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GROK 5. the first 7 trillion parameter model

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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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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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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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The Grok 5 model has a massive 6 trillion parameters plus much higher intelligence density. 2026 is going to be exciting! ๐Ÿง  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! ๐Ÿง  Thanks Elon Musk !

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Its here ! Grok 4.5 is now the model being used for Expert and Heavy modes on Grok Web and Apps. Using Grok Heavy now shows 4 agents using Grok v9 aka Grok 4.5 where it previously used 16 agents with the older models. Now we can use SpaceXAI's latest 1.5T parameter model anywhere!
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Its here ! Grok 4.5 is now the model being used for Expert and Heavy modes on Grok Web and Apps. Using Grok Heavy now shows 4 agents using Grok v9 aka Grok 4.5 where it previously used 16 agents with the older models. Now we can use SpaceXAI's latest 1.5T parameter model anywhere!

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โšก๏ธ 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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โšก๏ธ 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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At the World AI Conference, Moonshot AI debuted Kimi K3 โ€” a 2.8 trillionโ€‘parameter model that rivals Anthropic and OpenAI. Bloombergโ€™s Minmin Low breaks down what this means for the global AI race:
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At the World AI Conference, Moonshot AI debuted Kimi K3 โ€” a 2.8 trillionโ€‘parameter model that rivals Anthropic and OpenAI. Bloombergโ€™s Minmin Low breaks down what this means for the global AI race:

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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
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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

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This is the parameter group I use for the model. #Live2DWIP #Live2D
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This is the parameter group I use for the model. #Live2DWIP #Live2D

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NVIDIA JUST BECAME THE FIRST $5 TRILLION COMPANY IN HUMAN HISTORY
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Positron AI wants to run 16 trillion parameter models on a single server.
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Positron AI wants to run 16 trillion parameter models on a single server.

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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 itโ€™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.
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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 itโ€™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.

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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:
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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:

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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.
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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

508,540 Aufrufe โ€ข vor 6 Monaten

Grok now has the massive token usage on OpenRouter, holding 3 of the top 5 spots and processing over 7.5 trillion tokens in just one month
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Grok now has the massive token usage on OpenRouter, holding 3 of the top 5 spots and processing over 7.5 trillion tokens in just one month

X Freeze

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SpaceXAI just released the best 1-hour course on Grok Bot agents: from single prompt to a full 24/7 system 5:56 - Build your first agent 13:13 - Build the CEO 30:00 - Run bots 24/7 37:07 - Secret Grok Bot skills 44:46 - Full autonomous system Free, the best thing on Grok Bot agents I've come across. Watch it, then read the full Grok Bot breakdown below
52:32

SpaceXAI just released the best 1-hour course on Grok Bot agents: from single prompt to a full 24/7 system 5:56 - Build your first agent 13:13 - Build the CEO 30:00 - Run bots 24/7 37:07 - Secret Grok Bot skills 44:46 - Full autonomous system Free, the best thing on Grok Bot agents I've come across. Watch it, then read the full Grok Bot breakdown below

Mahax

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SpaceXAI just dropped the best 1-hour course on Grok Bot agents: from one prompt to a full 24/7 system 5:56 - Your first agent 13:13 - Build the CEO 30:00 - Run bots 24/7 37:07 - Secret Grok Bot skills 44:46 - Full autonomous system Free, the best thing on Grok Bot agents I've come across. Watch it, then read the full Grok Bot breakdown below
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SpaceXAI just dropped the best 1-hour course on Grok Bot agents: from one prompt to a full 24/7 system 5:56 - Your first agent 13:13 - Build the CEO 30:00 - Run bots 24/7 37:07 - Secret Grok Bot skills 44:46 - Full autonomous system Free, the best thing on Grok Bot agents I've come across. Watch it, then read the full Grok Bot breakdown below

Mahax

25,280 Aufrufe โ€ข vor 1 Monat

"One of the biggest misconceptions" Cerebras CFO Bob Komin pushes back on the small-models narrative. "We serve all models, and there is no limit to the size of the models that we can serve. Today, we're serving trillion parameter models. We're serving trillion parameter models that are internal for OpenAI today. We are currently running OpenAI 5.4 and 5.5 with them."
0:50

"One of the biggest misconceptions" Cerebras CFO Bob Komin pushes back on the small-models narrative. "We serve all models, and there is no limit to the size of the models that we can serve. Today, we're serving trillion parameter models. We're serving trillion parameter models that are internal for OpenAI today. We are currently running OpenAI 5.4 and 5.5 with them."

Deirdre Bosa

84,608 Aufrufe โ€ข vor 5 Monaten

We bring in $5 trillion. We spend $7 trillion. We borrow the difference. The Fed prints money to cover it. That's inflation. And inflation is making you poorer.
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We bring in $5 trillion. We spend $7 trillion. We borrow the difference. The Fed prints money to cover it. That's inflation. And inflation is making you poorer.

Rand Paul

784,314 Aufrufe โ€ข vor 6 Monaten

Leaked scenes from my conversation with a 3B parameter model.
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Leaked scenes from my conversation with a 3B parameter model.

Hugging Models

68,650 Aufrufe โ€ข vor 2 Monaten

Day 239. The model that predicted 7 bank failures just flagged private credit. $1.7 trillion. Same language pattern. Same trajectory. Thread below.
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Day 239. The model that predicted 7 bank failures just flagged private credit. $1.7 trillion. Same language pattern. Same trajectory. Thread below.

Eric Jackson

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