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APPLE BUILT A $10,000 COMPUTER THAT RUNS WHAT USED TO REQUIRE AN ENTIRE SERVER ROOM 06:40 he shows the number that breaks everything - one Mac Studio is more powerful than four Dell or AMD machines combined and uses half the energy DeepSeek R1 doesn't run on even two...

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Noisy

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26,305 views • 3 months ago •via X (Twitter)

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APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables
21:59

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables

Noisy

48,763 views • 3 months ago

CHINESE BUSINESSMAN BOUGHT 4 MAC MINIS FOR $1,699 AND CONNECTED THEM TO HIS MACBOOK - NOW THE CORNER OF HIS DESK MAKES MORE THAN MOST OFFICES MacBook Pro 16" runs everything - assigns tasks, controls the flow, holds context - 4 Mac Minis in the corner execute inference in parallel and don't know what downtime means he connected them through EXO - an open source framework that combines multiple Apple Silicon devices into one unified cluster - and together they run models no single device could handle alone while everyone else rents cloud GPUs for $1,900/month he paid $1,699 once and the entire cluster now runs on $20/month in electricity and nothing else clients pay for inference, data never leaves the room and the cluster scales by simply placing one more Mac Mini on top the MacBook is what he works on - the Mac Minis are what work for him around the clock without days off $1,699 invested once - and a corner of a desk that makes more than most companies with a rented office
0:20

CHINESE BUSINESSMAN BOUGHT 4 MAC MINIS FOR $1,699 AND CONNECTED THEM TO HIS MACBOOK - NOW THE CORNER OF HIS DESK MAKES MORE THAN MOST OFFICES MacBook Pro 16" runs everything - assigns tasks, controls the flow, holds context - 4 Mac Minis in the corner execute inference in parallel and don't know what downtime means he connected them through EXO - an open source framework that combines multiple Apple Silicon devices into one unified cluster - and together they run models no single device could handle alone while everyone else rents cloud GPUs for $1,900/month he paid $1,699 once and the entire cluster now runs on $20/month in electricity and nothing else clients pay for inference, data never leaves the room and the cluster scales by simply placing one more Mac Mini on top the MacBook is what he works on - the Mac Minis are what work for him around the clock without days off $1,699 invested once - and a corner of a desk that makes more than most companies with a rented office

Noisy

22,391 views • 4 months ago

THIS DEVELOPER RAN THE LARGEST AI MODEL IN THE WORLD ON 5 MAC STUDIOS - AND IT COST 100X LESS THAN WHAT OPENAI USES 27:47 he says it after hours of setup - Llama 3.1 405B running locally on five Mac Studios - a model that normally requires 42 GPUs at $1,600 each or an entire data center 320GB unified memory combined - every Mac Studio adds its 64GB into one shared pool - and a model that needs 1TB of VRAM just distributes across all five it launched, it responded and not a single token went to OpenAI or Google - everything local, everything private, everything yours and 100x cheaper 5 Mac Studios, 46 watts for all of them in idle - less than one light bulb per machine what OpenAI keeps behind a subscription he ran at home with a one-time purchase in a few clicks
34:56

THIS DEVELOPER RAN THE LARGEST AI MODEL IN THE WORLD ON 5 MAC STUDIOS - AND IT COST 100X LESS THAN WHAT OPENAI USES 27:47 he says it after hours of setup - Llama 3.1 405B running locally on five Mac Studios - a model that normally requires 42 GPUs at $1,600 each or an entire data center 320GB unified memory combined - every Mac Studio adds its 64GB into one shared pool - and a model that needs 1TB of VRAM just distributes across all five it launched, it responded and not a single token went to OpenAI or Google - everything local, everything private, everything yours and 100x cheaper 5 Mac Studios, 46 watts for all of them in idle - less than one light bulb per machine what OpenAI keeps behind a subscription he ran at home with a one-time purchase in a few clicks

Noisy

21,553 views • 3 months ago

A 27B parameter model used to need a server room. Now it runs on: • iPhone • Android • Mac PrismML's Bonsai makes it possible. • 1-bit weights • 27B params in just 3.9GB • ~90% of full precision quality (PrismML evals) • Better than the 2-bit version at less than half the size The entire Bonsai family is now live in RunAnywhere. • 1.7B to 27B models • 1-bit + 2-bit ternary • Thinking mode On iOS and Mac, Bonsai runs through ggml's llama.cpp and Apple's MLX. On Android, Bonsai runs through ggml's llama.cpp and directly on the Qualcomm Hexagon NPU through QHexRT, our proprietary inference runtime. We built custom silicon kernels to make 1-bit inference on an NPU possible for the first time. Two years ago this needed a data center. Now it thinks in airplane mode. Now available in the RunAnywhere app on the App Store and Google Play.
0:27

A 27B parameter model used to need a server room. Now it runs on: • iPhone • Android • Mac PrismML's Bonsai makes it possible. • 1-bit weights • 27B params in just 3.9GB • ~90% of full precision quality (PrismML evals) • Better than the 2-bit version at less than half the size The entire Bonsai family is now live in RunAnywhere. • 1.7B to 27B models • 1-bit + 2-bit ternary • Thinking mode On iOS and Mac, Bonsai runs through ggml's llama.cpp and Apple's MLX. On Android, Bonsai runs through ggml's llama.cpp and directly on the Qualcomm Hexagon NPU through QHexRT, our proprietary inference runtime. We built custom silicon kernels to make 1-bit inference on an NPU possible for the first time. Two years ago this needed a data center. Now it thinks in airplane mode. Now available in the RunAnywhere app on the App Store and Google Play.

RunAnywhere (YC W26)

17,587 views • 2 months ago

THIS CLUSTER OF 4 MAC STUDIOS USES LESS POWER THAN ONE NVIDIA CHIP - AND COSTS 5X LESS 17:30 the number hits hard - 600 watts for all four Mac Studios - less than one H200 that costs $30,000 on its own instead of one $30,000 GPU you get 4 machines with 1.5TB of memory, 3x more performance and models that simply cannot run on a single chip Apple quietly added RDMA over Thunderbolt in a beta update - latency dropped 99% and the same cluster became 3x faster without buying any new hardware before the update: 5 tokens per second. after: 15.5 - a free upgrade through a software update 8 Nvidia DGX Sparks for the same memory would cost $32,000 - this cluster costs a fraction and gets faster with every update 10x more productive at half the price - and the only thing you need is knowing where to look
20:03

THIS CLUSTER OF 4 MAC STUDIOS USES LESS POWER THAN ONE NVIDIA CHIP - AND COSTS 5X LESS 17:30 the number hits hard - 600 watts for all four Mac Studios - less than one H200 that costs $30,000 on its own instead of one $30,000 GPU you get 4 machines with 1.5TB of memory, 3x more performance and models that simply cannot run on a single chip Apple quietly added RDMA over Thunderbolt in a beta update - latency dropped 99% and the same cluster became 3x faster without buying any new hardware before the update: 5 tokens per second. after: 15.5 - a free upgrade through a software update 8 Nvidia DGX Sparks for the same memory would cost $32,000 - this cluster costs a fraction and gets faster with every update 10x more productive at half the price - and the only thing you need is knowing where to look

Noisy

106,054 views • 3 months ago

Apple is no longer hiding it. These machines are built for local AI. Apple unveiled the new Mac Mini with M6 their first 2nm chip. Starting at $899. What you can run on the $899 Mac Mini (32GB) 👇 → Qwen 3.6 27B : fits comfortably, near-GPT-4 quality → Qwen 3 Coder 32B : full coding assistant on your desk → Llama 4 8B at full precision → DeepSeek R1 32B for reasoning tasks → 13.5x faster LLM processing than M1. 4x faster than M4. What you can run on the Mac Studio with M5 Ultra (512GB) 👇 → Llama 4 70B at high quality quantization → DeepSeek V4 with hundreds of billions of parameters → Frontier-scale models entirely in local memory → Connect four Mac Studios over Thunderbolt 5 cluster them to run trillion-parameter models locally Apple said their Mac business grew 30% last quarter largely because people are buying these to run AI models on their own desks instead of paying for cloud tokens. The future of inference is local. Apple built the hardware for it.
0:38

Apple is no longer hiding it. These machines are built for local AI. Apple unveiled the new Mac Mini with M6 their first 2nm chip. Starting at $899. What you can run on the $899 Mac Mini (32GB) 👇 → Qwen 3.6 27B : fits comfortably, near-GPT-4 quality → Qwen 3 Coder 32B : full coding assistant on your desk → Llama 4 8B at full precision → DeepSeek R1 32B for reasoning tasks → 13.5x faster LLM processing than M1. 4x faster than M4. What you can run on the Mac Studio with M5 Ultra (512GB) 👇 → Llama 4 70B at high quality quantization → DeepSeek V4 with hundreds of billions of parameters → Frontier-scale models entirely in local memory → Connect four Mac Studios over Thunderbolt 5 cluster them to run trillion-parameter models locally Apple said their Mac business grew 30% last quarter largely because people are buying these to run AI models on their own desks instead of paying for cloud tokens. The future of inference is local. Apple built the hardware for it.

Vaibhav Sisinty

517,215 views • 1 month ago

PERPLEXITY 🔥: A new Hybrid mode is coming soon on Mac, letting local models handle some subtasks. > Gemma 4 (for 16 GB machines), Qwen3.6, and Perplexity's own model (For 32 GB machines) are currently supported. > "In Hybrid mode, Computer orchestrates in the cloud and delegates suitable subtasks to a model on your Mac." > "Local models run on your Mac's own memory and processor — the more capable your machine, the larger the models it can run." > "Privacy Gate is a small model that runs entirely on your Mac. It inspects anything headed to the cloud for personal or sensitive information — and if it finds any, you decide how to handle it before anything is sent." That would be huge 👀
0:21

PERPLEXITY 🔥: A new Hybrid mode is coming soon on Mac, letting local models handle some subtasks. > Gemma 4 (for 16 GB machines), Qwen3.6, and Perplexity's own model (For 32 GB machines) are currently supported. > "In Hybrid mode, Computer orchestrates in the cloud and delegates suitable subtasks to a model on your Mac." > "Local models run on your Mac's own memory and processor — the more capable your machine, the larger the models it can run." > "Privacy Gate is a small model that runs entirely on your Mac. It inspects anything headed to the cloud for personal or sensitive information — and if it finds any, you decide how to handle it before anything is sent." That would be huge 👀

🚨 AI News | TestingCatalog

41,281 views • 1 month ago

You can run local AI models up to 120B without a $10,000 Mac Studio This phone-sized device is your own server for open-source models. 100% local and private. And you can use it to: - Power an agent like OpenClaw 24/7 - Completely replace a chatbot - Literally anything that requires an API It’s called Tiiny, and, once again, you can run the latest open-source models on it. Link below
16:10

You can run local AI models up to 120B without a $10,000 Mac Studio This phone-sized device is your own server for open-source models. 100% local and private. And you can use it to: - Power an agent like OpenClaw 24/7 - Completely replace a chatbot - Literally anything that requires an API It’s called Tiiny, and, once again, you can run the latest open-source models on it. Link below

Paul Couvert

41,356 views • 6 months ago

THIS DEVELOPER LINES UP EVERY SMALL COMPUTER FROM RASPBERRY PI TO MAC MINI - AND EVERY SINGLE ONE CAN RUN LOCAL AI Raspberry Pi Zero 2W for $15, Odroid, thin client, AMD BC250 - a whole range of computers from credit card size to full desktop replacement AMD BC250 is an entire mid-range gaming computer on one card - they come in boxes of 12 and every single one runs local LLMs Mac Mini, N97 with 16GB RAM, 13th gen i7 - any of them replaces $236/month in subscriptions and costs $3/month in electricity you don't need an expensive server - even a $15 Raspberry Pi can run something useful locally without cloud and without subscriptions $600 once, one evening of setup - and AI that runs 24/7 on hardware that might already be sitting in your house
2:30

THIS DEVELOPER LINES UP EVERY SMALL COMPUTER FROM RASPBERRY PI TO MAC MINI - AND EVERY SINGLE ONE CAN RUN LOCAL AI Raspberry Pi Zero 2W for $15, Odroid, thin client, AMD BC250 - a whole range of computers from credit card size to full desktop replacement AMD BC250 is an entire mid-range gaming computer on one card - they come in boxes of 12 and every single one runs local LLMs Mac Mini, N97 with 16GB RAM, 13th gen i7 - any of them replaces $236/month in subscriptions and costs $3/month in electricity you don't need an expensive server - even a $15 Raspberry Pi can run something useful locally without cloud and without subscriptions $600 once, one evening of setup - and AI that runs 24/7 on hardware that might already be sitting in your house

Cortex

20,255 views • 3 months ago

THIS DEVELOPER JUST RAN A TRILLION PARAMETER MODEL ON 4 MAC STUDIOS - 10X FASTER AND 5X CHEAPER THAN CLOUD CODE 19:00 he says it out loud. "we just ran a trillion parameter model. 30 something tokens per second. wow." RDMA over Thunderbolt made the cluster 10x faster than before. tensor parallelism splits the model between machines in parallel instead of sequentially DeepSeek V3 full size without quantization. 183GB per machine. still 25 tokens per second. faster than most people can read Kimi K2 with trillion parameters and 256,000 context window. loaded, responded and adapted memory usage dynamically based on prompt size 4 machines consuming 66 watts total. cloud GPU for the same workload costs $1,900/month a cluster that used to cost millions now assembled from Mac Studios in a few hours
22:35

THIS DEVELOPER JUST RAN A TRILLION PARAMETER MODEL ON 4 MAC STUDIOS - 10X FASTER AND 5X CHEAPER THAN CLOUD CODE 19:00 he says it out loud. "we just ran a trillion parameter model. 30 something tokens per second. wow." RDMA over Thunderbolt made the cluster 10x faster than before. tensor parallelism splits the model between machines in parallel instead of sequentially DeepSeek V3 full size without quantization. 183GB per machine. still 25 tokens per second. faster than most people can read Kimi K2 with trillion parameters and 256,000 context window. loaded, responded and adapted memory usage dynamically based on prompt size 4 machines consuming 66 watts total. cloud GPU for the same workload costs $1,900/month a cluster that used to cost millions now assembled from Mac Studios in a few hours

Noisy

80,087 views • 3 months ago

This guy plugged a DGX Spark (the $3K Nvidia box) and a Mac Mini M4 together to run AI and what happened next surprised everyone > the Nvidia box handles the hard part - processing your prompt in milliseconds > Mac Mini M4 handles the fast part - generating the response at memory bandwidth speeds nothing else can match > together they hit 84 tokens per second on Llama - 6x faster than the Spark alone > running compute agents locally on this setup means your data never leaves your hardware > two boxes. two different architectures. one AI system that Deepseek runs at data center scale he ran it on his desk save this. the way we build local AI is about to change
18:41

This guy plugged a DGX Spark (the $3K Nvidia box) and a Mac Mini M4 together to run AI and what happened next surprised everyone > the Nvidia box handles the hard part - processing your prompt in milliseconds > Mac Mini M4 handles the fast part - generating the response at memory bandwidth speeds nothing else can match > together they hit 84 tokens per second on Llama - 6x faster than the Spark alone > running compute agents locally on this setup means your data never leaves your hardware > two boxes. two different architectures. one AI system that Deepseek runs at data center scale he ran it on his desk save this. the way we build local AI is about to change

Mr. Buzzoni

154,038 views • 4 months ago

THIS TOKYO PROGRAMMER MADE $8,500 IN HIS FIRST MONTH WITH A MAC MINI — AND SAYS IT'S THE ONLY MACHINE YOU NEED FOR AI Claude Code, Codex, any AI tool - all of it runs on a Mac Mini with zero issues and zero monthly cloud bills at the 0:11 second mark he turns the monitor around - Claude Code with Opus 4.7 running in full context, terminal active and the agent already working - one programmer, one room in Tokyo, one $599 Mac Mini used to pay $200+ a month on subscriptions and cloud GPU - now pays $3 in electricity and everything else stays in his business $599 invested once - and in the first year he saved $2,364 that used to go to someone else's data center his advice is simple: if you're serious about AI - the Mac Mini is the first thing you should buy
0:25

THIS TOKYO PROGRAMMER MADE $8,500 IN HIS FIRST MONTH WITH A MAC MINI — AND SAYS IT'S THE ONLY MACHINE YOU NEED FOR AI Claude Code, Codex, any AI tool - all of it runs on a Mac Mini with zero issues and zero monthly cloud bills at the 0:11 second mark he turns the monitor around - Claude Code with Opus 4.7 running in full context, terminal active and the agent already working - one programmer, one room in Tokyo, one $599 Mac Mini used to pay $200+ a month on subscriptions and cloud GPU - now pays $3 in electricity and everything else stays in his business $599 invested once - and in the first year he saved $2,364 that used to go to someone else's data center his advice is simple: if you're serious about AI - the Mac Mini is the first thing you should buy

Sprytix

14,771 views • 4 months ago

Video on how to set up a pre release #Toccata $KAS node for Mac users. It is so simple to do we should have 10,000 nodes running… IzioDev -Download the software for Mac at the link in Michael’s tweet -Open the folder. Copy the file path for Kaspad -Paste it in a terminal window, press space, then type “- - utxoindex” (no space between the two dashes). -Press enter and let it run. Use a mini pc or something without a display that can run 24/7. I am using an M1 Mac Mini.
1:09

Video on how to set up a pre release #Toccata $KAS node for Mac users. It is so simple to do we should have 10,000 nodes running… IzioDev -Download the software for Mac at the link in Michael’s tweet -Open the folder. Copy the file path for Kaspad -Paste it in a terminal window, press space, then type “- - utxoindex” (no space between the two dashes). -Press enter and let it run. Use a mini pc or something without a display that can run 24/7. I am using an M1 Mac Mini.

₿RT 𐤊 🐈📈

12,904 views • 4 months ago

FOUR BOXES ON A DESK ARE NOW A FRONTIER AI CLUSTER This is four NVIDIA DGX Spark units wired together, sitting on a single desk and running Qwen3-235B end to end. Each box holds 128GB of unified memory. Stacked together, that is half a terabyte of memory pooled across the cluster, all from hardware you could fit in a backpack. A year ago a setup like this lived in a data center behind a badge reader. Now it runs next to a monitor and a coffee cup. Here is the part people get wrong. The four boxes do not magically merge into one giant GPU. The model is sharded across all of them, and they pass data over a fast link while every box does its slice of the work. That is the whole trick. No single box could hold a 235B model, but split four ways, it runs. The machines stay quiet and barely warm while doing it, sipping a fraction of the power a rack of server GPUs would burn. The wall between you and a frontier-class model used to be a cloud bill. Now it is just desk space. Bookmark this & Watch it run ↓
1:42

FOUR BOXES ON A DESK ARE NOW A FRONTIER AI CLUSTER This is four NVIDIA DGX Spark units wired together, sitting on a single desk and running Qwen3-235B end to end. Each box holds 128GB of unified memory. Stacked together, that is half a terabyte of memory pooled across the cluster, all from hardware you could fit in a backpack. A year ago a setup like this lived in a data center behind a badge reader. Now it runs next to a monitor and a coffee cup. Here is the part people get wrong. The four boxes do not magically merge into one giant GPU. The model is sharded across all of them, and they pass data over a fast link while every box does its slice of the work. That is the whole trick. No single box could hold a 235B model, but split four ways, it runs. The machines stay quiet and barely warm while doing it, sipping a fraction of the power a rack of server GPUs would burn. The wall between you and a frontier-class model used to be a cloud bill. Now it is just desk space. Bookmark this & Watch it run ↓

slash1s

19,473 views • 3 months ago

4 MAC MINIS RUNNING A 671B PARAMETER MODEL AS A CLUSTER No data center, no Cloud, no expensive hardware and not a single API call.. Just 4 Mac minis connected through EXO running DeepSeek v3.1 671b locally and actually fast. The part nobody talks about is that you don’t need one monster machine, you can cluster old computers you already have and split the load between them. The full breakdown of tools that make this possible is in the article below. Save and Read it today ↓
0:29

4 MAC MINIS RUNNING A 671B PARAMETER MODEL AS A CLUSTER No data center, no Cloud, no expensive hardware and not a single API call.. Just 4 Mac minis connected through EXO running DeepSeek v3.1 671b locally and actually fast. The part nobody talks about is that you don’t need one monster machine, you can cluster old computers you already have and split the load between them. The full breakdown of tools that make this possible is in the article below. Save and Read it today ↓

slash1s

58,724 views • 3 months ago

$15,000 for a Mac sounds insane. Until you realize what Apple is actually selling. 256GB of unified memory. Not a huge GPU farm. Not an NVIDIA rack. Not 4 different machines. Just one box that can keep multiple large LLMs in memory and run them locally. That changes the equation for local AI. NVIDIA wins when you need raw GPU compute. Apple gets interesting when the bottleneck is memory capacity. Because once your model doesn't fit into 24GB, 32GB or 48GB of VRAM, buying a faster GPU doesn't solve the problem. You need more memory. And that's where machines like the Mac Studio start looking less crazy. The interesting question isn't: “Is a $15K Mac faster than an NVIDIA GPU?” It isn't. The real question is: “How much are you willing to pay to make huge local models actually fit?” That’s where unified memory gets interesting.
0:44

$15,000 for a Mac sounds insane. Until you realize what Apple is actually selling. 256GB of unified memory. Not a huge GPU farm. Not an NVIDIA rack. Not 4 different machines. Just one box that can keep multiple large LLMs in memory and run them locally. That changes the equation for local AI. NVIDIA wins when you need raw GPU compute. Apple gets interesting when the bottleneck is memory capacity. Because once your model doesn't fit into 24GB, 32GB or 48GB of VRAM, buying a faster GPU doesn't solve the problem. You need more memory. And that's where machines like the Mac Studio start looking less crazy. The interesting question isn't: “Is a $15K Mac faster than an NVIDIA GPU?” It isn't. The real question is: “How much are you willing to pay to make huge local models actually fit?” That’s where unified memory gets interesting.

KyzoroX

19,536 views • 1 month ago

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

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

slash1s

101,317 views • 3 months ago

100 MAC MINIS ON ONE METAL SHELF. IN THE CLOUD, EACH OF THOSE RENTS FOR $120 TO $500 A MONTH. that clip is a homemade rack: wire shelving packed with mac minis, each running macos, wired into one cluster. why macs and not a normal server? two reasons. you can only build, sign, and notarize ios and mac apps on apple hardware. cloud providers know it and charge for it. and apple silicon sips power and runs near-silent, so you can stack a lot of them on a shelf in a room, not a datacenter. the rent they're escaping: a single mac in the cloud runs ~$120 to $500 a month (macstadium up to aws) one engineer documented saving $4,000+ a month by self-hosting mac minis instead of cloud ci runners a used or new mini is a few hundred dollars once, then a few dollars of electricity what a shelf like this actually runs: ios and mac build farms, render jobs, app and device testing automation, and increasingly local ai inference. own the boxes, rent nothing. the uncomfortable part: the cloud was never the only option. it was the convenient one. the people who did the math bought the shelf. the honest caveat: it's real capex up front, real heat and power, and you become the sysadmin. no rented rack means no one to call at 3am. worth it at scale, overkill for one build a week. no rack rental, no per-hour metering, no fleet you don't own. save this before your cloud invoice renews again.
0:25

100 MAC MINIS ON ONE METAL SHELF. IN THE CLOUD, EACH OF THOSE RENTS FOR $120 TO $500 A MONTH. that clip is a homemade rack: wire shelving packed with mac minis, each running macos, wired into one cluster. why macs and not a normal server? two reasons. you can only build, sign, and notarize ios and mac apps on apple hardware. cloud providers know it and charge for it. and apple silicon sips power and runs near-silent, so you can stack a lot of them on a shelf in a room, not a datacenter. the rent they're escaping: a single mac in the cloud runs ~$120 to $500 a month (macstadium up to aws) one engineer documented saving $4,000+ a month by self-hosting mac minis instead of cloud ci runners a used or new mini is a few hundred dollars once, then a few dollars of electricity what a shelf like this actually runs: ios and mac build farms, render jobs, app and device testing automation, and increasingly local ai inference. own the boxes, rent nothing. the uncomfortable part: the cloud was never the only option. it was the convenient one. the people who did the math bought the shelf. the honest caveat: it's real capex up front, real heat and power, and you become the sysadmin. no rented rack means no one to call at 3am. worth it at scale, overkill for one build a week. no rack rental, no per-hour metering, no fleet you don't own. save this before your cloud invoice renews again.

RetroChainer

21,055 views • 2 months ago

A USED $700 RTX 3090 HAS MORE MEMORY AND FAR MORE SPEED THAN THE ~$1,000 A2 GOING INTO THIS SERVER. THE A2 STILL WINS - FOR ONE REASON THAT ISN'T PERFORMANCE. that clip is a dell poweredge r760 - a 2u rack server - getting an nvidia a2 dropped in through a riser. this is the enterprise route to local ai. the rung the desktop ladder skips entirely. the a2, verified: 16gb of memory, single-slot, low-profile, and just 40 to 60 watts it's an inference card, built to sit in a server that has no room or spare power for a real gpu so why not a 3090? a used rtx 3090 gives you 24gb and many times the compute for around $700. the a2 gives 16gb, much slower, for roughly a thousand. on raw local-llm value, the desktop card wins, and it isn't close. the a2's whole reason to exist is the thing you can't see on a spec sheet: it fits. a 2u server has no spare gpu power and no room for a triple-slot furnace. the a2 slips into one low-profile slot on 60 watts and lets an existing server do inference without a rebuild. the honest caveats: the r760 is not a desk machine. loaded, it idles at hundreds of watts, and its fans sound like a hair dryer that never turns off. it belongs in a rack or a closet 16gb is still 16gb. the same 7b-to-32b ceiling as the cheap cards. the enterprise badge doesn't buy you a bigger model what it does buy: ecc memory, redundant power, remote management, hot-swap everything. reliability, not speed so who this is for: someone who already runs a rack and wants to add private inference without touching the power or cooling budget. for anyone starting from a desk, the mac mini or the 3090 wins on every axis that matters. the real point: the "best" local ai box depends entirely on what you already own. on a desk, efficiency wins. in a rack, fit and reliability win. the a2 isn't a bad card - it's a card for a constraint the ladder never mentions. no fastest gpu here, no desk-friendly box, no bigger model than the cheap cards already run. save this before you buy enterprise gear for a desktop job.
0:28

A USED $700 RTX 3090 HAS MORE MEMORY AND FAR MORE SPEED THAN THE ~$1,000 A2 GOING INTO THIS SERVER. THE A2 STILL WINS - FOR ONE REASON THAT ISN'T PERFORMANCE. that clip is a dell poweredge r760 - a 2u rack server - getting an nvidia a2 dropped in through a riser. this is the enterprise route to local ai. the rung the desktop ladder skips entirely. the a2, verified: 16gb of memory, single-slot, low-profile, and just 40 to 60 watts it's an inference card, built to sit in a server that has no room or spare power for a real gpu so why not a 3090? a used rtx 3090 gives you 24gb and many times the compute for around $700. the a2 gives 16gb, much slower, for roughly a thousand. on raw local-llm value, the desktop card wins, and it isn't close. the a2's whole reason to exist is the thing you can't see on a spec sheet: it fits. a 2u server has no spare gpu power and no room for a triple-slot furnace. the a2 slips into one low-profile slot on 60 watts and lets an existing server do inference without a rebuild. the honest caveats: the r760 is not a desk machine. loaded, it idles at hundreds of watts, and its fans sound like a hair dryer that never turns off. it belongs in a rack or a closet 16gb is still 16gb. the same 7b-to-32b ceiling as the cheap cards. the enterprise badge doesn't buy you a bigger model what it does buy: ecc memory, redundant power, remote management, hot-swap everything. reliability, not speed so who this is for: someone who already runs a rack and wants to add private inference without touching the power or cooling budget. for anyone starting from a desk, the mac mini or the 3090 wins on every axis that matters. the real point: the "best" local ai box depends entirely on what you already own. on a desk, efficiency wins. in a rack, fit and reliability win. the a2 isn't a bad card - it's a card for a constraint the ladder never mentions. no fastest gpu here, no desk-friendly box, no bigger model than the cheap cards already run. save this before you buy enterprise gear for a desktop job.

Grimmer

24,287 views • 2 months ago

A used Mac Pro off Craigslist for $800, three weeks of upgrades and a $2,070 total build cost. 192GB RAM, an Afterburner card, 8TB NVMe and an eGPU enclosure later, video exports dropped from 18 minutes to 4, three local LLMs run simultaneously without breaking a sweat and models that normally need cloud compute run entirely locally. Apple wants $12,000 for similar specs. The used market sold him the same hardware for a fraction of that, and the $400 to $600 in monthly API savings paid the whole build back in under 4 months.
0:58

A used Mac Pro off Craigslist for $800, three weeks of upgrades and a $2,070 total build cost. 192GB RAM, an Afterburner card, 8TB NVMe and an eGPU enclosure later, video exports dropped from 18 minutes to 4, three local LLMs run simultaneously without breaking a sweat and models that normally need cloud compute run entirely locally. Apple wants $12,000 for similar specs. The used market sold him the same hardware for a fraction of that, and the $400 to $600 in monthly API savings paid the whole build back in under 4 months.

0xMarioNawfal

138,239 views • 2 months ago