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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...

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Gipp 🦅

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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
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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 🦅

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AMD'S NEW MINI PC RUNS CLAUDE CODE OFFLINE FOR $0/MONTH AND REPLACES $200 OPENAI + $200 CLAUDE CODE SUBSCRIPTIONS 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 coder 30b at 40 to 50 tokens per second and llama 3.3 70b at 20 to 25 tokens per second locally. on linux you get 110gb of usable vram out of 128gb setup takes 90 minutes. wipe windows for linux, install ollama with one command, pull the model, point claude code at localhost. same interface, same agent loop, nothing leaves your network a heavy ai user pays $200 for claude code max, $200 for chatgpt pro and $20 for cursor. that's $5,040 a year and the box pays itself off in 5 months after break-even every month is $420 that used to leave your account. three year math is roughly $13,000 not paid to ai companies plus zero rate limits on overnight agents bookmark this and read the article below
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AMD'S NEW MINI PC RUNS CLAUDE CODE OFFLINE FOR $0/MONTH AND REPLACES $200 OPENAI + $200 CLAUDE CODE SUBSCRIPTIONS 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 coder 30b at 40 to 50 tokens per second and llama 3.3 70b at 20 to 25 tokens per second locally. on linux you get 110gb of usable vram out of 128gb setup takes 90 minutes. wipe windows for linux, install ollama with one command, pull the model, point claude code at localhost. same interface, same agent loop, nothing leaves your network a heavy ai user pays $200 for claude code max, $200 for chatgpt pro and $20 for cursor. that's $5,040 a year and the box pays itself off in 5 months after break-even every month is $420 that used to leave your account. three year math is roughly $13,000 not paid to ai companies plus zero rate limits on overnight agents bookmark this and read the article below

starmex

33,516 Aufrufe • vor 1 Monat

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

0xMarioNawfal

50,730 Aufrufe • vor 1 Monat

A $1,800 MINI PC RUNS CLAUDE CODE WITHOUT THE INTERNET it looks like a hardcover book sitting on a shelf, but this is the GMKtec EVO-X2, and it changes the local AI math completely built on AMD's Ryzen AI Max+ 395, with 128GB of unified memory shared between CPU and GPU that shared pool is the entire trick - the model loads once, both chips read from the same address space wipe Windows, install Ubuntu, pull Ollama, point Claude Code at the local endpoint same CLI, same agent loop, nothing leaves your network Qwen3 Coder 30B and Llama 3.3 70B run comfortably, fully usable for daily development work Claude Code Max + ChatGPT Pro = $4,800/year leaving your account the box costs $1,800, electricity runs $9/month break even hits at month five after that you stop rationing tokens - overnight agents, full codebase audits, experiments you'd never start when the meter was running won't replace frontier reasoning for the hardest 5% of problems for everything else, the local stack quietly becomes the default
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A $1,800 MINI PC RUNS CLAUDE CODE WITHOUT THE INTERNET it looks like a hardcover book sitting on a shelf, but this is the GMKtec EVO-X2, and it changes the local AI math completely built on AMD's Ryzen AI Max+ 395, with 128GB of unified memory shared between CPU and GPU that shared pool is the entire trick - the model loads once, both chips read from the same address space wipe Windows, install Ubuntu, pull Ollama, point Claude Code at the local endpoint same CLI, same agent loop, nothing leaves your network Qwen3 Coder 30B and Llama 3.3 70B run comfortably, fully usable for daily development work Claude Code Max + ChatGPT Pro = $4,800/year leaving your account the box costs $1,800, electricity runs $9/month break even hits at month five after that you stop rationing tokens - overnight agents, full codebase audits, experiments you'd never start when the meter was running won't replace frontier reasoning for the hardest 5% of problems for everything else, the local stack quietly becomes the default

wast3

13,321 Aufrufe • vor 1 Monat

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.
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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

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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
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NVIDIA'S BIGGEST COMPETITOR AMD BUILT A BOX THAT RUNS A 235B MODEL AND KILLS $200 OPENAI AND $200 CLAUDE CODE FOR $9 A MONTH amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on a single piece of silicon with 128gb of unified memory the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months ollama installs in one command and claude code points at localhost so nothing leaves the machine and nothing costs per request bookmark this and read the article below

starmex

93,465 Aufrufe • vor 1 Monat

AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below
0:59

AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

starmex

979,627 Aufrufe • vor 1 Monat

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.
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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 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 🦅

39,609 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 18 Tagen

What Lisa Su actually held on stage: A mini PC the size of a lunchbox running Qwen3-235B locally, with no cloud and no discrete GPU Inside: the Ryzen AI Max+ 395, 128GB unified memory, 110GB usable as VRAM on Linux The first x86 chip that handles 200+ billion parameters on a single die AMD claims it beats the RTX 5080 by several times on memory-bound models — because the 5080 simply cannot fit them $1,400 to $2,500 once. cloud bills run $200 to $400 a month It pays for itself in a few months, then costs nothing per request This is not a faster GPU. it is the first real argument that your AI does not belong in someone else's data center
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What Lisa Su actually held on stage: A mini PC the size of a lunchbox running Qwen3-235B locally, with no cloud and no discrete GPU Inside: the Ryzen AI Max+ 395, 128GB unified memory, 110GB usable as VRAM on Linux The first x86 chip that handles 200+ billion parameters on a single die AMD claims it beats the RTX 5080 by several times on memory-bound models — because the 5080 simply cannot fit them $1,400 to $2,500 once. cloud bills run $200 to $400 a month It pays for itself in a few months, then costs nothing per request This is not a faster GPU. it is the first real argument that your AI does not belong in someone else's data center

plutos

1,236,133 Aufrufe • vor 1 Monat

Apple CEO : "Apple just made the Mac Mini small enough to disappear on your desk, but powerful enough to become your AI workstation." The new M4 Mac Mini starts at $599, comes with Apple Silicon, and pushes desktop compute into a box most people could hide behind a monitor. It is the next cheap entry point for local AI, automation, coding, content, and private workflows. Mac Mini + M4 + local models + low power = the new desk setup. Worth more than another $500 productivity course.
5:32

Apple CEO : "Apple just made the Mac Mini small enough to disappear on your desk, but powerful enough to become your AI workstation." The new M4 Mac Mini starts at $599, comes with Apple Silicon, and pushes desktop compute into a box most people could hide behind a monitor. It is the next cheap entry point for local AI, automation, coding, content, and private workflows. Mac Mini + M4 + local models + low power = the new desk setup. Worth more than another $500 productivity course.

Lummox

262,119 Aufrufe • vor 1 Monat

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 🦅

457,929 Aufrufe • vor 2 Monaten

💥AI is no longer confined to the cloud. At Computex, Jack Huynh showcased the AMD Ryzen AI Halo and Ryzen AI Max PRO 400 Series processors. The next wave of innovation will be built, trained, and run locally. As AI evolves from chatbots to intelligent agents, developers and enterprises need the performance, memory capacity, and flexibility to bring these experiences to life directly on the device. ⚡ Ryzen AI Halo enables local AI development with support for models up to 200B parameters, up to 128GB of unified memory, ROCm optimization, and Windows + Linux support. ⚡ Ryzen AI Max PRO 400 Series combines AI, graphics, and compute in a single architecture, delivering workstation-class performance for next-gen Agent Computers and AI PCs. Some of the most impactful AI experiences will happen right at your desk, where latency, privacy, and control matter most. We're just getting started.
1:24

💥AI is no longer confined to the cloud. At Computex, Jack Huynh showcased the AMD Ryzen AI Halo and Ryzen AI Max PRO 400 Series processors. The next wave of innovation will be built, trained, and run locally. As AI evolves from chatbots to intelligent agents, developers and enterprises need the performance, memory capacity, and flexibility to bring these experiences to life directly on the device. ⚡ Ryzen AI Halo enables local AI development with support for models up to 200B parameters, up to 128GB of unified memory, ROCm optimization, and Windows + Linux support. ⚡ Ryzen AI Max PRO 400 Series combines AI, graphics, and compute in a single architecture, delivering workstation-class performance for next-gen Agent Computers and AI PCs. Some of the most impactful AI experiences will happen right at your desk, where latency, privacy, and control matter most. We're just getting started.

AMD

24,315 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 2 Monaten

✨ Personal AI is the next computing platform. AI is shifting from something you access to something you build with, locally, at the edge, and across systems. We’re unlocking new possibilities for developers: • AMD Ryzen AI Halo, a local-first developer system, preorder starting in June, develop AI without limits on your desk • Gorgon Halo with up to 192GB unified memory, supporting 300B+ parameter models locally, run massive models locally We’re excited to partner with clem 🤗🤗, Co-founder and CEO of Hugging Face, to advance open-source AI for Ryzen AI. Our focus is seamless AI, from model to deployment. Cloud, edge, device. One continuum. Multi-agent systems, local inference at scale, open models as infrastructure. This is the next computing era 🚀
4:49

✨ Personal AI is the next computing platform. AI is shifting from something you access to something you build with, locally, at the edge, and across systems. We’re unlocking new possibilities for developers: • AMD Ryzen AI Halo, a local-first developer system, preorder starting in June, develop AI without limits on your desk • Gorgon Halo with up to 192GB unified memory, supporting 300B+ parameter models locally, run massive models locally We’re excited to partner with clem 🤗🤗, Co-founder and CEO of Hugging Face, to advance open-source AI for Ryzen AI. Our focus is seamless AI, from model to deployment. Cloud, edge, device. One continuum. Multi-agent systems, local inference at scale, open models as infrastructure. This is the next computing era 🚀

Jack Huynh

47,822 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 1 Monat

EIGHT USED RTX 3090s. 192GB OF VRAM. ONE PRIVATE BOX BUILT TO RUN 70B AI MODELS WITHOUT RENTING A DATACENTER. 00:06 the team lowers all eight GPUs into the chassis at once, turning a pile of old gaming cards into the core of a serious local AI server. each 3090 contributes 24GB of memory. combined, the rack has enough VRAM for heavyweight models, large document collections, long context and multiple jobs running side by side. these cards are five years old, but AI economics work differently from gaming. memory capacity matters more than having the newest badge, which is why used 3090s remain valuable. the server can keep one model loaded while separate GPUs handle video, images, transcription and overnight agents without sending private company data into the cloud. bookmark this build before yesterday’s gaming GPUs become tomorrow’s cheapest AI infrastructure.
0:29

EIGHT USED RTX 3090s. 192GB OF VRAM. ONE PRIVATE BOX BUILT TO RUN 70B AI MODELS WITHOUT RENTING A DATACENTER. 00:06 the team lowers all eight GPUs into the chassis at once, turning a pile of old gaming cards into the core of a serious local AI server. each 3090 contributes 24GB of memory. combined, the rack has enough VRAM for heavyweight models, large document collections, long context and multiple jobs running side by side. these cards are five years old, but AI economics work differently from gaming. memory capacity matters more than having the newest badge, which is why used 3090s remain valuable. the server can keep one model loaded while separate GPUs handle video, images, transcription and overnight agents without sending private company data into the cloud. bookmark this build before yesterday’s gaming GPUs become tomorrow’s cheapest AI infrastructure.

Gipp 🦅

118,807 Aufrufe • vor 1 Monat

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 1 Monat

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 Aufrufe • vor 1 Monat

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

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

Myttle

27,108 Aufrufe • vor 1 Monat