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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...

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Noisy

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47,399 просмотров • 21 дней назад •via X (Twitter)

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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 competitors - you need four - one Mac Studio does it alone runs quieter than a fan in the room and uses less energy than a kettle in the kitchen four Mac Studios on a desk connected through a regular cable run a 1 trillion parameter model - what used to require an entire data center anyone can put this at home and Jeff Geerling proved it - run the same models that big companies use without cloud, without subscriptions, without data leaving the room
14:24

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 competitors - you need four - one Mac Studio does it alone runs quieter than a fan in the room and uses less energy than a kettle in the kitchen four Mac Studios on a desk connected through a regular cable run a 1 trillion parameter model - what used to require an entire data center anyone can put this at home and Jeff Geerling proved it - run the same models that big companies use without cloud, without subscriptions, without data leaving the room

Noisy

26,153 просмотров • 17 дней назад

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

79,665 просмотров • 26 дней назад

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

105,595 просмотров • 24 дней назад

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

101,133 просмотров • 1 месяц назад

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 просмотров • 1 месяц назад

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 просмотров • 24 дней назад

THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less
24:17

THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less

Noisy

184,815 просмотров • 25 дней назад

APPLE JUST SHOWED HOW TO RUN 10 AI AGENTS LOCALLY ON MAC - NO CLOUD, NO API KEYS, ZERO COSTS 00:10 Apple engineer says: "your data stays on your device, AI available anywhere at any time, zero usage costs" agent reads your code, checks GitHub, finds what needs attention and writes a report - all on your Mac, nothing goes to the internet 10 agents work simultaneously - one writes code, another tests, third fixes bugs - parallel with no queue built a full iPad app from scratch in 2 minutes, fixed its own mistakes and compiled without issues takes 5 minutes to set up - and you never pay again for a bot that runs 24/7
13:29

APPLE JUST SHOWED HOW TO RUN 10 AI AGENTS LOCALLY ON MAC - NO CLOUD, NO API KEYS, ZERO COSTS 00:10 Apple engineer says: "your data stays on your device, AI available anywhere at any time, zero usage costs" agent reads your code, checks GitHub, finds what needs attention and writes a report - all on your Mac, nothing goes to the internet 10 agents work simultaneously - one writes code, another tests, third fixes bugs - parallel with no queue built a full iPad app from scratch in 2 minutes, fixed its own mistakes and compiled without issues takes 5 minutes to set up - and you never pay again for a bot that runs 24/7

Noisy

522,781 просмотров • 22 дней назад

Google just shipped a free dictation app for Mac and iPhone called AI Edge Eloquent, and the model running it is Gemma 4 12B, entirely on your device. What it actually does: you speak, it transcribes, removes filler words, and polishes the result before dropping clean text into whatever app you are using. You can also give it voice commands to reformat, like "translate to Hindi" or "make this a bullet list." Audio and video file transcription is built in too. The interesting part is the privacy angle. Most AI writing tools send your audio to a server. This one never leaves your machine, which matters if you are dictating anything sensitive. The tradeoff is hardware: you need a Mac with at least 16GB of RAM to run a 12B parameter model comfortably.
1:15

Google just shipped a free dictation app for Mac and iPhone called AI Edge Eloquent, and the model running it is Gemma 4 12B, entirely on your device. What it actually does: you speak, it transcribes, removes filler words, and polishes the result before dropping clean text into whatever app you are using. You can also give it voice commands to reformat, like "translate to Hindi" or "make this a bullet list." Audio and video file transcription is built in too. The interesting part is the privacy angle. Most AI writing tools send your audio to a server. This one never leaves your machine, which matters if you are dictating anything sensitive. The tradeoff is hardware: you need a Mac with at least 16GB of RAM to run a 12B parameter model comfortably.

Aditya ⚡Rao

84,813 просмотров • 1 месяц назад

Karpathy told Dwarkesh that a 1 billion parameter model, trained on clean data, could hit the intelligence of today's 1.8 trillion parameter frontier. That is a 1,800x compression claim. The math behind it is more defensible than it sounds. When researchers at frontier labs look at random samples from their training corpus, they see stock ticker symbols, broken HTML, forum spam, autogenerated gibberish. Not Wikipedia. Not the Wall Street Journal. The actual pretraining dataset is mostly noise, and the model is burning parameters to vaguely remember all of it. One estimate pegs Llama 3's information compression at 0.07 bits per token. Well-structured English carries around 1.5 bits per token of real information. The trillion-parameter model is holding a roughly 5% resolution image of the internet it trained on. So when a lab ships a 1.8 trillion parameter model, the overwhelming majority of those weights are handling rough memorization. They are compression overhead for a noisy training set, taking up capacity that could be doing reasoning instead. Karpathy's proposal is to separate the two. Build a cognitive core: a small model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization. Pair it with external memory the model queries when it needs a fact. A 1 billion parameter reasoner plus retrieval beats a 1.8 trillion parameter model trying to do both. The data already supports this direction. GPT-4o runs at roughly 200 billion parameters and outperforms the original 1.8 trillion GPT-4. Inference costs for GPT-3.5 level performance fell 280x between 2022 and 2024, driven almost entirely by smaller, cleaner, better-architected models. The trend line is pointing where Karpathy says it should. The real implication for anyone tracking the AI trade: data quality is the actual constraint. The companies winning the next phase will be the ones who figured out what to train on, and what to throw away.
2:00

Karpathy told Dwarkesh that a 1 billion parameter model, trained on clean data, could hit the intelligence of today's 1.8 trillion parameter frontier. That is a 1,800x compression claim. The math behind it is more defensible than it sounds. When researchers at frontier labs look at random samples from their training corpus, they see stock ticker symbols, broken HTML, forum spam, autogenerated gibberish. Not Wikipedia. Not the Wall Street Journal. The actual pretraining dataset is mostly noise, and the model is burning parameters to vaguely remember all of it. One estimate pegs Llama 3's information compression at 0.07 bits per token. Well-structured English carries around 1.5 bits per token of real information. The trillion-parameter model is holding a roughly 5% resolution image of the internet it trained on. So when a lab ships a 1.8 trillion parameter model, the overwhelming majority of those weights are handling rough memorization. They are compression overhead for a noisy training set, taking up capacity that could be doing reasoning instead. Karpathy's proposal is to separate the two. Build a cognitive core: a small model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization. Pair it with external memory the model queries when it needs a fact. A 1 billion parameter reasoner plus retrieval beats a 1.8 trillion parameter model trying to do both. The data already supports this direction. GPT-4o runs at roughly 200 billion parameters and outperforms the original 1.8 trillion GPT-4. Inference costs for GPT-3.5 level performance fell 280x between 2022 and 2024, driven almost entirely by smaller, cleaner, better-architected models. The trend line is pointing where Karpathy says it should. The real implication for anyone tracking the AI trade: data quality is the actual constraint. The companies winning the next phase will be the ones who figured out what to train on, and what to throw away.

Aakash Gupta

507,835 просмотров • 2 месяцев назад

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

153,432 просмотров • 1 месяц назад

INTEL JUST SHIPPED A WORKSTATION CARD WITH TWO GPUs ON ONE PCB AND 48GB OF VRAM. FOUR CARDS GIVE A SINGLE MOTHERBOARD 192GB OF POOLED INFERENCE MEMORY FOR THE PRICE OF TWO RTX 5090s 00:14 he holds one card up to the camera, two GPU dies side by side under the cooler, each one running its own x8 PCIe lane back to the chipset the cluster sees eight discrete accelerators in software. intel's Battle Matrix stack shards a model across all eight, so a 235B parameter network loads in slices and answers requests in parallel what 192GB of VRAM unlocks: an entire 200B class model in memory without quantization. a vision agent reading 100 invoices at once. a research box that holds three frontier models loaded simultaneously, switching between them in under a second intel is the slow side of inference. nvidia is faster per token, that is the honest tradeoff. but the only other path to this much VRAM is a $40,000 nvidia rack or three networked Mac Studios four B60 cards plus the chassis lands under $5,000. power draw averages 800 watts, $55 a month in electricity. one engineer paying $400 a month for combined ChatGPT Pro and Claude Code Max pays the hardware off in less than a year
1:16

INTEL JUST SHIPPED A WORKSTATION CARD WITH TWO GPUs ON ONE PCB AND 48GB OF VRAM. FOUR CARDS GIVE A SINGLE MOTHERBOARD 192GB OF POOLED INFERENCE MEMORY FOR THE PRICE OF TWO RTX 5090s 00:14 he holds one card up to the camera, two GPU dies side by side under the cooler, each one running its own x8 PCIe lane back to the chipset the cluster sees eight discrete accelerators in software. intel's Battle Matrix stack shards a model across all eight, so a 235B parameter network loads in slices and answers requests in parallel what 192GB of VRAM unlocks: an entire 200B class model in memory without quantization. a vision agent reading 100 invoices at once. a research box that holds three frontier models loaded simultaneously, switching between them in under a second intel is the slow side of inference. nvidia is faster per token, that is the honest tradeoff. but the only other path to this much VRAM is a $40,000 nvidia rack or three networked Mac Studios four B60 cards plus the chassis lands under $5,000. power draw averages 800 watts, $55 a month in electricity. one engineer paying $400 a month for combined ChatGPT Pro and Claude Code Max pays the hardware off in less than a year

NO1ennn

48,054 просмотров • 15 дней назад

Andrej Karpathy just made one of the most interesting arguments about AI model design that most people are completely missing. His take is that frontier AI models are not too big because the technology is complex and too big because the training data is garbage. When you or I think of the internet, we picture Wall Street Journal articles, Wikipedia entries, serious writing. That is not what a pretraining dataset looks like. When researchers at frontier labs look at random documents from the actual training corpus, it is stock ticker symbols, broken HTML, spam, gibberish. One estimate puts Llama 3's information compression at just 0.07 bits per token meaning the model has only a hazy recollection of most of what it trained on. So we build trillion parameter models not because we need a trillion parameter brain but because we need a trillion-parameter compression engine to squeeze some intelligence out of a firehose of noise. Most of those parameters are doing memory work, not cognitive work. Karpathy's prediction is separate the two entirely. Build a cognitive core, a model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization and pair it with external memory that it can query when it needs facts. He thinks a cognitive core trained on high-quality data could hit genuine intelligence at around one billion parameters. For reference, today's flagship models run between 200 billion and 1.8 trillion parameters with most of that weight dedicated to remembering the internet's slop. The trend is already moving his direction. GPT-4o operates at roughly 200 billion parameters and outperforms the original 1.8 trillion-parameter GPT-4. Inference costs for GPT-3.5-level performance dropped 280-fold between 2022 and 2024 driven almost entirely by smaller, cleaner, better-architected models. The real bottleneck in AI right now is not compute but rather data quality.
2:00

Andrej Karpathy just made one of the most interesting arguments about AI model design that most people are completely missing. His take is that frontier AI models are not too big because the technology is complex and too big because the training data is garbage. When you or I think of the internet, we picture Wall Street Journal articles, Wikipedia entries, serious writing. That is not what a pretraining dataset looks like. When researchers at frontier labs look at random documents from the actual training corpus, it is stock ticker symbols, broken HTML, spam, gibberish. One estimate puts Llama 3's information compression at just 0.07 bits per token meaning the model has only a hazy recollection of most of what it trained on. So we build trillion parameter models not because we need a trillion parameter brain but because we need a trillion-parameter compression engine to squeeze some intelligence out of a firehose of noise. Most of those parameters are doing memory work, not cognitive work. Karpathy's prediction is separate the two entirely. Build a cognitive core, a model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization and pair it with external memory that it can query when it needs facts. He thinks a cognitive core trained on high-quality data could hit genuine intelligence at around one billion parameters. For reference, today's flagship models run between 200 billion and 1.8 trillion parameters with most of that weight dedicated to remembering the internet's slop. The trend is already moving his direction. GPT-4o operates at roughly 200 billion parameters and outperforms the original 1.8 trillion-parameter GPT-4. Inference costs for GPT-3.5-level performance dropped 280-fold between 2022 and 2024 driven almost entirely by smaller, cleaner, better-architected models. The real bottleneck in AI right now is not compute but rather data quality.

Milk Road AI

200,209 просмотров • 2 месяцев назад

THE $599 MAC MINI GETS A $49 DOCK AND TURNS FROM A DESK COMPUTER INTO A TINY 24/7 AI WORKFLOW SERVER 00:19 he clips the dock onto the back of the mac mini, adds extra ports, ethernet, storage and turns one small silver box into a workflow hub that can stay online for 700+ hours a month a base mac mini can sit online all day, pulling light power while scripts watch folders, move files, process notes and keep boring workflows running in the background paired with claude, it becomes a small home automation box: 2-hour lectures get transcribed, 30-page articles get summarized, reports get drafted, and research gets cleaned before you even open the laptop the difference is the loop. trigger, process, verify, save, repeat. the same workflow that costs $40-100/month across cloud tools can run locally from one machine on your desk that is why this tiny upgrade matters. the dock is cheap, but it gives the mac mini enough expansion to act like a quiet server instead of another computer waiting for you to use it bookmark this before your desk setup realizes it became infrastructure.
0:29

THE $599 MAC MINI GETS A $49 DOCK AND TURNS FROM A DESK COMPUTER INTO A TINY 24/7 AI WORKFLOW SERVER 00:19 he clips the dock onto the back of the mac mini, adds extra ports, ethernet, storage and turns one small silver box into a workflow hub that can stay online for 700+ hours a month a base mac mini can sit online all day, pulling light power while scripts watch folders, move files, process notes and keep boring workflows running in the background paired with claude, it becomes a small home automation box: 2-hour lectures get transcribed, 30-page articles get summarized, reports get drafted, and research gets cleaned before you even open the laptop the difference is the loop. trigger, process, verify, save, repeat. the same workflow that costs $40-100/month across cloud tools can run locally from one machine on your desk that is why this tiny upgrade matters. the dock is cheap, but it gives the mac mini enough expansion to act like a quiet server instead of another computer waiting for you to use it bookmark this before your desk setup realizes it became infrastructure.

Gipp 🦅

73,558 просмотров • 19 дней назад

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below
2:00

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below

John Doe

193,709 просмотров • 1 месяц назад

Claude Fable 5 is INSANE 🤯 So we asked it to build the perfect private AI infrastructure, where: → 500+ open models run behind a single API, not one company's black box → An open data marketplace → You fine-tune and serve models on decentralized GPU compute → Agents pay per call in USDC via x402 → Every model, dataset, and payment settles on Base It failed. Luckily, we've spent years building exactly that. Private Inference. Data. Compute. Settlement. One private AI infrastructure layer. Cluster Protocol.
0:21

Claude Fable 5 is INSANE 🤯 So we asked it to build the perfect private AI infrastructure, where: → 500+ open models run behind a single API, not one company's black box → An open data marketplace → You fine-tune and serve models on decentralized GPU compute → Agents pay per call in USDC via x402 → Every model, dataset, and payment settles on Base It failed. Luckily, we've spent years building exactly that. Private Inference. Data. Compute. Settlement. One private AI infrastructure layer. Cluster Protocol.

Cluster Protocol

25,516 просмотров • 1 месяц назад

Jensen Huang told a room of global investors that AI is not one industry. It is five stacked on top of each other. Most people are investing in layer four and ignoring layers one through three entirely. He called it the five-layer cake. Layer one is energy. Jensen said this is the single greatest opportunity for the energy industry in a hundred years. The first time in a century that the grid in most countries can actually attract serious capital. Nuclear, solar, wind, hydrogen, it does not matter what form. If it produces energy, it gets funded. Siemens, GE Vernova, Mitsubishi. That is why they are all doing so well right now. Layer two is chips, computers, networking, and silicon photonics. Everything that processes the intelligence. Layer three is infrastructure. Land, power, buildings, data center operations. Every single one in short supply today. Layer four is the model layer. OpenAI, Anthropic. The layer everyone talks about. Layer five is applications. Every startup applying AI to financial services, legal, healthcare, logistics, transportation. Last year alone, a hundred billion dollars of venture capital went into this layer. The single largest VC year in the history of humanity. Then he said the number that stopped me cold. We are putting one trillion dollars into this five-layer cake this year. That sounds enormous. Jensen thinks the AI industry will eventually run at twenty trillion dollars per year. We are one trillion in of a twenty trillion dollar per year ecosystem. Most people watching AI are staring at layer four. Jensen was describing layers one through five as a single compounding system where every layer feeds the one above it. The people who understand that will invest differently than the people who do not.
4:50

Jensen Huang told a room of global investors that AI is not one industry. It is five stacked on top of each other. Most people are investing in layer four and ignoring layers one through three entirely. He called it the five-layer cake. Layer one is energy. Jensen said this is the single greatest opportunity for the energy industry in a hundred years. The first time in a century that the grid in most countries can actually attract serious capital. Nuclear, solar, wind, hydrogen, it does not matter what form. If it produces energy, it gets funded. Siemens, GE Vernova, Mitsubishi. That is why they are all doing so well right now. Layer two is chips, computers, networking, and silicon photonics. Everything that processes the intelligence. Layer three is infrastructure. Land, power, buildings, data center operations. Every single one in short supply today. Layer four is the model layer. OpenAI, Anthropic. The layer everyone talks about. Layer five is applications. Every startup applying AI to financial services, legal, healthcare, logistics, transportation. Last year alone, a hundred billion dollars of venture capital went into this layer. The single largest VC year in the history of humanity. Then he said the number that stopped me cold. We are putting one trillion dollars into this five-layer cake this year. That sounds enormous. Jensen thinks the AI industry will eventually run at twenty trillion dollars per year. We are one trillion in of a twenty trillion dollar per year ecosystem. Most people watching AI are staring at layer four. Jensen was describing layers one through five as a single compounding system where every layer feeds the one above it. The people who understand that will invest differently than the people who do not.

Ihtesham Ali

97,514 просмотров • 1 месяц назад

For the first time, the latest LLMs run on the Apple Neural Engine — and NexaSDK is the only framework that makes it possible, powered by the NexaML engine. Last year, our two co-founders were invited by Apple DMLI team (Data & Machine Learning Innovation) to share their research about on-device multimodal model for local AI agents. One of the big questions in the room was: “Can the newest LLMs actually run on ANE?” At the time, nobody had a clear path. Today, that path exists. NexaSDK now runs Granite-4.0 (IBM), Qwen3 (Qwen), Gemma3 (Google), and Parakeet-v3 (NVIDIA) fully on Apple’s NPU — unlocking low-power, always-on, fast inference across Mac and iPhone. A new wave of NPU-first local AI apps is coming to Apple devices. Start with one line of code on Mac. iOS SDK coming soon.
1:42

For the first time, the latest LLMs run on the Apple Neural Engine — and NexaSDK is the only framework that makes it possible, powered by the NexaML engine. Last year, our two co-founders were invited by Apple DMLI team (Data & Machine Learning Innovation) to share their research about on-device multimodal model for local AI agents. One of the big questions in the room was: “Can the newest LLMs actually run on ANE?” At the time, nobody had a clear path. Today, that path exists. NexaSDK now runs Granite-4.0 (IBM), Qwen3 (Qwen), Gemma3 (Google), and Parakeet-v3 (NVIDIA) fully on Apple’s NPU — unlocking low-power, always-on, fast inference across Mac and iPhone. A new wave of NPU-first local AI apps is coming to Apple devices. Start with one line of code on Mac. iOS SDK coming soon.

NEXA AI

30,213 просмотров • 7 месяцев назад

🚨Stop gambling on the wrong local LLM. Most people download a model, watch it crawl, then start over. LLM Hardware Scanner is a CLI tool that scans your actual specs and ranks hundreds of LLMs before you waste the bandwidth: ⤷ Reads your RAM, CPU, and GPU to score real compatibility ⤷ Accounts for MoE activation not just bloated total parameter counts ⤷ Auto-selects the optimal quantization for your exact setup ⤷ Ranks models across quality, speed, context length, and true memory fit ⤷ Works natively with Ollama, llama.cpp, and MLX ⤷ Shows a composite score plus a speed estimate before you download a single byte Not a benchmark site. Not a spec sheet. A hardware-aware ranker that tells you the truth about what will actually run on your machine. One scan. Instant ranking. Zero wasted downloads.
0:21

🚨Stop gambling on the wrong local LLM. Most people download a model, watch it crawl, then start over. LLM Hardware Scanner is a CLI tool that scans your actual specs and ranks hundreds of LLMs before you waste the bandwidth: ⤷ Reads your RAM, CPU, and GPU to score real compatibility ⤷ Accounts for MoE activation not just bloated total parameter counts ⤷ Auto-selects the optimal quantization for your exact setup ⤷ Ranks models across quality, speed, context length, and true memory fit ⤷ Works natively with Ollama, llama.cpp, and MLX ⤷ Shows a composite score plus a speed estimate before you download a single byte Not a benchmark site. Not a spec sheet. A hardware-aware ranker that tells you the truth about what will actually run on your machine. One scan. Instant ranking. Zero wasted downloads.

Charlie Hills

37,499 просмотров • 4 месяцев назад

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

16,937 просмотров • 16 дней назад