ARK Invest has released their highly anticipated "Big Ideas 2025" report, outlining a future where technological convergence drives unprecedented economic growth and innovation.
Here's what you need to know:
The report identifies five key innovation platforms that are evolving and converging:
- AI
- Robotics
- Energy Storage
- Public Blockchains
- Multiomics Sequencing
Economic Impact
ARK projects global GDP growth could reach 7.3% by 2030, significantly higher than the IMF's forecast of 3.1%. Disruptive innovation companies could command more than two-thirds of global equity markets, with market value expanding beyond the current tech giants.
Key Technology Trends
1. AI Agents
- OpenAI projected to surpass $10B in revenue by 2025
- AI-mediated advertising could capture 54% of the $1.1T digital ad market by 2030
- AI agents revolutionizing consumer interaction and business workflows
2. Bitcoin & Digital Assets
- Bitcoin reached new all-time highs in 2024
- Spot Bitcoin ETFs marked the most successful ETF launch in history
- 2030 price targets: $300,000 (bear case) to $1.5M (bull case)
- Stablecoins overtook Mastercard and Visa in transaction value
3. Autonomous Vehicles
- Robotaxis could reduce transport costs to $0.25 per mile
- Market potential of $34T by 2030
- Tesla and Waymo leading commercial deployment
4. Energy & Infrastructure
- AI computing driving unprecedented energy demand
- Nuclear energy gaining renewed attention
- Battery costs continuing to decline
- Renewable energy integration accelerating
5. Robotics & Manufacturing
- Humanoid robots entering commercial use
- $26T+ global revenue opportunity
- 3D printing market growing 40% annually
- Manufacturing becoming increasingly automated
6. Healthcare Innovation
- AI dramatically reducing drug development costs
- DNA sequencing costs falling faster than Moore's Law
- Precision medicine becoming mainstream
- Multi-cancer screening revolutionizing diagnostics
Here's what you need to know:
The report identifies five key innovation platforms that are evolving and converging:
- AI
- Robotics
- Energy Storage
- Public Blockchains
- Multiomics Sequencing
Economic Impact
ARK projects global GDP growth could reach 7.3% by 2030, significantly higher than the IMF's forecast of 3.1%. Disruptive innovation companies could command more than two-thirds of global equity markets, with market value expanding beyond the current tech giants.
Key Technology Trends
1. AI Agents
- OpenAI projected to surpass $10B in revenue by 2025
- AI-mediated advertising could capture 54% of the $1.1T digital ad market by 2030
- AI agents revolutionizing consumer interaction and business workflows
2. Bitcoin & Digital Assets
- Bitcoin reached new all-time highs in 2024
- Spot Bitcoin ETFs marked the most successful ETF launch in history
- 2030 price targets: $300,000 (bear case) to $1.5M (bull case)
- Stablecoins overtook Mastercard and Visa in transaction value
3. Autonomous Vehicles
- Robotaxis could reduce transport costs to $0.25 per mile
- Market potential of $34T by 2030
- Tesla and Waymo leading commercial deployment
4. Energy & Infrastructure
- AI computing driving unprecedented energy demand
- Nuclear energy gaining renewed attention
- Battery costs continuing to decline
- Renewable energy integration accelerating
5. Robotics & Manufacturing
- Humanoid robots entering commercial use
- $26T+ global revenue opportunity
- 3D printing market growing 40% annually
- Manufacturing becoming increasingly automated
6. Healthcare Innovation
- AI dramatically reducing drug development costs
- DNA sequencing costs falling faster than Moore's Law
- Precision medicine becoming mainstream
- Multi-cancer screening revolutionizing diagnostics
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State of AI: China - Key Insights from Q1 2025
While the US maintains an overall lead in the intelligence frontier, China is no longer far behind.
1. Closing the AI Gap
- Chinese AI models have rapidly caught up to US capabilities
- DeepSeek R1 achieved 89 on Intelligence Index, approaching OpenAI's o3 (94)
- Multiple Chinese models now match frontier US model performance
2. Leading Players & Performance
Top Chinese Models:
- DeepSeek R1: 89
- Qwen 2.5 Max (Alibaba): 79
- DeepSeek V3: 80
Market Leaders:
- Big Tech: Alibaba, Baidu, ByteDance, Tencent
- Rising Stars: DeepSeek, MoonShot AI, Zhipu
3. Export Control Impact
- US restrictions on high-end AI chips continue
- NVIDIA H100 (989 TFLOPs) banned for export
- China-approved NVIDIA H20 limited to 148 TFLOPs
4. Investment & Scale
Major Funding:
- MoonShot AI: $1.67B
- Zhipu: $1.12B
- Baichuan: $1.04B
5. Emerging Trends
- Focus on reasoning capabilities
- Open-source model development
- Multiple companies releasing frontier-level models
While the US maintains an overall lead in the intelligence frontier, China is no longer far behind.
1. Closing the AI Gap
- Chinese AI models have rapidly caught up to US capabilities
- DeepSeek R1 achieved 89 on Intelligence Index, approaching OpenAI's o3 (94)
- Multiple Chinese models now match frontier US model performance
2. Leading Players & Performance
Top Chinese Models:
- DeepSeek R1: 89
- Qwen 2.5 Max (Alibaba): 79
- DeepSeek V3: 80
Market Leaders:
- Big Tech: Alibaba, Baidu, ByteDance, Tencent
- Rising Stars: DeepSeek, MoonShot AI, Zhipu
3. Export Control Impact
- US restrictions on high-end AI chips continue
- NVIDIA H100 (989 TFLOPs) banned for export
- China-approved NVIDIA H20 limited to 148 TFLOPs
4. Investment & Scale
Major Funding:
- MoonShot AI: $1.67B
- Zhipu: $1.12B
- Baichuan: $1.04B
5. Emerging Trends
- Focus on reasoning capabilities
- Open-source model development
- Multiple companies releasing frontier-level models
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Google showed a very cool commercial for its Gemini AI chatbot
According to the plot of the video, a man with a Google Pixel 9 in his hands asks Gemini to help him prepare for an interview. Talking about himself, cute family shots from his past appear in the video.
At the end of the video, Gemini says that the hero is ready and the man joins the call with future employers.
According to the plot of the video, a man with a Google Pixel 9 in his hands asks Gemini to help him prepare for an interview. Talking about himself, cute family shots from his past appear in the video.
At the end of the video, Gemini says that the hero is ready and the man joins the call with future employers.
By the way, this video will be shown at the Super Bowl, which will be held on the night of February 9th to 10th.
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Prime intellect introduced SYNTHETIC-1: Collaboratively generating the largest synthetic dataset of verified reasoning traces for math, coding and science using DeepSeek-R1.
SYNTHETIC-1:
- 1.4 million high-quality tasks & verifiers
- Public synthetic data run - allowing anyone to contribute compute
- GENESYS: open, extendable synthetic data generation framework + call for crowdsourcing tasks & verifiers.
SYNTHETIC-1:
- 1.4 million high-quality tasks & verifiers
- Public synthetic data run - allowing anyone to contribute compute
- GENESYS: open, extendable synthetic data generation framework + call for crowdsourcing tasks & verifiers.
www.primeintellect.ai
SYNTHETIC-1: Scaling Distributed Synthetic Data Generation for Verified Reasoning
Today, we are excited to introduce SYNTHETIC-1, a collaborative effort to create the largest open-source dataset of verified reasoning traces for math, coding and science, leveraging DeepSeek-R1. Our dataset consists of 1.4 million high-quality tasks and…
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Figure AI to produce over 100,000 humanoid robots by 2029 👋
Figure AI has signed a second contract for the supply of robots of its own production, the client being an unnamed large company from the USA.
Thus, by 2029, Figure AI should produce 100,000 humanoid robots.
The robots are expected to be used to replace physical labor in manufacturing.
Figure AI has signed a second contract for the supply of robots of its own production, the client being an unnamed large company from the USA.
Thus, by 2029, Figure AI should produce 100,000 humanoid robots.
The robots are expected to be used to replace physical labor in manufacturing.
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We do voiceovers for videos — a model for creating audio from text, Kokoro v1.0, has been released.
Simple but high-quality audio tracks are created. The neural network works locally in the browser, so user data does not leak anywhere.
Test it for free here
Simple but high-quality audio tracks are created. The neural network works locally in the browser, so user data does not leak anywhere.
Test it for free here
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A new neural network for image generation has appeared on the network — Loras[.]dev. AI creates anime picchis, logos, icons, sketches, and even tarot cards. It's all free.
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HuggingFace released 8GB of high quality reasoning math
They temporarily commandeered the HF cluster to generate 1.2 million reasoning-filled solutions for 500,000 NuminaMath problems using the DeepSeek-R1 model.
This is significant for AI development because:
1. It creates a large dataset of mathematical reasoning examples
2. These solutions can be used to train future AI models
3. It demonstrates the current capabilities of AI in solving complex mathematical problems
Datasets.
They temporarily commandeered the HF cluster to generate 1.2 million reasoning-filled solutions for 500,000 NuminaMath problems using the DeepSeek-R1 model.
This is significant for AI development because:
1. It creates a large dataset of mathematical reasoning examples
2. These solutions can be used to train future AI models
3. It demonstrates the current capabilities of AI in solving complex mathematical problems
Datasets.
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OpenAI roadmap update for GPT-4.5 and GPT-5
Sam Altman just dropped major news about the future of their AI development. The company is making a dramatic shift toward unified intelligence with GPT-4.5 and GPT-5.
Key highlights from the announcement:
• GPT-4.5 (codenamed "Orion") will be their final traditional model before a complete system overhaul
• GPT-5 represents a groundbreaking merger of all OpenAI technologies, including o3, marking the end of standalone models
• The most surprising part? Free ChatGPT users will get unlimited access to GPT-5's standard intelligence setting
They're moving toward what they call "magic unified intelligence" - essentially making AI that "just works" without users needing to understand the technical details.
Premium features for Plus and Pro users will include enhanced intelligence levels plus access to voice, canvas, search, and deep research capabilities.
Sam Altman just dropped major news about the future of their AI development. The company is making a dramatic shift toward unified intelligence with GPT-4.5 and GPT-5.
Key highlights from the announcement:
• GPT-4.5 (codenamed "Orion") will be their final traditional model before a complete system overhaul
• GPT-5 represents a groundbreaking merger of all OpenAI technologies, including o3, marking the end of standalone models
• The most surprising part? Free ChatGPT users will get unlimited access to GPT-5's standard intelligence setting
They're moving toward what they call "magic unified intelligence" - essentially making AI that "just works" without users needing to understand the technical details.
Premium features for Plus and Pro users will include enhanced intelligence levels plus access to voice, canvas, search, and deep research capabilities.
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GREEN is a new lightweight neural network designed to how we analyze brain activity
GREEN combines two powerful approaches:
1. Wavelet-based frequency filtering to capture dynamic brain rhythms
2. Riemannian geometry to decode spatial patterns in EEG signals
EEG data holds secrets to brain health, cognitive function, and aging—but traditional methods often miss nuanced signals. GREEN changes the game by:
- Detecting subtle brain activity changes with exceptional sensitivity
- Operating on a low computational budget (ideal for real-world applications!)
- Delivering interpretable outputs to link findings to brain mechanisms.
GitHub.
GREEN combines two powerful approaches:
1. Wavelet-based frequency filtering to capture dynamic brain rhythms
2. Riemannian geometry to decode spatial patterns in EEG signals
EEG data holds secrets to brain health, cognitive function, and aging—but traditional methods often miss nuanced signals. GREEN changes the game by:
- Detecting subtle brain activity changes with exceptional sensitivity
- Operating on a low computational budget (ideal for real-world applications!)
- Delivering interpretable outputs to link findings to brain mechanisms.
GitHub.
Patterns
GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals
Our brains generate electrical signals whose unique patterns reveal information about
aging, health, and even thoughts. This information is useful for studying brain disorders
and developing novel therapies. However, traditional analysis methods can miss…
aging, health, and even thoughts. This information is useful for studying brain disorders
and developing novel therapies. However, traditional analysis methods can miss…
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OpenAI released its Reasoning model best practices
1. Use delimiters for clarity: Use delimiters like markdown, XML tags, and section titles to clearly indicate distinct parts of the input, helping the model interpret different sections appropriately.
2. The guide differentiates between reasoning models (e.g., o1, o3-mini) and GPT models (e.g., GPT-4o).
Reasoning models are built for complex, multi-step tasks—such as planning, detailed document analysis, and visual interpretation—while GPT models focus on speed and cost efficiency for well-defined tasks.
- In practice, reasoning models excel at clarifying ambiguous prompts, extracting key details from extensive unstructured data, and performing multi-step planning or code review.
- They are best used with clear, concise prompts that include explicit constraints and delimiters;
- elaborate chain-of-thought instructions are unnecessary since these models reason internally.
1. Use delimiters for clarity: Use delimiters like markdown, XML tags, and section titles to clearly indicate distinct parts of the input, helping the model interpret different sections appropriately.
2. The guide differentiates between reasoning models (e.g., o1, o3-mini) and GPT models (e.g., GPT-4o).
Reasoning models are built for complex, multi-step tasks—such as planning, detailed document analysis, and visual interpretation—while GPT models focus on speed and cost efficiency for well-defined tasks.
- In practice, reasoning models excel at clarifying ambiguous prompts, extracting key details from extensive unstructured data, and performing multi-step planning or code review.
- They are best used with clear, concise prompts that include explicit constraints and delimiters;
- elaborate chain-of-thought instructions are unnecessary since these models reason internally.
Openai
Reasoning best practices | OpenAI API
Explore best practices for using o-series reasoning models, like o1 and o3-mini, vs. GPT models—including use cases, how to choose a model, and prompting guidance.
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Google DeepMind released a short course on AGI safety
The course offers a concise and accessible introduction to AI alignment problems and our technical & governance approaches, consisting of short recorded talks and exercises (75 minutes total).
The course offers a concise and accessible introduction to AI alignment problems and our technical & governance approaches, consisting of short recorded talks and exercises (75 minutes total).
Medium
Introducing our short course on AGI safety
We are excited to release a short course on AGI safety for students, researchers and professionals interested in this topic. The course…
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DeepSeek introduced CodeI/O, a new method that helps AI learn reasoning patterns hidden in code
Models train to predict inputs and outputs of given code, all while explaining its reasoning with Chain-of-Thought (CoT) in natural language.
CodeI/O improves models' general reasoning skills, such as:
- planning steps logically
- searching for solutions
- breaking problems into smaller parts
DeepSeek gathered over 810,000 Python code files from different sources to cover a big variety of reasoning styles, like puzzles, math problems, etc.
Then they cleaned and structured it into a unified format using DeepSeek-V2.5, ran the code and collected input-output pairs.
CODEI/O++: Improving training with multi-step feedback
It's an improved dataset where the model learns not just from its successes but also from its mistakes. To create it, researchers used a feedback loop:
- If the model gets an answer wrong, it's told why it was wrong and tries again.
- If the function fails to run, researchers include that feedback too.
- The model then revises its response and tries again.
This extra training step makes models even more accurate.
Data and models.
Models train to predict inputs and outputs of given code, all while explaining its reasoning with Chain-of-Thought (CoT) in natural language.
CodeI/O improves models' general reasoning skills, such as:
- planning steps logically
- searching for solutions
- breaking problems into smaller parts
DeepSeek gathered over 810,000 Python code files from different sources to cover a big variety of reasoning styles, like puzzles, math problems, etc.
Then they cleaned and structured it into a unified format using DeepSeek-V2.5, ran the code and collected input-output pairs.
CODEI/O++: Improving training with multi-step feedback
It's an improved dataset where the model learns not just from its successes but also from its mistakes. To create it, researchers used a feedback loop:
- If the model gets an answer wrong, it's told why it was wrong and tries again.
- If the function fails to run, researchers include that feedback too.
- The model then revises its response and tries again.
This extra training step makes models even more accurate.
Data and models.
arXiv.org
CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction
Reasoning is a fundamental capability of Large Language Models. While prior research predominantly focuses on enhancing narrow skills like math or code generation, improving performance on many...
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xAI announces Grok 3. Here is everything you need to know
Elon mentioned that Grok 3 is an order of magnitude more capable than Grok 2.
Total GPUs: 200K
The capacity was doubled in 92 days!
All of this compute was used to improve Grok -- which has lead to Grok 3.
Grok 3 involved 10x more training than Grok 2!
Grok finished pretraining in early January!
The model is still training.
Here are the benchmark numbers:
Grok 3 significantly outperforms other models in its category such as Gemini 2 Pro and GPT-4o. Even Grok-3 mini shows to be competitive.
Results of early Grok 3 in the Chatbot Arena (LMSYS)
It reached an Elo score of 1400 which no other model has achieved.
The model score keeps improving.
Grok 3 also has reasoning capabilities too!
The Grok team has been testing these capabilities which they have unlocked using RL.
The model is good, especially in coding.
Grok 3 coding example:
Thinking traces as generated as the model tries to solve the problem.
Elon confirmed that the thinking steps have been obscured to avoid getting copied.
Grok 3 also excels at creative coding like generating creative and novel games.
Elon emphasized Grok 3's creative emergent capabilities.
You can also use the Big Brain mode to use more compute and reasoning with Grok 3.
Grok 3 Reasoning performance:
The results correspond to the beta version of Grok-3 Reasoning.
It outperforms o1 and DeepSeek-R1 when given more test-time compute (allowing it to think longer).
The Grok 3 mini reasoning model is also very capable.
Grok 3 Reasoning Beta performance on AIME 2025.
Grok 3 shows generalization capabilities.
It not only does coding and math problem-solving, but it can also do other creative and useful real-world tasks.
One of the results generated with Grok 3 mini.
Bejeweled Tetris generated by Grok 3.
Grok 3 cannot only unlock test-time compute, it also enables capable agents.
These capabilities have led to a new product called DeepSearch.
"Next generation of search agents to understand the universe".
More on DeepSearch:
- the model can think deeply about user intent
- what facts to consider
- how many websites to browse
- it can cross-validate different sources.
DeepSearch also exposes the steps that it takes to conduct the search itself.
Improvements will happen rapidly and almost daily according to the team.
There is also a Grok-powered voice app coming too -- about a week away!
Open-source approach:
The last version will be open-sourced when the most recent version is fully out.
After Grok 3 stable version is out, it is highly likely Grok 2 will be open-sourced. (within a few months).
SuperGrok dedicated app is also available with a polished experience.
Try on the web as well: grok.com
The web will include the latest Grok features.
Elon mentioned that Grok 3 is an order of magnitude more capable than Grok 2.
Total GPUs: 200K
The capacity was doubled in 92 days!
All of this compute was used to improve Grok -- which has lead to Grok 3.
Grok 3 involved 10x more training than Grok 2!
Grok finished pretraining in early January!
The model is still training.
Here are the benchmark numbers:
Grok 3 significantly outperforms other models in its category such as Gemini 2 Pro and GPT-4o. Even Grok-3 mini shows to be competitive.
Results of early Grok 3 in the Chatbot Arena (LMSYS)
It reached an Elo score of 1400 which no other model has achieved.
The model score keeps improving.
Grok 3 also has reasoning capabilities too!
The Grok team has been testing these capabilities which they have unlocked using RL.
The model is good, especially in coding.
Grok 3 coding example:
Thinking traces as generated as the model tries to solve the problem.
Elon confirmed that the thinking steps have been obscured to avoid getting copied.
Grok 3 also excels at creative coding like generating creative and novel games.
Elon emphasized Grok 3's creative emergent capabilities.
You can also use the Big Brain mode to use more compute and reasoning with Grok 3.
Grok 3 Reasoning performance:
The results correspond to the beta version of Grok-3 Reasoning.
It outperforms o1 and DeepSeek-R1 when given more test-time compute (allowing it to think longer).
The Grok 3 mini reasoning model is also very capable.
Grok 3 Reasoning Beta performance on AIME 2025.
Grok 3 shows generalization capabilities.
It not only does coding and math problem-solving, but it can also do other creative and useful real-world tasks.
One of the results generated with Grok 3 mini.
Bejeweled Tetris generated by Grok 3.
Grok 3 cannot only unlock test-time compute, it also enables capable agents.
These capabilities have led to a new product called DeepSearch.
"Next generation of search agents to understand the universe".
More on DeepSearch:
- the model can think deeply about user intent
- what facts to consider
- how many websites to browse
- it can cross-validate different sources.
DeepSearch also exposes the steps that it takes to conduct the search itself.
Improvements will happen rapidly and almost daily according to the team.
There is also a Grok-powered voice app coming too -- about a week away!
Open-source approach:
The last version will be open-sourced when the most recent version is fully out.
After Grok 3 stable version is out, it is highly likely Grok 2 will be open-sourced. (within a few months).
SuperGrok dedicated app is also available with a polished experience.
Try on the web as well: grok.com
The web will include the latest Grok features.
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Vortex Next Gen Trends pinned «✅ ARE YOU LOOKING FOR QUALITY CRYPTO & AI TRAFFIC? WE ARE HERE TO HELP! Promote your Crypto, AI Tools, Apps, Exchanges, Play-to-Earn Games, NFTs, DeFi, Wallets, Tokens, Meme Coins, Telegram Bots, P2E, and more! Get a high-converting video review on our 5…»
Breakthrough in Robot Design: Universal Controllers Transform How We Build Robots?
Northwestern University researchers have made a significant breakthrough in robotics design, introducing a method that could revolutionize how we create and evolve robots.
Their paper "Accelerated co-design of robots through morphological pretraining" presents a novel approach that solves a decades-old challenge in robotics.
Code is coming soon.
And here are more robots
Key Innovations:
1. Universal Controller
- Developed a single controller that can work with multiple robot body types
- Pre-trained on millions of different robot morphologies
- Uses gradient-based optimization through differentiable simulation
- Can immediately adapt to new robot designs without extensive retraining
2. Zero-Shot Evolution
- Allows rapid testing of new robot body designs
- Enables immediate evaluation of design changes
- Supports successful recombination of robot parts
- Dramatically speeds up the design process
3. Diversity Maintenance
- Identified and solved "diversity collapse" - a previously unknown problem in robot co-design
- Developed methods to maintain morphological diversity while improving performance
- Enabled successful crossover between different robot designs
Technical Details:
- Controllers are trained on over 10 million distinct robot morphologies
- Uses differentiable simulation for gradient-based optimization
- Supports complex 3D environments with varying terrains
- Enables robots to perform adaptive behaviors like phototaxis (movement toward light)
Future Implications:
- Could dramatically accelerate robot design and development
- Opens new possibilities for self-reconfigurable robots
- Provides a framework for more complex multi-material robots
- May help bridge the simulation-to-reality gap in robotics
Northwestern University researchers have made a significant breakthrough in robotics design, introducing a method that could revolutionize how we create and evolve robots.
Their paper "Accelerated co-design of robots through morphological pretraining" presents a novel approach that solves a decades-old challenge in robotics.
Code is coming soon.
And here are more robots
Key Innovations:
1. Universal Controller
- Developed a single controller that can work with multiple robot body types
- Pre-trained on millions of different robot morphologies
- Uses gradient-based optimization through differentiable simulation
- Can immediately adapt to new robot designs without extensive retraining
2. Zero-Shot Evolution
- Allows rapid testing of new robot body designs
- Enables immediate evaluation of design changes
- Supports successful recombination of robot parts
- Dramatically speeds up the design process
3. Diversity Maintenance
- Identified and solved "diversity collapse" - a previously unknown problem in robot co-design
- Developed methods to maintain morphological diversity while improving performance
- Enabled successful crossover between different robot designs
Technical Details:
- Controllers are trained on over 10 million distinct robot morphologies
- Uses differentiable simulation for gradient-based optimization
- Supports complex 3D environments with varying terrains
- Enables robots to perform adaptive behaviors like phototaxis (movement toward light)
Future Implications:
- Could dramatically accelerate robot design and development
- Opens new possibilities for self-reconfigurable robots
- Provides a framework for more complex multi-material robots
- May help bridge the simulation-to-reality gap in robotics
Google
co-design mpt
Abstract
The co-design of robot morphology and neural control typically requires using reinforcement learning to approximate a unique control policy gradient for each body plan, demanding massive amounts of training data to measure the performance of each…
The co-design of robot morphology and neural control typically requires using reinforcement learning to approximate a unique control policy gradient for each body plan, demanding massive amounts of training data to measure the performance of each…
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HuggingFace released the "Ultra-Scale Playbook"
A free, open-source, book to learn everything about 5D parallelism, ZeRO, fast CUDA kernels, how and why overlap compute & communication – all scaling bottlenecks and tools introduced with motivation, theory, interactive plots from our 4000+ scaling experiments and even NotebookLM podcasters to tag along with you.
- How was DeepSeek trained for $5M only?
- Why did Mistral trained an MoE?
- Why is PyTorch native Data Parallelism implementation so complex under the hood?
- What are all the parallelism techniques and why were they invented?
- Should I use ZeRO-3 or Pipeline Parallelism when scaling and what's the story behind both techniques?
- What is this Context Parallelism that Meta used to train Llama 3? Is it different from Sequence Parallelism?
- What is FP8? how does it compares to BF16?
The largest factor for democratizing AI will always be teaching everyone how to build AI and in particular how to create, train and fine-tune high performance models. In other word making accessible to everybody the techniques that power all recent large language models and efficient training is possibly one of the most essential of them.
A free, open-source, book to learn everything about 5D parallelism, ZeRO, fast CUDA kernels, how and why overlap compute & communication – all scaling bottlenecks and tools introduced with motivation, theory, interactive plots from our 4000+ scaling experiments and even NotebookLM podcasters to tag along with you.
- How was DeepSeek trained for $5M only?
- Why did Mistral trained an MoE?
- Why is PyTorch native Data Parallelism implementation so complex under the hood?
- What are all the parallelism techniques and why were they invented?
- Should I use ZeRO-3 or Pipeline Parallelism when scaling and what's the story behind both techniques?
- What is this Context Parallelism that Meta used to train Llama 3? Is it different from Sequence Parallelism?
- What is FP8? how does it compares to BF16?
The largest factor for democratizing AI will always be teaching everyone how to build AI and in particular how to create, train and fine-tune high performance models. In other word making accessible to everybody the techniques that power all recent large language models and efficient training is possibly one of the most essential of them.
huggingface.co
The Ultra-Scale Playbook - a Hugging Face Space by nanotron
The ultimate guide to training LLM on large GPU Clusters
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Wow, DeepSeek announced Day 0: Warming up for OpenSourceWeek
Starting next week, they'll be open-sourcing 5 repos, sharing sincere progress with full transparency.
These humble building blocks in their online service have been documented, deployed and battle-tested in production.
Daily unlocks are coming soon. No ivory towers - just pure garage-energy and community-driven innovation.
Starting next week, they'll be open-sourcing 5 repos, sharing sincere progress with full transparency.
These humble building blocks in their online service have been documented, deployed and battle-tested in production.
Daily unlocks are coming soon. No ivory towers - just pure garage-energy and community-driven innovation.
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