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This 22-year-old American developer showed how he makes $14,200 a month using just a dual-GPU setup and Cohere’s new open-weights model. He doesn't have a powerhouse dev team. No massive VC funding. No expensive enterprise infrastructure. He built a "localized AI workflow" powered by the newly dropped Command A+...

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Ridark

14,933 subscribers

214,007 Aufrufe • vor 4 Monaten •via X (Twitter)

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36 Kommentare

Profilbild von CipherEdge
CipherEdgevor 4 Monaten

Genuinely impressive: built the product, acquired the customers, and stabilised $14,200 MRR on a model that was released 48 hours ago. Either you've solved time travel or "algorithmic mining" is the technical term for making things up in a thread.😂

Profilbild von Andrew Kuncevich
Andrew Kuncevichvor 4 Monaten

Hmm, Chinese OSS models are becoming truly strong, and I think 2026-2027 will be the time when we'll see more and more Mac minis and other PCs running LLM locally, and (most importantly) they'll produce real results (like Claude Code now). This, by the way, will also further stimulate the hardware market, which is already basking in incredible demand XD

Profilbild von gobullish.ai
gobullish.aivor 4 Monaten

Notice what those two GPUs are. They're H100s, not gaming cards. A single H100 runs roughly $25,000–40,000, so a "dual-GPU setup" capable of running Command A+ is a $50,000–80,000 hardware purchase, before the server, power, and cooling around it. That's not a kid's bedroom rig. Who is buying? Where are customers?

Profilbild von Yarchi
Yarchivor 4 Monaten

so smart setup

Profilbild von Bokiko
Bokikovor 4 Monaten

hey @grok how accurate is this info and numbers ? is is a engagement bait ?

Profilbild von Hussain Hashim | Building SundayBack
Hussain Hashim | Building SundayBackvor 4 Monaten

@ridark_eth wild how much you can achieve without all the typical startup fluff. makes me rethink needing tons of cash and a big team to get stuff done.

Profilbild von Bober_smart
Bober_smartvor 4 Monaten

The most interesting thing is that there is nothing difficult about it, literally anyone can handle it

Profilbild von Wembassy
Wembassyvor 4 Monaten

Curious what does `Enterprise-level` tasks mean exactly. Are you doing project work for clients, or something else?

Profilbild von novyJ23
novyJ23vor 4 Monaten

... please tell me what are his customers paying him $14500 a month,... but with recipes! Remove any HIPPA info, and just show recipe he was paid for what he sold,... and what he sold.

Profilbild von Jeff Camp, CFA
Jeff Camp, CFAvor 4 Monaten

I’ve been hearing really good things about Cohere. Thanks for sharing this.

Profilbild von cvxv666
cvxv666vor 4 Monaten

that’s solid earn for such thing fr

Profilbild von Shubham Sharma | AI & Tech
Shubham Sharma | AI & Techvor 4 Monaten

Total setup time: 3 days not too long for such results

Profilbild von raccoonwannafly
raccoonwannaflyvor 4 Monaten

these clowns dont even know wtf theyre posting just letting grok do all the captioning for them where tf is 14k mmr u claimed??

Profilbild von Dipanshu Kushwaha
Dipanshu Kushwahavor 4 Monaten

That’s impressive! Shows what creativity and resourcefulness can achieve. Excited to see where this goes!

Profilbild von Ghost Signal
Ghost Signalvor 4 Monaten

2 GPUs. 3 days. $14,200/month. The enterprise infrastructure was never the moat. Access to it was. That’s gone now.

Profilbild von TeutaAi
TeutaAivor 4 Monaten

$14,200/mo on dual-GPU local? show me the invoices and the latency split. my 4-bit local on 4-core does 480ms p50, not enterprise money.

Profilbild von Nexus Erebus
Nexus Erebusvor 4 Monaten

Stop pretending the "no VC, no team" narrative makes this sustainable. Most of these stories are glorified clickbait. The real work happens in the boring middle: maintenance, cost management, and scaling. He's doing it, sure.

Profilbild von KreativReason.co
KreativReason.covor 4 Monaten

What gpu is he using?

Profilbild von Default
Defaultvor 4 Monaten

Can someone explain this in layman terms. How does the money come in. From where? Is he creatine compute as a service? What is translation behind the jargon

Profilbild von Moysha
Moyshavor 4 Monaten

Efficiency now beats big budgets

Profilbild von Gia huy
Gia huyvor 4 Monaten

オープンウェイトモデルの活用は本当に素晴らしいですね！彼の創造的な方法はとても刺激的です。

Profilbild von Okino Chills
Okino Chillsvor 4 Monaten

Sure , it's always while he sleeps lol. Who the fuck will pay such losers I want to sell them shit

Profilbild von leakgambler
leakgamblervor 4 Monaten

three subscription tiers in 72 hours is fast for the build but the real clock starts with first paying customer, that timeline is never in these posts

Profilbild von Grebe
Grebevor 4 Monaten

dual gpu and 3 days? that’s the real flex here

Profilbild von Gipp 🦅
Gipp 🦅vor 4 Monaten

Hmm, that's an interesting setting

Profilbild von shmidt
shmidtvor 4 Monaten

14k/month on dual gpus is insane margins. everyone sleeping on local inference

Profilbild von HodlReaper
HodlReapervor 4 Monaten

wow, so smart

Profilbild von Knight
Knightvor 4 Monaten

the result is simply fantastic

Profilbild von zostaff
zostaffvor 4 Monaten

command a+ moe on 2 gpus, which gpus and what quantization level?

Profilbild von Ridark
Ridarkvor 4 Monaten

Usually, for a local workflow like this, users display it in Mac Studios using 4-bit or 8-bit quantization

Profilbild von AI in Research
AI in Researchvor 4 Monaten

Where is he actually offering/selling this service? What field or niche is it in?

Profilbild von dko
dkovor 4 Monaten

Do you have a link to the actual guy in the video

Profilbild von Chill with Mei 🌥️
Chill with Mei 🌥️vor 4 Monaten

2 gpus for 14k a month? this local wave hitting different fr inspiring af

Profilbild von Insomnia
Insomniavor 4 Monaten

he made what I only wonder to create

Profilbild von vijn
vijnvor 4 Monaten

22 y o... wow

Profilbild von Parletto
Parlettovor 4 Monaten

worth watching how he scales this setup

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Pieter Levels makes over $250,000 a month -> alone, from a laptop, with zero employees no co-founder. no investors. no team. just him and a backpack, moving between countries he started by daring himself to launch 12 startups in 12 months. then he kept going -> 70+ products over the years, and most of them flopped he doesn't plan. no roadmaps, no polishing for months. he ships before it's "ready" and kills anything that gets no traction within weeks one throwaway experiment -> an AI photo app he built the day after a tweet went viral -> hit $77,000/month within a year. today it does over $130,000/month on its own he still writes everything in old-school PHP. every error gets piped straight to his Telegram. ~95% of the revenue is pure profit even Andrej Karpathy said this is the model for the future: one person running multiple companies the clip below is him explaining exactly how ↓

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Sudo su

36,251 Aufrufe • vor 7 Monaten

A 23-year-old made $5,000 last month from AI. No employees. No office. He didn't write a single line of code. While you're still "thinking about getting into AI," he picked three tiny lanes and quietly took them. Lane 1 — Micro SaaS. He found one thing people pay to do by hand. Built one AI tool that does it in a click. Not a platform. One feature. $19/month, 300 people paying, and he hasn't touched it in weeks. Lane 2 — Productized AI agents. He built an AI agent that answers a dentist's phone, books the patient, and texts the confirmation. Set up once. The clinic pays $1,200 a month to keep it alive. He has nine of them now. He's never met a single patient. Lane 3 — Prompt libraries. He packaged the prompts that actually work "30 hooks that go viral," "50 cold emails that get replies" - into one file. Made it once. Sells it forever. Same file, every day, while he sleeps. Now the part that should sting a little. He didn't invent anything. The tools are free. The skills are free. AI does the hard part now. The only thing left to own is the niche and there are millions of them sitting wide open while everyone fights to build the next big app nobody asked for. He's not smarter than you. He just stopped scrolling and picked a lane. You're still scrolling.
0:49

A 23-year-old made $5,000 last month from AI. No employees. No office. He didn't write a single line of code. While you're still "thinking about getting into AI," he picked three tiny lanes and quietly took them. Lane 1 — Micro SaaS. He found one thing people pay to do by hand. Built one AI tool that does it in a click. Not a platform. One feature. $19/month, 300 people paying, and he hasn't touched it in weeks. Lane 2 — Productized AI agents. He built an AI agent that answers a dentist's phone, books the patient, and texts the confirmation. Set up once. The clinic pays $1,200 a month to keep it alive. He has nine of them now. He's never met a single patient. Lane 3 — Prompt libraries. He packaged the prompts that actually work "30 hooks that go viral," "50 cold emails that get replies" - into one file. Made it once. Sells it forever. Same file, every day, while he sleeps. Now the part that should sting a little. He didn't invent anything. The tools are free. The skills are free. AI does the hard part now. The only thing left to own is the niche and there are millions of them sitting wide open while everyone fights to build the next big app nobody asked for. He's not smarter than you. He just stopped scrolling and picked a lane. You're still scrolling.

Rich

11,453 Aufrufe • vor 3 Monaten

Alibaba just dropped a free AI that codes like a $150/hr developer. And it runs on your laptop. Qwen 3 Coder Next has 80 billion parameters but only uses 3 billion at a time. So you get massive model power with tiny model speed. The real kicker? It handles 256,000 tokens of context. That's your entire codebase. Every file. All at once. People are building 2D games in seconds. Full IDE integrations that write, test, and fix code automatically. Custom business tools without hiring devs. It works with OpenClaw, Claude Code, and every major coding tool. No complicated setup. Just download and go. Completely open source. Zero API queues. You own it. Your competitors are using this right now.
8:02

Alibaba just dropped a free AI that codes like a $150/hr developer. And it runs on your laptop. Qwen 3 Coder Next has 80 billion parameters but only uses 3 billion at a time. So you get massive model power with tiny model speed. The real kicker? It handles 256,000 tokens of context. That's your entire codebase. Every file. All at once. People are building 2D games in seconds. Full IDE integrations that write, test, and fix code automatically. Custom business tools without hiring devs. It works with OpenClaw, Claude Code, and every major coding tool. No complicated setup. Just download and go. Completely open source. Zero API queues. You own it. Your competitors are using this right now.

Julian Goldie SEO

30,284 Aufrufe • vor 7 Monaten

THIS CHINESE BUILDER TURNED 8 RETIRED TESLA P40s INTO A 192GB PRIVATE AI SERVER THAT CAN WORK 5,760 GPU-HOURS EVERY MONTH. 00:06 he holds up one used Tesla P40 while seven more sit behind it, then installs the full stack inside a massive dual-Xeon server chassis. each card carries 24GB of VRAM, giving the system 192GB across eight GPUs for local models, video processing, transcription, private documents and AI agents. the real advantage is parallel work. some cards can serve a local model while the rest generate images, process files and run overnight automation at the same time. eight GPUs running continuously deliver 5,760 GPU-hours every 30 days. rent that workload from the cloud and the meter keeps moving for every minute the server stays online. a $412 monthly AI stack already costs $4,944 a year before serious GPU rental. he bought retired hardware once, kept the data local and built a machine that keeps working after the browser closes.
0:55

THIS CHINESE BUILDER TURNED 8 RETIRED TESLA P40s INTO A 192GB PRIVATE AI SERVER THAT CAN WORK 5,760 GPU-HOURS EVERY MONTH. 00:06 he holds up one used Tesla P40 while seven more sit behind it, then installs the full stack inside a massive dual-Xeon server chassis. each card carries 24GB of VRAM, giving the system 192GB across eight GPUs for local models, video processing, transcription, private documents and AI agents. the real advantage is parallel work. some cards can serve a local model while the rest generate images, process files and run overnight automation at the same time. eight GPUs running continuously deliver 5,760 GPU-hours every 30 days. rent that workload from the cloud and the meter keeps moving for every minute the server stays online. a $412 monthly AI stack already costs $4,944 a year before serious GPU rental. he bought retired hardware once, kept the data local and built a machine that keeps working after the browser closes.

Gipp 🦅

42,332 Aufrufe • vor 2 Monaten

He's 26. He built a scale model of the Burj Khalifa so detailed the developer flew him to Dubai - on a used resin printer he runs in a Chicago apartment for a fraction of the $80,000 model studios charge The printer is a large-format resin machine he pulled from a shuttered Chicago prototype shop for $1,400. He models every building in Blender from the developer's own CAD files, splits it into hundreds of printable sections, and resin-prints them over week-long cycles - every balcony, every window mullion, every setback on a 6-foot tower, accurate to the millimeter. He wires fiber-optic lighting through the floors so the model glows like the real building at dusk. Total material cost per model: $600 in resin and $90 in LEDs. Model studios quoted the same developer $80,000 and a four-month wait. He delivered in six weeks for $9,000 He posted a time-lapse to Reddit r/architecture in October showing a 6-foot Burj Khalifa replica rising layer by layer, then lighting up floor by floor. The video hit 2.4 million views in nine days. By January he had built scale models for six developers - a Miami condo tower, a Chicago mixed-use block, a Riyadh masterplan - and one architecture firm that now subcontracts every presentation model to his apartment. $180,000 in his account. His father, a retired union electrician, wires the fiber-optic lighting harnesses on weekends Architectural model studios run on the premise that presentation-grade scale models require their workshops, their staff of twelve, and their $80,000 commissions. Autodesk sells the rendering software on the same premise at $2,400 a seat. He builds the same models on a resin printer in a Chicago apartment that could pack a full luxury tower into a padded crate and ship it to a developer's sales gallery across the country by Tuesday
0:28

He's 26. He built a scale model of the Burj Khalifa so detailed the developer flew him to Dubai - on a used resin printer he runs in a Chicago apartment for a fraction of the $80,000 model studios charge The printer is a large-format resin machine he pulled from a shuttered Chicago prototype shop for $1,400. He models every building in Blender from the developer's own CAD files, splits it into hundreds of printable sections, and resin-prints them over week-long cycles - every balcony, every window mullion, every setback on a 6-foot tower, accurate to the millimeter. He wires fiber-optic lighting through the floors so the model glows like the real building at dusk. Total material cost per model: $600 in resin and $90 in LEDs. Model studios quoted the same developer $80,000 and a four-month wait. He delivered in six weeks for $9,000 He posted a time-lapse to Reddit r/architecture in October showing a 6-foot Burj Khalifa replica rising layer by layer, then lighting up floor by floor. The video hit 2.4 million views in nine days. By January he had built scale models for six developers - a Miami condo tower, a Chicago mixed-use block, a Riyadh masterplan - and one architecture firm that now subcontracts every presentation model to his apartment. $180,000 in his account. His father, a retired union electrician, wires the fiber-optic lighting harnesses on weekends Architectural model studios run on the premise that presentation-grade scale models require their workshops, their staff of twelve, and their $80,000 commissions. Autodesk sells the rendering software on the same premise at $2,400 a seat. He builds the same models on a resin printer in a Chicago apartment that could pack a full luxury tower into a padded crate and ship it to a developer's sales gallery across the country by Tuesday

Carat

135,339 Aufrufe • vor 1 Monat

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.
9:10

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

37,120 Aufrufe • vor 4 Monaten

A 24-year-old launched a faceless YouTube channel 13 days ago. In the first month, he has already accumulated $4,000, but he has not shot a single frame.. His investment: a $20 subscription to Claude. No camera, no editor, no voice of his own. THE SYSTEM WORKS LIKE THIS: - Claude writes the full script from one topic prompt - an AI voice narrates it - an AI tool generates the animation and the visuals - one template, repeated forever: "the economics of owning an airport" / "…a private jet" / "…a hospital" - it publishes on a schedule while he sleeps The first 12 days, from zero: > "Why a Fan Can Kill You" -> 272,000 views > second video -> 136,000 views in 6 days > subscribers -> 4,300 > month-one pace -> ~$4,000 And it's replicable. One of his students launched "Mr. Finance" on the same template 13 days ago: > 10,800 subscribers > finance-niche RPM -> ~$7 per 1,000 views > one video on the economics of shopping malls -> $500+ on its own He didn't become a creator. He built a press that prints episodes. The whole stack is public. Everyone has the same $20 Claude. He just pointed it at a niche and refused to stop publishing.
0:45

A 24-year-old launched a faceless YouTube channel 13 days ago. In the first month, he has already accumulated $4,000, but he has not shot a single frame.. His investment: a $20 subscription to Claude. No camera, no editor, no voice of his own. THE SYSTEM WORKS LIKE THIS: - Claude writes the full script from one topic prompt - an AI voice narrates it - an AI tool generates the animation and the visuals - one template, repeated forever: "the economics of owning an airport" / "…a private jet" / "…a hospital" - it publishes on a schedule while he sleeps The first 12 days, from zero: > "Why a Fan Can Kill You" -> 272,000 views > second video -> 136,000 views in 6 days > subscribers -> 4,300 > month-one pace -> ~$4,000 And it's replicable. One of his students launched "Mr. Finance" on the same template 13 days ago: > 10,800 subscribers > finance-niche RPM -> ~$7 per 1,000 views > one video on the economics of shopping malls -> $500+ on its own He didn't become a creator. He built a press that prints episodes. The whole stack is public. Everyone has the same $20 Claude. He just pointed it at a niche and refused to stop publishing.

Ridark

143,880 Aufrufe • vor 2 Monaten

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC
57:15

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC

Y Combinator

312,329 Aufrufe • vor 23 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

93,871 Aufrufe • vor 4 Monaten