brain available. summaries small refactors docs scripts log parsing boilerplate That is how the bill gets stupid. The Mac Mini M4 trade is simple: run routine work locally keep private code on your desk pay once for hardware save frontier models for hard calls Local AI does not beat Claude at everything. It does not need to. It only needs to replace the boring prompts that never needed a frontier model in the first place. Cloud AI stays. The default route changes. Full breakdown below ↓

Fluixo

35,295 просмотров • 15 дней назад

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 просмотров • 3 месяцев назад

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

1,998,810 просмотров • 3 месяцев назад

This guy put an AI data center into a box that fits on a desk It doesn't look like a serious machine It looks like a mini PC you could hide behind a monitor But with enough unified memory it can run local models, answer over your own files, power agents and keep private work off someone else's servers That sounds like a hobby setup until you look at what people are actually renting every month Claude subscription ChatGPT subscription API credits cloud GPU time usage limits private files uploaded into third-party tools For years the deal was simple: if you wanted intelligence, you rented it Now a Mac Mini M4 Pro or a 128GB AMD mini PC can handle a big part of everyday AI work locally Ollama runs the models Open WebUI gives you the chat interface Tailscale lets you reach it from your phone or laptop without exposing it to the open internet The cloud still wins for the hardest tasks But most daily AI work is not frontier research It is reading documents, summarizing files, drafting, coding helpers, search, notes and private workflows Most people will keep paying rent because it is convenient Others are realizing the assistant they rent every month can become hardware they own save this
0:27

This guy put an AI data center into a box that fits on a desk It doesn't look like a serious machine It looks like a mini PC you could hide behind a monitor But with enough unified memory it can run local models, answer over your own files, power agents and keep private work off someone else's servers That sounds like a hobby setup until you look at what people are actually renting every month Claude subscription ChatGPT subscription API credits cloud GPU time usage limits private files uploaded into third-party tools For years the deal was simple: if you wanted intelligence, you rented it Now a Mac Mini M4 Pro or a 128GB AMD mini PC can handle a big part of everyday AI work locally Ollama runs the models Open WebUI gives you the chat interface Tailscale lets you reach it from your phone or laptop without exposing it to the open internet The cloud still wins for the hardest tasks But most daily AI work is not frontier research It is reading documents, summarizing files, drafting, coding helpers, search, notes and private workflows Most people will keep paying rent because it is convenient Others are realizing the assistant they rent every month can become hardware they own save this

Asteri

12,171 просмотров • 2 месяцев назад

A 24-YEAR-OLD CHINESE DEVELOPER FROM HANGZHOU TURNED RTX 4090 / 3090-CLASS GPU RACKS INTO HIS OWN PRIVATE AI CLOUD. HIS $740/MONTH AI BILL DROPPED TO $31 IN ELECTRICITY he got tired of paying for chatgpt, claude, cursor, openai api credits and every “pro” tool that quietly turns into another monthly tax. long context runs, codebase scans, document parsing, agent loops. every workflow ended with a new invoice so he built a local llm rack instead. used server hardware, RTX 4090 / 3090-class GPU boxes, ollama for automation, lm studio for testing models, llama.cpp for heavier local runs. around $6,200 upfront, but after that the cost is mostly power and maintenance now his scripts hit localhost instead of a cloud api. code reviews, private docs, chinese contracts, sql cleanup, support replies and research tasks stay inside the room. no token panic, no rate-limit wall, no sensitive files leaving his own machines the funny part is that he did not replace claude completely. he just stopped using frontier models for dumb volume work. 65% of daily ai tasks do not need the smartest model alive. they need cheap tokens, privacy and a machine that can run all night cloud ai is still the brain. local ai is the engine room. once he separated those two, his monthly ai stack stopped looking like subscriptions and started looking like infrastructure by 2027, owning your own local ai rack will not look extreme. it will look like the moment people realized renting intelligence forever was the expensive option.
1:40

A 24-YEAR-OLD CHINESE DEVELOPER FROM HANGZHOU TURNED RTX 4090 / 3090-CLASS GPU RACKS INTO HIS OWN PRIVATE AI CLOUD. HIS $740/MONTH AI BILL DROPPED TO $31 IN ELECTRICITY he got tired of paying for chatgpt, claude, cursor, openai api credits and every “pro” tool that quietly turns into another monthly tax. long context runs, codebase scans, document parsing, agent loops. every workflow ended with a new invoice so he built a local llm rack instead. used server hardware, RTX 4090 / 3090-class GPU boxes, ollama for automation, lm studio for testing models, llama.cpp for heavier local runs. around $6,200 upfront, but after that the cost is mostly power and maintenance now his scripts hit localhost instead of a cloud api. code reviews, private docs, chinese contracts, sql cleanup, support replies and research tasks stay inside the room. no token panic, no rate-limit wall, no sensitive files leaving his own machines the funny part is that he did not replace claude completely. he just stopped using frontier models for dumb volume work. 65% of daily ai tasks do not need the smartest model alive. they need cheap tokens, privacy and a machine that can run all night cloud ai is still the brain. local ai is the engine room. once he separated those two, his monthly ai stack stopped looking like subscriptions and started looking like infrastructure by 2027, owning your own local ai rack will not look extreme. it will look like the moment people realized renting intelligence forever was the expensive option.

Gipp 🦅

21,320 просмотров • 2 месяцев назад

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

54,445 просмотров • 2 месяцев назад

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 просмотров • 2 месяцев назад

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

194,375 просмотров • 3 месяцев назад

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.
0:24

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

Gipp 🦅

11,334 просмотров • 1 месяц назад

$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.
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$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

27,360 просмотров • 2 месяцев назад

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 просмотров • 2 месяцев назад

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 ↓
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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

101,317 просмотров • 2 месяцев назад

A $599 MAC MINI, A 340 YUAN SCREEN, AND $3 TO $5 OF POWER CAN TURN INTO A PRIVATE AI SERVER THAT CUTS THE DUMBEST PART OF A $150 API BILL 00:03 he shows the whole setup on the desk: tiny mac mini, cheap external display, keyboard, cables, and one silent box small enough to hide under the monitor while running local models all day. developers burn paid tokens on the wrong stuff. code formatting, quick summaries, draft emails, cron checks, log cleanup and tiny reviews can eat most of the bill, even when none of it needs a frontier model. ollama on a mac mini changes the math. qwen, llama, gemma or phi can handle the boring repeat work locally, while claude stays for architecture, production code and the problems that actually need expensive reasoning. one box can serve the whole workflow. open webui turns the mac mini into a private chat interface for your phone, laptop and tablet, with local files staying in your room instead of getting shipped to another cloud tab. monthly savings come from the invisible calls. 1,440 cron checks, 200 small summaries, 80 log reviews and dozens of throwaway prompts can move off paid tokens before they quietly become another $150 invoice. bookmark this before your next API bill looks like rent.
0:27

A $599 MAC MINI, A 340 YUAN SCREEN, AND $3 TO $5 OF POWER CAN TURN INTO A PRIVATE AI SERVER THAT CUTS THE DUMBEST PART OF A $150 API BILL 00:03 he shows the whole setup on the desk: tiny mac mini, cheap external display, keyboard, cables, and one silent box small enough to hide under the monitor while running local models all day. developers burn paid tokens on the wrong stuff. code formatting, quick summaries, draft emails, cron checks, log cleanup and tiny reviews can eat most of the bill, even when none of it needs a frontier model. ollama on a mac mini changes the math. qwen, llama, gemma or phi can handle the boring repeat work locally, while claude stays for architecture, production code and the problems that actually need expensive reasoning. one box can serve the whole workflow. open webui turns the mac mini into a private chat interface for your phone, laptop and tablet, with local files staying in your room instead of getting shipped to another cloud tab. monthly savings come from the invisible calls. 1,440 cron checks, 200 small summaries, 80 log reviews and dozens of throwaway prompts can move off paid tokens before they quietly become another $150 invoice. bookmark this before your next API bill looks like rent.

Gipp 🦅

21,848 просмотров • 1 месяц назад

AMD JUST KILLED THE $200 - A - MONTH AI SUBSCRIPTION - A 65-WATT BOX WITH 128GB OF UNIFIED MEMORY THAT RUNS 70B MODELS LOCALLY. that clip is amd's ryzen ai max+ 395 mini box, held up on stage. the whole pitch is one spec: 128gb of high-speed unified memory. why it matters: cpu, gpu and npu share the same 128gb pool, so a model that used to need a rack of vram just fits. what it actually runs: llama 70b in full precision on the built-in gpu open models up to ~100b parameters, locally, no cloud all of it in a mini pc pulling around 65 watts that's the part that ends the subscription: your prompts, your data, your model, on a box on your desk, not metered by the token. honest line: this runs open models, not gpt-5 or claude opus. for chat, coding help and agents on your own data it's more than enough. for the absolute frontier, the cloud still wins. and it isn't free: the box runs ~$1,500 and up. but that's a few months of a $200 sub, then it's yours forever. no monthly ai bill, no tokens metered, no prompt leaving the room. the uncomfortable part isn't that amd made a fast box. it's that "you need the cloud for ai" was a pricing choice, and unified memory just made it optional. follow RetroChainer for the hardware that turns rent into ownership, and bookmark this before your next ai renewal.
0:41

AMD JUST KILLED THE $200 - A - MONTH AI SUBSCRIPTION - A 65-WATT BOX WITH 128GB OF UNIFIED MEMORY THAT RUNS 70B MODELS LOCALLY. that clip is amd's ryzen ai max+ 395 mini box, held up on stage. the whole pitch is one spec: 128gb of high-speed unified memory. why it matters: cpu, gpu and npu share the same 128gb pool, so a model that used to need a rack of vram just fits. what it actually runs: llama 70b in full precision on the built-in gpu open models up to ~100b parameters, locally, no cloud all of it in a mini pc pulling around 65 watts that's the part that ends the subscription: your prompts, your data, your model, on a box on your desk, not metered by the token. honest line: this runs open models, not gpt-5 or claude opus. for chat, coding help and agents on your own data it's more than enough. for the absolute frontier, the cloud still wins. and it isn't free: the box runs ~$1,500 and up. but that's a few months of a $200 sub, then it's yours forever. no monthly ai bill, no tokens metered, no prompt leaving the room. the uncomfortable part isn't that amd made a fast box. it's that "you need the cloud for ai" was a pricing choice, and unified memory just made it optional. follow RetroChainer for the hardware that turns rent into ownership, and bookmark this before your next ai renewal.

RetroChainer

16,995 просмотров • 18 дней назад

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 просмотров • 2 месяцев назад