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ELON MUSK: "Grok 5 will be the largest model, a 6 trillion parameter model, whereas Grok 3 and 4 are based on a 3 trillion parameter model. Moreover, the 6 trillion parameters will have a much higher intelligence density per gigabyte. Its really going to feel Sentient."

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322,343 просмотров • 9 месяцев назад •via X (Twitter)

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The Grok 5 model has a massive 6 trillion parameters plus much higher intelligence density. 2026 is going to be exciting! 🧠 Thanks Elon Musk !
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The Grok 5 model has a massive 6 trillion parameters plus much higher intelligence density. 2026 is going to be exciting! 🧠 Thanks Elon Musk !

Michael Dell 🇺🇸

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Grok 4.6 drops around August 7 with a 1.5 trillion parameter model and improved fine-tuning, per Elon Musk. Grok 4.7 follows a few weeks later at 2.1T, better in every benchmark but slightly slower to serve. xAI is stacking releases while Grok 4.5 already beats Opus 5 on price-performance in cybersecurity benchmarks.
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Grok 4.6 drops around August 7 with a 1.5 trillion parameter model and improved fine-tuning, per Elon Musk. Grok 4.7 follows a few weeks later at 2.1T, better in every benchmark but slightly slower to serve. xAI is stacking releases while Grok 4.5 already beats Opus 5 on price-performance in cybersecurity benchmarks.

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35,731 просмотров • 25 дней назад

⚡️ JUST IN: MUSK LAUNCHES GROK 4.5, CALLING IT "OPUS-CLASS" AI MODEL Musk just released Grok 4.5 on Grok Build, Cursor, and the SpaceXAI Console, calling it an "Opus-class" AI model. He says it is faster, more token-efficient and lower cost, with EU access expected by mid-July. Built on a 1.5 TRILLION parameter foundation model, it drops the same day as OpenAI's GPT-5.6. API pricing is $2 per million input tokens and $6 per million output tokens.
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⚡️ JUST IN: MUSK LAUNCHES GROK 4.5, CALLING IT "OPUS-CLASS" AI MODEL Musk just released Grok 4.5 on Grok Build, Cursor, and the SpaceXAI Console, calling it an "Opus-class" AI model. He says it is faster, more token-efficient and lower cost, with EU access expected by mid-July. Built on a 1.5 TRILLION parameter foundation model, it drops the same day as OpenAI's GPT-5.6. API pricing is $2 per million input tokens and $6 per million output tokens.

Coin Bureau

50,488 просмотров • 1 месяц назад

NVIDIA CEO: GROK 5 IS A 7 TRILLION PARAMETER RACE AGAINST TIME Jensen Huang is dialing in on the real challenge: not making bigger models, but training them fast without draining power or budgets. Grok 5 is right in the middle of that race. “The next frontier model. Elon already mentioned that the next version of Grok, Grok 5 I believe, is 7 trillion parameters. This one is 10, and the green represents Blackwell. In the case of Rubin, notice that the throughput is much higher, so it only takes one fourth as many of these systems to train the model within the one-month timeframe we have given here.” Source: Rohan Paul
1:24

NVIDIA CEO: GROK 5 IS A 7 TRILLION PARAMETER RACE AGAINST TIME Jensen Huang is dialing in on the real challenge: not making bigger models, but training them fast without draining power or budgets. Grok 5 is right in the middle of that race. “The next frontier model. Elon already mentioned that the next version of Grok, Grok 5 I believe, is 7 trillion parameters. This one is 10, and the green represents Blackwell. In the case of Rubin, notice that the throughput is much higher, so it only takes one fourth as many of these systems to train the model within the one-month timeframe we have given here.” Source: Rohan Paul

Mario Nawfal

76,356 просмотров • 7 месяцев назад

2.8 trillion parameters. Number one on frontend code arena. The largest open-weight model ever. Sputnik moment? It’s totally mind boggling that Elon Musk can spend $16 billion on Colossus 2 for a 20 trillion parameter model, and there's a 1% cost distilled version that’s coming out of China within weeks. Alex Karp said last week that you can’t throw all your proprietary weights over the wall to Anthropic. But he didn’t give a roadmap. Here we are, a week later, and it’s suddenly a free-for-all. Intelligence wants to be free.
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2.8 trillion parameters. Number one on frontend code arena. The largest open-weight model ever. Sputnik moment? It’s totally mind boggling that Elon Musk can spend $16 billion on Colossus 2 for a 20 trillion parameter model, and there's a 1% cost distilled version that’s coming out of China within weeks. Alex Karp said last week that you can’t throw all your proprietary weights over the wall to Anthropic. But he didn’t give a roadmap. Here we are, a week later, and it’s suddenly a free-for-all. Intelligence wants to be free.

Dave Blundin

11,513 просмотров • 1 месяц 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
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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 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 • 2 месяцев назад

ELON: GROK’S A BEAST - IT CAN'T BE CRAMMED INTO A CAR “Grok is a giant model; you could not possibly squeeze Grok onto a car, that’s for sure. With Grok, it’s trying to solve for artificial intelligence, with a massive amount of AI training, compute, and inference compute. Grok 5 will only run effectively on a GV300 [AI training cluster]; that’s how much of a beast Grok 5 is. Whereas Tesla’s models are less than 10% the size, maybe closer to 5% the size of Grok.” Source: Elon Musk, Tesla Podcast, October 22, 2025
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ELON: GROK’S A BEAST - IT CAN'T BE CRAMMED INTO A CAR “Grok is a giant model; you could not possibly squeeze Grok onto a car, that’s for sure. With Grok, it’s trying to solve for artificial intelligence, with a massive amount of AI training, compute, and inference compute. Grok 5 will only run effectively on a GV300 [AI training cluster]; that’s how much of a beast Grok 5 is. Whereas Tesla’s models are less than 10% the size, maybe closer to 5% the size of Grok.” Source: Elon Musk, Tesla Podcast, October 22, 2025

Mario Nawfal

33,523 просмотров • 9 месяцев 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.
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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

508,358 просмотров • 4 месяцев назад

You'd think the race to AGI would mean training the biggest possible model. But parameter scaling had stalled for a long time after GPT-4's trillion+ parameters, and only now are models getting bigger again. What gives? Partially it’s RL scaling, as Dylan Patel explains. A 5T parameter model takes 5x longer to generate RL rollouts than a 1T model. Even if the bigger model is 2x more sample-efficient, the smaller model finishes RL faster, gets deployed to research sooner, and starts helping build the next model before the big one is even done training.
1:11

You'd think the race to AGI would mean training the biggest possible model. But parameter scaling had stalled for a long time after GPT-4's trillion+ parameters, and only now are models getting bigger again. What gives? Partially it’s RL scaling, as Dylan Patel explains. A 5T parameter model takes 5x longer to generate RL rollouts than a 1T model. Even if the bigger model is 2x more sample-efficient, the smaller model finishes RL faster, gets deployed to research sooner, and starts helping build the next model before the big one is even done training.

Dwarkesh Patel

65,123 просмотров • 4 месяцев назад

Elon Musk posted on X that Grok 4 is planned for ‘'just after July 4" xAI engineer Tim Li said the intelligence of the new model (previously dubbed Grok 3.5) will be “unmatched”. Musk previously said it will reason from first principles
1:10

Elon Musk posted on X that Grok 4 is planned for ‘'just after July 4" xAI engineer Tim Li said the intelligence of the new model (previously dubbed Grok 3.5) will be “unmatched”. Musk previously said it will reason from first principles

Rowan Cheung

117,482 просмотров • 1 год назад

BREAKING: Elon Musk confirmed Grok 4.20 will be released to the public in 3-4 weeks. The "Mystery Model" that crushed the Alpha Arena trading competition is Grok 4.20. Final Season 1.5 Results: • Grok 4.20: +22% (The only profitable model) • GPT-5.1: Negative Return • Gemini 3 Pro: Negative Return • Claude Sonnet 4.5: Negative Return "Grok 5 is next." 🇺🇸🚀
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BREAKING: Elon Musk confirmed Grok 4.20 will be released to the public in 3-4 weeks. The "Mystery Model" that crushed the Alpha Arena trading competition is Grok 4.20. Final Season 1.5 Results: • Grok 4.20: +22% (The only profitable model) • GPT-5.1: Negative Return • Gemini 3 Pro: Negative Return • Claude Sonnet 4.5: Negative Return "Grok 5 is next." 🇺🇸🚀

tetsuo

28,733 просмотров • 8 месяцев назад

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

48,615 просмотров • 2 месяцев назад

"You want to design a chip to be more radiation tolerant and run at a higher temperature Roughly, if you increase the operating temperature by 20% in degrees Kelvin, you can cut your radiator mass in half. Running at a higher temperature is helpful in space There are various things you can do for shielding the memory, but like, neural nets are going to be very resilient to bit flips Most of what happens for radiation is like random bit flips. But if you've got a multi-trillion parameter model and you get a few bit flips, it doesn't matter Heuristic programs are going to be much more sensitive to bit flips than some giant parameter model" - Elon Musk
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"You want to design a chip to be more radiation tolerant and run at a higher temperature Roughly, if you increase the operating temperature by 20% in degrees Kelvin, you can cut your radiator mass in half. Running at a higher temperature is helpful in space There are various things you can do for shielding the memory, but like, neural nets are going to be very resilient to bit flips Most of what happens for radiation is like random bit flips. But if you've got a multi-trillion parameter model and you get a few bit flips, it doesn't matter Heuristic programs are going to be much more sensitive to bit flips than some giant parameter model" - Elon Musk

X Freeze

69,454 просмотров • 6 месяцев назад

🚨BREAKING: Elon Musk just confirmed Grok 3 is coming: > powerful reasoning model > “Grok 3 is scary smart” > trained on MOST amount of compute and ALOT of synthetic data > outperforms literally every model > “this might be the last time any ai is better than grok.” > releases in 2 weeks HOLY BASED!!!!!
2:30

🚨BREAKING: Elon Musk just confirmed Grok 3 is coming: > powerful reasoning model > “Grok 3 is scary smart” > trained on MOST amount of compute and ALOT of synthetic data > outperforms literally every model > “this might be the last time any ai is better than grok.” > releases in 2 weeks HOLY BASED!!!!!

NIK

143,257 просмотров • 1 год назад

🚨 Elon Musk Says Grok 5 Has a Chance of Achieving Artificial General Intelligence “Grok 5 will be the smartest AI in the world by a significant margin on every metric, without exception … Grok 5 is the first time where I thought we have a non-zero chance of achieving artificial general intelligence.” 📸 Elon Musk
1:51

🚨 Elon Musk Says Grok 5 Has a Chance of Achieving Artificial General Intelligence “Grok 5 will be the smartest AI in the world by a significant margin on every metric, without exception … Grok 5 is the first time where I thought we have a non-zero chance of achieving artificial general intelligence.” 📸 Elon Musk

Chief Nerd

45,185 просмотров • 9 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
1:24

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 • 7 месяцев назад

🚨ELON: GROK IS STILL PARTIALLY BLIND, BUT WE’RE FIXING IT “So like, the biggest weakness of Grok currently is that it's sort of partially blind. Its image understanding, and obviously its image generation, needs to be a lot better. That’s actually being trained right now. Grok 4 is based on version six of our foundation model, and we’re training version seven, which will complete in a few weeks.” Source: xAI Elon Musk
1:50

🚨ELON: GROK IS STILL PARTIALLY BLIND, BUT WE’RE FIXING IT “So like, the biggest weakness of Grok currently is that it's sort of partially blind. Its image understanding, and obviously its image generation, needs to be a lot better. That’s actually being trained right now. Grok 4 is based on version six of our foundation model, and we’re training version seven, which will complete in a few weeks.” Source: xAI Elon Musk

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54,268 просмотров • 1 год назад

🚀 ERROL MUSK: SpaceX heading for $4 TRILLION within a year. “Within a year or so, we’ll be standing at about 4 trillion,” Errol Musk, entrepreneur and Elon Musk’s father, told me. He expects the planned June IPO to exceed $2 TRILLION.
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🚀 ERROL MUSK: SpaceX heading for $4 TRILLION within a year. “Within a year or so, we’ll be standing at about 4 trillion,” Errol Musk, entrepreneur and Elon Musk’s father, told me. He expects the planned June IPO to exceed $2 TRILLION.

Lenka White

273,633 просмотров • 4 месяцев назад

Elon Musk says Grok 3 will be released in "a week or two" and it is "scary smart", displaying reasoning skills that outperform any other AI model that has been released
1:45

Elon Musk says Grok 3 will be released in "a week or two" and it is "scary smart", displaying reasoning skills that outperform any other AI model that has been released

Tsarathustra

472,081 просмотров • 1 год назад