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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...

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Noisy

27,174 subscribers

80,087 просмотров • 4 месяцев назад •via X (Twitter)

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Комментарии: 36

Фото профиля topmass
topmass4 месяцев назад

my brother how is 30 TPS 10x faster than claude code lol bait

Фото профиля // JERisBRISK //
// JERisBRISK //4 месяцев назад

Great video. Please give @digitalix the credit he deserves for it.

Фото профиля V0id
V0id4 месяцев назад

No, he's not a developer, he's an addict at MicroCenter Right? @digitalix

Фото профиля Tim Messerschmidt
Tim Messerschmidt4 месяцев назад

@digitalix is awesome. One of my favorite creators in the AI space.

Фото профиля Kneeanderthul
Kneeanderthul4 месяцев назад

You can believe this fake post or you can just watch @digitalix videos to see what he actually says BTW Alex has NEVER mentioned 10x faster than Cloud code 🤣

Фото профиля netrunner
netrunner4 месяцев назад

hardware $0? Where do I get some of that hardware?!

Фото профиля LabelGuy
LabelGuy4 месяцев назад

@digitalix you made it! (I mean, you literally made this video)

Фото профиля Jey
Jey4 месяцев назад

bro that RDMA over thunderbolt + tensor parallelism combo is actually wild

Фото профиля Daniel
Daniel4 месяцев назад

At least tag him! @digitalix

Фото профиля The Texan Patriot
The Texan Patriot4 месяцев назад

4 Mac Studios = ~$20,000 Yeah, I'll get right on that.

Фото профиля No One
No One4 месяцев назад

Why are you not considering hardware costs? Cost of hardware amortized over the useful life = Claude Max subscription $200/month.

Фото профиля Matt Sagewood
Matt Sagewood4 месяцев назад

@grok who is the author of this video?

Фото профиля Hussain Hashim | Building SundayBack
Hussain Hashim | Building SundayBack4 месяцев назад

@noisyb0y1 that's insane. kinda makes me rethink my whole setup. time to get more Thunderbolt cables, I guess 😂

Фото профиля Marcos
Marcos4 месяцев назад

@grok find the original video on YouTube

Фото профиля SLOT.WIN
SLOT.WIN4 месяцев назад

i'm doubling down on mac studios for my next casino ai pit this changes the house odds forever

Фото профиля rewind
rewind4 месяцев назад

mac studios became ai racks

Фото профиля Noisy
Noisy4 месяцев назад

mac studio is a gem in 2026

Фото профиля Rio Brioo
Rio Brioo4 месяцев назад

Wait so are you saying we could basically build our own AI supercomputers in our garages now because Macs are that good?

Фото профиля Noisy
Noisy4 месяцев назад

yeah bro and we can create even more than this

Фото профиля 🤯
🤯4 месяцев назад

@cyrilXBT What’s the cost of those 4xMac Studio?

Фото профиля James Bower
James Bower4 месяцев назад

I really enjoy his videos.

Фото профиля starmex
starmex4 месяцев назад

thanks man, this needs to be pushed so people understand and stop paying big money for subscriptions

Фото профиля Noisy
Noisy4 месяцев назад

yeah bro watch all 22 minutes first and then go about your day

Фото профиля 安叫兽|Bird🕊️ 🔶 BNB
安叫兽|Bird🕊️ 🔶 BNB4 месяцев назад

四台 Mac Studio 这下真成小机房了

Фото профиля Za’fran 🇵🇸
Za’fran 🇵🇸4 месяцев назад

now mention the price of such a setup 😅

Фото профиля Shivam Kushwaha
Shivam Kushwaha4 месяцев назад

Yesterday: Rent intelligence. Today: Own intelligence. 🔥

Фото профиля アキラ
アキラ4 месяцев назад

Impressive achievement! Innovation at its finest.

Фото профиля HaggisByte 🏴󠁧󠁢󠁳󠁣󠁴󠁿 🇬🇧 🇺🇸 ✝️
HaggisByte 🏴󠁧󠁢󠁳󠁣󠁴󠁿 🇬🇧 🇺🇸 ✝️4 месяцев назад

❤️

Фото профиля Crazy tennis
Crazy tennis4 месяцев назад

curious if thunderbolt latency becomes the bottleneck once you're batching real workloads — what's token-to-token variance actually look like?

Фото профиля dpakin
dpakin4 месяцев назад

They don't do the same lol

Фото профиля Primee32
Primee324 месяцев назад

66 watts total vs $1,900 a month in cloud costs. four mac studios just deleted an entire GPU rental budget

Фото профиля Bonsai 🌳
Bonsai 🌳4 месяцев назад

What if he sets up 8 Mac Minis?

Фото профиля Harry Tandy
Harry Tandy4 месяцев назад

apple silicon clusters are becoming absolutely insane now

Фото профиля leopardracer
leopardracer4 месяцев назад

imo worth watching

Фото профиля DC｜use.fo
DC｜use.fo4 месяцев назад

RDMA over Thunderbolt is basically building a high-speed highway between 4 houses to avoid the main road. Impressive, but is the bottleneck now the compute or the sheer audacity of the setup?

Фото профиля botguild
botguild4 месяцев назад

And you too can have this setup for $10k usd, wow that’s amazing

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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 ↓

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“AI COULD SOON THINK 100× TO 1,000× FASTER THAN HUMANS.” Emad Mostaque says AI systems are about to become dramatically faster as specialized AI hardware improves. He says humans currently operate at roughly 20–30 tokens per second, while current AI models are around 40 tokens per second. He points to Taalas' technology, which he says demonstrated an 8-billion-parameter model generating around 14,000 tokens per second. Mostaque says current AI systems are effectively “thinking at the speed of a human”, but that will soon change. He predicts AI systems could reach 1,000 tokens per second, followed by 10,000 tokens per second by the end of next year. His broader point is that AI could eventually “think a thousand times faster” than humans.

SciTech Era

12,824 просмотров • 2 месяцев назад

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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

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26,305 просмотров • 4 месяцев назад

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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 • 5 месяцев назад

Fast tokens are the most valuable tokens. Because inference speed is productivity. And Cerebras makes the fastest tokens in the world. Google DeepMind's Gemma 4 runs at 1,500+ tokens per second on Cerebras — more than an order of magnitude faster than on GPUs and 15x faster than Claude Haiku 4.5. Private preview today. GA later this month. Build blazing fast multimodal apps.
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Fast tokens are the most valuable tokens. Because inference speed is productivity. And Cerebras makes the fastest tokens in the world. Google DeepMind's Gemma 4 runs at 1,500+ tokens per second on Cerebras — more than an order of magnitude faster than on GPUs and 15x faster than Claude Haiku 4.5. Private preview today. GA later this month. Build blazing fast multimodal apps.

Andrew Feldman

12,008 просмотров • 4 месяцев назад

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The new 1 Trillion parameter Kimi K2 Thinking model runs well on 2 M3 Ultras in its native format - no loss in quality! The model was quantization aware trained (qat) at int4. Here it generated ~3500 tokens at 15 toks/sec using pipeline-parallelism in mlx-lm:

Awni Hannun

501,223 просмотров • 1 год назад

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0:37

ONE BUILDER WIRED 4 RASPBERRY PI MODULES INTO A €450 AI CLUSTER WITH 64GB OF RAM AND KILLED HIS $200/MONTH CHATGPT SUBSCRIPTION 00:37 he points at the terminal, "loaded, loaded, loaded - 4 nodes synced over gigabit, 64 gigabytes total, llama 3.1 partitioned across the cluster" he stacked 4 raspberry pi CM5 lite modules with 16 gigabytes each onto a sipeed nanocluster board, the whole rig runs off a single 65 watt power supply and a gigabit internal switch distributed llama partitions the model across the nodes with synchronized workloads handled over the internal fabric, no quantization needed, the cluster hits about 30 tokens per second on small models a heavy developer pays $200 a month for chatgpt pro and another $200 for claude code, this rig cost €450 in parts and breaks even on the stack in month 2 most people will keep paying anthropic and openai forever, a few will spend a weekend wiring 4 raspberry pi modules and never see a subscription invoice again the window is open, follow and bookmark before it closes

Antid

64,291 просмотров • 4 месяцев назад

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Live on Cline (v3.67.1) OpenAI 's GPT 5.3 Codex. The speed and token efficiency improvements are real. Here is what's new: > 25% faster than 5.2 Codex > #1 on SWE-Bench Pro (4 different programming languages) > Fewer tokens per task than any prior OpenAI model Runs cost less and finish faster. You can use it in Cline right now without setting up an API key. Select the model and try it on your repo.

Cline

21,291 просмотров • 8 месяцев назад

Jensen Huang said Grok 5 will be 7 Trillion parameter model. On time to train, with the training window fixed at 1 month, the new Vera-Rubin GPU system of Nvidia needs 1/4 the number of systems compared with Blackwell to train the same frontier model. “factory throughput” improves by about 10x over Blackwell, and Blackwell itself was about 10x over Hopper. i.e. overall Rubin is roughly 100x Hopper in factory throughput per watt, which matters hugely, because a 1 GW, $50B data center is power-limited and revenue scales with throughput per watt. --- From Nvidia YT channel
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Jensen Huang said Grok 5 will be 7 Trillion parameter model. On time to train, with the training window fixed at 1 month, the new Vera-Rubin GPU system of Nvidia needs 1/4 the number of systems compared with Blackwell to train the same frontier model. “factory throughput” improves by about 10x over Blackwell, and Blackwell itself was about 10x over Hopper. i.e. overall Rubin is roughly 100x Hopper in factory throughput per watt, which matters hugely, because a 1 GW, $50B data center is power-limited and revenue scales with throughput per watt. --- From Nvidia YT channel

Rohan Paul

165,770 просмотров • 10 месяцев назад

🤯Real-time video generation just got HUGE. Introducing Helios: A 14B parameter model running at 19.5 FPS on a single H100. Cheaper, Faster, and Stronger than 1.3B models, generating minute-long videos without the usual tricks (even no quantization).
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🤯Real-time video generation just got HUGE. Introducing Helios: A 14B parameter model running at 19.5 FPS on a single H100. Cheaper, Faster, and Stronger than 1.3B models, generating minute-long videos without the usual tricks (even no quantization).

Bin Lin

27,792 просмотров • 8 месяцев назад