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Nvidia will now let regular operators replace $25,200/year of cloud GPUs with two boxes on a desk DGX Spark just shipped, and the math is brutal: → One box ($2,999) runs the same workload AWS charges $1,900/month for → Two boxes ($3,248 total) replace ChatGPT, Claude, Cursor, OpenAI API...

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ZEUS⚡️

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14,199 Aufrufe • vor 15 Tagen •via X (Twitter)

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This guy turned a $250K cloud bill into a $2,999 Nvidia box on his desk. One box. One-time charge. Same size as a Mac mini. Runs the workload AWS was charging him $1,900/month for. He made $22,000 in a year just from the bill he stopped paying. The kicker: it speaks OpenAI-compatible API out of the box. Migrating his existing stack was 1 line of code. - No more cloud GPUs. - No more monthly meter. - No more "let me check the AWS dashboard before I run that." Full build, exact math, and what the box actually runs is in the article below 👇
2:04

This guy turned a $250K cloud bill into a $2,999 Nvidia box on his desk. One box. One-time charge. Same size as a Mac mini. Runs the workload AWS was charging him $1,900/month for. He made $22,000 in a year just from the bill he stopped paying. The kicker: it speaks OpenAI-compatible API out of the box. Migrating his existing stack was 1 line of code. - No more cloud GPUs. - No more monthly meter. - No more "let me check the AWS dashboard before I run that." Full build, exact math, and what the box actually runs is in the article below 👇

ZEUS⚡️

13,528 Aufrufe • vor 15 Tagen

people burn $1,900 a month on cloud, this $2,999 NVIDIA box runs 5 AI models at once and costs $10 to power 128GB of unified memory, stack two together and you double it, enough to run models a $2,000 graphics card cannot even open in the demo it ran Qwen3 Vision, Nemotron, a voice model, text to speech and speech to text all at once, and only used half the box here is the math that makes people switch: > cloud GPUs were costing $1,900 every single month, billed forever > the box is $2,999 one time, then about $10 of electricity a month > it paid for itself in two months, $22,000 stayed in the business in year one > zero data ever leaves your desk, no rental, no rate limits most people are still renting what this owns outright the full breakdown of the box and the exact math is in the article below
1:04

people burn $1,900 a month on cloud, this $2,999 NVIDIA box runs 5 AI models at once and costs $10 to power 128GB of unified memory, stack two together and you double it, enough to run models a $2,000 graphics card cannot even open in the demo it ran Qwen3 Vision, Nemotron, a voice model, text to speech and speech to text all at once, and only used half the box here is the math that makes people switch: > cloud GPUs were costing $1,900 every single month, billed forever > the box is $2,999 one time, then about $10 of electricity a month > it paid for itself in two months, $22,000 stayed in the business in year one > zero data ever leaves your desk, no rental, no rate limits most people are still renting what this owns outright the full breakdown of the box and the exact math is in the article below

John Doe

30,995 Aufrufe • vor 15 Tagen

NVIDIA will cut your power bill if you let them bolt a $1,000,000 data center to your house. Or spend $2,999 and make $22,000. Option 1, they bolt 16 Blackwell chips to the outside of your house. You get free internet, battery backup, cheaper electricity. They get a million-dollar asset running 24/7 on your property, serving requests for Amazon, Microsoft, Google. Option 2, you buy a DGX Spark for $2,999. Size of a paperback. Sits on your desk. 128GB memory. Runs a 70B model locally. $10/month in electricity. No data leaves the room. Paid for itself in 6 weeks for someone spending $1,900/month on cloud GPU rentals. $22,000 back in year one. One option, you're the infrastructure. Other option, you own it. Same NVIDIA chips. Very different contracts.
1:21

NVIDIA will cut your power bill if you let them bolt a $1,000,000 data center to your house. Or spend $2,999 and make $22,000. Option 1, they bolt 16 Blackwell chips to the outside of your house. You get free internet, battery backup, cheaper electricity. They get a million-dollar asset running 24/7 on your property, serving requests for Amazon, Microsoft, Google. Option 2, you buy a DGX Spark for $2,999. Size of a paperback. Sits on your desk. 128GB memory. Runs a 70B model locally. $10/month in electricity. No data leaves the room. Paid for itself in 6 weeks for someone spending $1,900/month on cloud GPU rentals. $22,000 back in year one. One option, you're the infrastructure. Other option, you own it. Same NVIDIA chips. Very different contracts.

Superior

386,382 Aufrufe • vor 16 Tagen

NVIDIA will refund your cloud GPU bill if you let them bolt a $250,000 AI supercomputer to your desk for $2,999. NVIDIA's $2,999 DGX Spark puts a 128GB AI supercomputer on your desk runs the same 70B models you've been renting in the cloud, but nothing crosses your network and no ToS governs a machine you own. the $22K/year savings everyone's posting is the loud number. the quiet one is bigger: the contracts you stopped losing on data residency. own-compute isn't a budget optimization anymore. it's a sales motion. ↓ builder benchmark from the week the box shipped. real numbers, not marketing.
1:14

NVIDIA will refund your cloud GPU bill if you let them bolt a $250,000 AI supercomputer to your desk for $2,999. NVIDIA's $2,999 DGX Spark puts a 128GB AI supercomputer on your desk runs the same 70B models you've been renting in the cloud, but nothing crosses your network and no ToS governs a machine you own. the $22K/year savings everyone's posting is the loud number. the quiet one is bigger: the contracts you stopped losing on data residency. own-compute isn't a budget optimization anymore. it's a sales motion. ↓ builder benchmark from the week the box shipped. real numbers, not marketing.

dunik

817,961 Aufrufe • vor 16 Tagen

NVIDIA will refund your cloud bill if you let them put a $250,000 supercomputer on your desk for $2,999 The $2,999 DGX Spark puts a 128GB AI supercomputer on your desk the same 70B models you rented in the cloud, but nothing goes over the network and no ToS governs the machine you own $22,000 in annual savings is the big number everyone posts The quiet number is even bigger it’s the contracts you’ve stopped losing due to data residency Owning your own compute is no longer just budget optimization it’s a way to sell to customers who can’t send their data to the cloud
0:58

NVIDIA will refund your cloud bill if you let them put a $250,000 supercomputer on your desk for $2,999 The $2,999 DGX Spark puts a 128GB AI supercomputer on your desk the same 70B models you rented in the cloud, but nothing goes over the network and no ToS governs the machine you own $22,000 in annual savings is the big number everyone posts The quiet number is even bigger it’s the contracts you’ve stopped losing due to data residency Owning your own compute is no longer just budget optimization it’s a way to sell to customers who can’t send their data to the cloud

Asteri

143,167 Aufrufe • vor 12 Tagen

JENSEN HUANG UNVEILED A $3,000 SUPERCOMPUTER THAT REPLACES $1,900/MONTH IN CLOUD GPUS WITH $2 IN ELECTRICITY. IT FITS ON YOUR DESK a developer was paying $1,900 a month renting a100 and h100 cloud gpus. he bought the dgx spark, set it up in an afternoon, and his monthly bill dropped from $1,900 to $2 in electricity the box runs 128gb of unified memory and 1 petaflop of ai compute. a 4090 gives you 24gb of vram and taps out at 30b models. the spark loads 70b at full precision and stretches to 200b without breaking a sweat break-even hit in 6 weeks. after that the $1,890 he used to wire to a rental company every month stayed in his business. first year that's $22,000 back install ollama, change one line in your code, point it at localhost instead of the cloud. nothing else changes. nothing leaves your machine and nothing costs money per request cloud gpu costs aren't getting cheaper and rate limits keep getting tighter. the people who pulled their ai workloads onto a box on their desk in 2026 are going to look very far ahead in 2028 bookmark this and read the article below
0:33

JENSEN HUANG UNVEILED A $3,000 SUPERCOMPUTER THAT REPLACES $1,900/MONTH IN CLOUD GPUS WITH $2 IN ELECTRICITY. IT FITS ON YOUR DESK a developer was paying $1,900 a month renting a100 and h100 cloud gpus. he bought the dgx spark, set it up in an afternoon, and his monthly bill dropped from $1,900 to $2 in electricity the box runs 128gb of unified memory and 1 petaflop of ai compute. a 4090 gives you 24gb of vram and taps out at 30b models. the spark loads 70b at full precision and stretches to 200b without breaking a sweat break-even hit in 6 weeks. after that the $1,890 he used to wire to a rental company every month stayed in his business. first year that's $22,000 back install ollama, change one line in your code, point it at localhost instead of the cloud. nothing else changes. nothing leaves your machine and nothing costs money per request cloud gpu costs aren't getting cheaper and rate limits keep getting tighter. the people who pulled their ai workloads onto a box on their desk in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

140,989 Aufrufe • vor 15 Tagen

This Chinese developer linked two $2,999 NVIDIA DGX Sparks into one box and runs the full Qwen3-235B at home, after dropping his $1,999-a-month cloud bill to zero. He wired 2 small boxes into a single computer, split a giant 235-billion-parameter model in half between them, and serves it across his own network at about 10 tokens a second, with no internet, no cloud, right there on the desk. No data center, no thousand-dollar graphics cards, no monthly cloud bill. Just him, 2 gold boxes the size of a sandwich, one cable between them, and 1 power strip. And here is the whole payoff. He used to pay the cloud $1,999 a month for the same model, and the meter ticked on every request. Now he paid $5,998 once for 2 boxes, they covered their cost in 3 months, and after that he sends as many requests as he wants for free, only electricity. The two Sparks talk over one fast cable, each holds 128GB of memory, and together they carry the whole model, about 73GB loaded per box, with the chip inside pinned near the limit at 96%. Both boxes work as one and keep trading data over the cable, with no cloud in the loop and no single word leaking out. The ready model sits on one local address, and any app on his network calls it as easily as ChatGPT. And here is how he described, in plain words, what this pair of boxes does: "this is a pair of boxes that holds the huge Qwen3-235B model and serves it to one network. the model is split in half, and each box owns its half. parts: // Box 1 (holds the first half of the model and starts the answer fast, the first word appears in under a second) // Box 2 (holds the second half and writes out the rest, about 10 tokens a second) // Cable (connects the 2 boxes and moves data between them on every step, with no lag) // Address (one local address where any app sends its request, like to a cloud model) // Test (a script that runs big prompts through and measures speed and delays) // Monitor (checks temperature, power draw, and load on both boxes every 2 seconds). the model never goes to the cloud. he only steps in when a box runs hotter than 80 degrees or the cable between them starts dropping data." So the system knows exactly what it is, what it is for, and where its limits are. It knows it has to hold the whole huge model across 2 boxes on its own. It knows it has to answer every request locally, with no meter, no limits, and no internet. It knows the human is only needed when a box overheats or the link between them stalls. → The setup runs around the clock on 2 boxes, each pulling under 60 watts → However many requests he sends, the monthly bill is $0, only electricity → The first box starts the answer in under a second → The second writes text at about 10 tokens a second → One request at a time: 838 tokens in 85 seconds, first word in 0.8s → Two requests at once: 697 tokens in 108 seconds, first word in 0.7s → Both boxes sit at 96% load and warm up to 76-78 degrees And only when a chip in a box runs hotter than 80 degrees or the cable between the 2 Sparks drops data does the system call the owner. And when he himself is out on a run or in a coffee shop, he still reaches his own model at home from his phone: sends a big prompt to the local Qwen3-235B, gets the full answer back in under a minute and a half, with no token meter ticking and no limit to hit. Here is what the test shows on his screen during one of the night runs: "one request at a time: 838 tokens in 84.9 seconds, first word in 0.8s, then 0.1s per token." "two requests at once: 697 tokens in 107.6 seconds, first word in 0.7s, then 0.15s per token." "Box 1: chip at 96% load, 76 degrees, 56 watts, 73GB used in memory." "Box 2: chip at 96% load, 78 degrees, 56 watts, the Qwen3-235B model fully loaded." And while everyone around is paying for AI by the month and bumping into limits, his top-tier model just sits on the desk and works as much as he wants: his own little power plant instead of a forever meter. He has no server rack of his own and no cloud account behind it. Just 2 DGX Spark boxes on a desk, one model split in half between them, one local address, and a folder of prompts next to it. Out of everything I have seen this year, this is the cleanest way to stop paying for AI: $5,998 of hardware on the desk once, $0 a month to the cloud, unlimited forever, and between them 2 gold boxes, 1 cable, and the full Qwen3-235B answering at home with no internet.
1:16

This Chinese developer linked two $2,999 NVIDIA DGX Sparks into one box and runs the full Qwen3-235B at home, after dropping his $1,999-a-month cloud bill to zero. He wired 2 small boxes into a single computer, split a giant 235-billion-parameter model in half between them, and serves it across his own network at about 10 tokens a second, with no internet, no cloud, right there on the desk. No data center, no thousand-dollar graphics cards, no monthly cloud bill. Just him, 2 gold boxes the size of a sandwich, one cable between them, and 1 power strip. And here is the whole payoff. He used to pay the cloud $1,999 a month for the same model, and the meter ticked on every request. Now he paid $5,998 once for 2 boxes, they covered their cost in 3 months, and after that he sends as many requests as he wants for free, only electricity. The two Sparks talk over one fast cable, each holds 128GB of memory, and together they carry the whole model, about 73GB loaded per box, with the chip inside pinned near the limit at 96%. Both boxes work as one and keep trading data over the cable, with no cloud in the loop and no single word leaking out. The ready model sits on one local address, and any app on his network calls it as easily as ChatGPT. And here is how he described, in plain words, what this pair of boxes does: "this is a pair of boxes that holds the huge Qwen3-235B model and serves it to one network. the model is split in half, and each box owns its half. parts: // Box 1 (holds the first half of the model and starts the answer fast, the first word appears in under a second) // Box 2 (holds the second half and writes out the rest, about 10 tokens a second) // Cable (connects the 2 boxes and moves data between them on every step, with no lag) // Address (one local address where any app sends its request, like to a cloud model) // Test (a script that runs big prompts through and measures speed and delays) // Monitor (checks temperature, power draw, and load on both boxes every 2 seconds). the model never goes to the cloud. he only steps in when a box runs hotter than 80 degrees or the cable between them starts dropping data." So the system knows exactly what it is, what it is for, and where its limits are. It knows it has to hold the whole huge model across 2 boxes on its own. It knows it has to answer every request locally, with no meter, no limits, and no internet. It knows the human is only needed when a box overheats or the link between them stalls. → The setup runs around the clock on 2 boxes, each pulling under 60 watts → However many requests he sends, the monthly bill is $0, only electricity → The first box starts the answer in under a second → The second writes text at about 10 tokens a second → One request at a time: 838 tokens in 85 seconds, first word in 0.8s → Two requests at once: 697 tokens in 108 seconds, first word in 0.7s → Both boxes sit at 96% load and warm up to 76-78 degrees And only when a chip in a box runs hotter than 80 degrees or the cable between the 2 Sparks drops data does the system call the owner. And when he himself is out on a run or in a coffee shop, he still reaches his own model at home from his phone: sends a big prompt to the local Qwen3-235B, gets the full answer back in under a minute and a half, with no token meter ticking and no limit to hit. Here is what the test shows on his screen during one of the night runs: "one request at a time: 838 tokens in 84.9 seconds, first word in 0.8s, then 0.1s per token." "two requests at once: 697 tokens in 107.6 seconds, first word in 0.7s, then 0.15s per token." "Box 1: chip at 96% load, 76 degrees, 56 watts, 73GB used in memory." "Box 2: chip at 96% load, 78 degrees, 56 watts, the Qwen3-235B model fully loaded." And while everyone around is paying for AI by the month and bumping into limits, his top-tier model just sits on the desk and works as much as he wants: his own little power plant instead of a forever meter. He has no server rack of his own and no cloud account behind it. Just 2 DGX Spark boxes on a desk, one model split in half between them, one local address, and a folder of prompts next to it. Out of everything I have seen this year, this is the cleanest way to stop paying for AI: $5,998 of hardware on the desk once, $0 a month to the cloud, unlimited forever, and between them 2 gold boxes, 1 cable, and the full Qwen3-235B answering at home with no internet.

Blaze

93,219 Aufrufe • vor 15 Tagen

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 Aufrufe • vor 15 Tagen

THIS AI DEVELOPER RUNS 120B PARAMETER MODELS LOCALLY AND HIS MACBOOK PRO CAN’T EVEN OPEN THEM he bought a DGX Spark, gave it the model, went back to his desk by end of day the inference was done, tested, committed his team is still waiting for the cloud GPU queue to free up the only thing between them is 128GB of unified memory in a box smaller than your lunch one Spark is a datacenter node, two Sparks is your own cluster bookmark this and send it to whoever is still paying for cloud GPUs
2:54

THIS AI DEVELOPER RUNS 120B PARAMETER MODELS LOCALLY AND HIS MACBOOK PRO CAN’T EVEN OPEN THEM he bought a DGX Spark, gave it the model, went back to his desk by end of day the inference was done, tested, committed his team is still waiting for the cloud GPU queue to free up the only thing between them is 128GB of unified memory in a box smaller than your lunch one Spark is a datacenter node, two Sparks is your own cluster bookmark this and send it to whoever is still paying for cloud GPUs

leopardracer

83,303 Aufrufe • vor 14 Tagen

CHINESE AI TEACHER STACKED 4 NVIDIA DGX SPARK CHIPS INTO A CLUSTER - AND NOW RUNS A BUSINESS ON IT FROM HIS DESK 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else he's running a Qwen 32 35B model with 4 concurrent requests at the same time and the speed barely drops compared to a single request what used to require a $10,000+/month cloud setup now sits on his desk and costs him $40-60/month in electricity 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else the people still paying per token are financing someone else's infrastructure
1:42

CHINESE AI TEACHER STACKED 4 NVIDIA DGX SPARK CHIPS INTO A CLUSTER - AND NOW RUNS A BUSINESS ON IT FROM HIS DESK 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else he's running a Qwen 32 35B model with 4 concurrent requests at the same time and the speed barely drops compared to a single request what used to require a $10,000+/month cloud setup now sits on his desk and costs him $40-60/month in electricity 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else the people still paying per token are financing someone else's infrastructure

Sprytix

50,912 Aufrufe • vor 16 Tagen

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

2,346,200 Aufrufe • vor 19 Tagen

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

1,992,835 Aufrufe • vor 20 Tagen

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

23,238 Aufrufe • vor 14 Tagen

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 🦅

449,038 Aufrufe • vor 12 Tagen

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 kit running llama, mistral, qwen and deepseek from your own table that changes the cost structure. the same automations people casually run through openai for $100-200/month can move the boring work local: summaries, drafts, code checks, document q&a, scraping cleanup, private agents. the board is $249 once, then around $2/month in electricity the setup is almost stupid. install ollama, pull a 7b model, aim your app at localhost. your workflow still talks to an openai-style api, but now the request never leaves the room and the bill does not grow every time a script loops no, it is not replacing claude for the hardest 20%. but it absolutely attacks the expensive background layer people forgot they were paying for every day cloud ai made people rent intelligence by the token. nvidia is quietly showing what happens when that meter sits under your desk instead.
4:08

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 kit running llama, mistral, qwen and deepseek from your own table that changes the cost structure. the same automations people casually run through openai for $100-200/month can move the boring work local: summaries, drafts, code checks, document q&a, scraping cleanup, private agents. the board is $249 once, then around $2/month in electricity the setup is almost stupid. install ollama, pull a 7b model, aim your app at localhost. your workflow still talks to an openai-style api, but now the request never leaves the room and the bill does not grow every time a script loops no, it is not replacing claude for the hardest 20%. but it absolutely attacks the expensive background layer people forgot they were paying for every day cloud ai made people rent intelligence by the token. nvidia is quietly showing what happens when that meter sits under your desk instead.

Gipp 🦅

38,467 Aufrufe • vor 18 Tagen

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

16,535 Aufrufe • vor 10 Tagen

THIS ENGINEER RUNS CLAUDE CODE WITHOUT PAYING ANTHROPIC A SINGLE DOLLAR RTX 5090 under the desk, Qwen 3.5 35B, 140 tokens per second Claude Code pointed at localhost instead of Anthropic’s servers override two environment variables and your API bill drops to zero he built a full-stack Next.js app from scratch to prove it works then showed every bug and limitation other YouTubers hide same setup I run with two GPUs and Qwen 3.6 27B full breakdown ↓
16:33

THIS ENGINEER RUNS CLAUDE CODE WITHOUT PAYING ANTHROPIC A SINGLE DOLLAR RTX 5090 under the desk, Qwen 3.5 35B, 140 tokens per second Claude Code pointed at localhost instead of Anthropic’s servers override two environment variables and your API bill drops to zero he built a full-stack Next.js app from scratch to prove it works then showed every bug and limitation other YouTubers hide same setup I run with two GPUs and Qwen 3.6 27B full breakdown ↓

leopardracer

29,742 Aufrufe • vor 28 Tagen

Want to reduce your Claude Code bill? Claude Code uses smaller models like Haiku for simple tasks. Instead of sending every small request to a paid API, run those tasks locally with Jan and keep heavier work in the cloud. One setup. Lower API usage. More control. Get the latest version at
0:21

Want to reduce your Claude Code bill? Claude Code uses smaller models like Haiku for simple tasks. Instead of sending every small request to a paid API, run those tasks locally with Jan and keep heavier work in the cloud. One setup. Lower API usage. More control. Get the latest version at

👋 Jan

13,085 Aufrufe • vor 4 Monaten

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

2,763,923 Aufrufe • vor 20 Stunden

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
1:06

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

83,770 Aufrufe • vor 21 Stunden