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video: https://youtu.be/tmrQWp8L48E
Le nouveau modèle de Chatgpt Cerebras , qui révolutionne l'intelligence artificielle avec une puissance multipliée par 1000. Cette avancée technologique est rendue possible grâce aux progrès réalisés dans le domaine des chip et des gpu, notamment avec les modèles de Nvidia et Amd. Les systèmes de Cerebras, tels que le wafer scale engine, jouent également un rôle clé dans le développement de cette technologie. Le marché boursier est également impacté, avec des conséquences sur les actions de Nvidia, Amd et Intel. Les prévisions pour les actions de Nvidia et Amd sont sujettes à débat, avec certains investisseurs se demandant s'il faut acheter ou vendre. L'intelligence artificielle et l'automatisation de l'AI sont en plein essor, avec des entreprises comme IBM et Meta qui investissent massivement dans ces technologies. Le nouveau modèle de Chatgpt oss 20 est conçu pour fonctionner avec les dernières générations de gpu, telles que la Nvidia Rtx 5070 et la Rtx 5090, et devrait être compatible avec les futurs processeurs Intel Panther Lake. Les systèmes de Cerebras et les technologies de wafer scale devraient également jouer un rôle important dans le développement de cette technologie en agent IA et automatisation. Découvrez comment cette avancée va changer le visage de l'intelligence artificielle et du marché boursier, et comment les entreprises comme Nvidia, Amd et Intel sont prêtes à relever le défi.
🚀 N°1 FORMATION IA & BUSINESS
👉 https://parlonsia.teachizy.fr
📩 Email : formation.ai87@gmail.com
🧠 Ce que je propose
Formations IA pour automatiser ton business
Création et mise en place d’agents IA pour ton activité
Accompagnement pour utiliser ChatGPT, Gemini & co sur tes vrais dossiers
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Site & formations : https://parlonsia.teachizy.fr
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🧪 Outils IA à tester
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Short AI : http://bit.ly/4lzE782
SEO Agent IA : https://urlr.me/P8AS5N
— 🎁 -25 % avec le code PARLONSIA25
Q : OpenAI utilise-t-il Cerebras pour ChatGPT ?
R : Oui, OpenAI a annoncé un accord pluriannuel avec Cerebras pour sécuriser de la capacité d’inference et diversifier ses gpu/chip. Objectif: plus de débit, moins de dépendance aux fournisseurs dominants.
Q : Cerebras: x1000, vrai ou marketing ?
R : Pas un “×1000” universel: les gains varient selon le modèle, le batch et la métrique (latence vs débit). Cerebras met plutôt en avant des écarts de l’ordre de quelques × sur des comparaisons tokens/s (ex: ~2700 vs ~900).
Q : C’est quoi le Wafer Scale Engine ?
R : C’est un chip “wafer-scale” (taille d’un wafer) conçu pour traiter
Le nouveau modèle de Chatgpt Cerebras , qui révolutionne l'intelligence artificielle avec une puissance multipliée par 1000. Cette avancée technologique est rendue possible grâce aux progrès réalisés dans le domaine des chip et des gpu, notamment avec les modèles de Nvidia et Amd. Les systèmes de Cerebras, tels que le wafer scale engine, jouent également un rôle clé dans le développement de cette technologie. Le marché boursier est également impacté, avec des conséquences sur les actions de Nvidia, Amd et Intel. Les prévisions pour les actions de Nvidia et Amd sont sujettes à débat, avec certains investisseurs se demandant s'il faut acheter ou vendre. L'intelligence artificielle et l'automatisation de l'AI sont en plein essor, avec des entreprises comme IBM et Meta qui investissent massivement dans ces technologies. Le nouveau modèle de Chatgpt oss 20 est conçu pour fonctionner avec les dernières générations de gpu, telles que la Nvidia Rtx 5070 et la Rtx 5090, et devrait être compatible avec les futurs processeurs Intel Panther Lake. Les systèmes de Cerebras et les technologies de wafer scale devraient également jouer un rôle important dans le développement de cette technologie en agent IA et automatisation. Découvrez comment cette avancée va changer le visage de l'intelligence artificielle et du marché boursier, et comment les entreprises comme Nvidia, Amd et Intel sont prêtes à relever le défi.
🚀 N°1 FORMATION IA & BUSINESS
👉 https://parlonsia.teachizy.fr
📩 Email : formation.ai87@gmail.com
🧠 Ce que je propose
Formations IA pour automatiser ton business
Création et mise en place d’agents IA pour ton activité
Accompagnement pour utiliser ChatGPT, Gemini & co sur tes vrais dossiers
🌐 Site & contenus
Site & formations : https://parlonsia.teachizy.fr
Chaîne YouTube : https://www.youtube.com/@IAExpliquee.x
Chaîne Dailymotion : https://dailymotion.com/formation.ai87
Podcast : https://spoti.fi/4dqZ3uO
Blog : https://medium.com/@flma1349/
📲 Réseaux & contact
X / Twitter : https://x.com/ParlonsIAx
TikTok : https://www.tiktok.com/@parlonsia
🧪 Outils IA à tester
Coder Agent IA : https://bit.ly/Coder_agentiA
Short AI : http://bit.ly/4lzE782
SEO Agent IA : https://urlr.me/P8AS5N
— 🎁 -25 % avec le code PARLONSIA25
Q : OpenAI utilise-t-il Cerebras pour ChatGPT ?
R : Oui, OpenAI a annoncé un accord pluriannuel avec Cerebras pour sécuriser de la capacité d’inference et diversifier ses gpu/chip. Objectif: plus de débit, moins de dépendance aux fournisseurs dominants.
Q : Cerebras: x1000, vrai ou marketing ?
R : Pas un “×1000” universel: les gains varient selon le modèle, le batch et la métrique (latence vs débit). Cerebras met plutôt en avant des écarts de l’ordre de quelques × sur des comparaisons tokens/s (ex: ~2700 vs ~900).
Q : C’est quoi le Wafer Scale Engine ?
R : C’est un chip “wafer-scale” (taille d’un wafer) conçu pour traiter
Catégorie
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TechnologieTranscription
00:00Are you going to lose your job in 2026? I'm going to give you 3 predictions for 2026 that will tell you what's going to happen.
00:08You have 3 signals to consider. Should you sell Nvidia now? Will the job market collapse in 2026?
00:14OpenAI has already started to model the doctor's profession, the doctor's profession completely, with ChatGPT.
00:21I'm not talking about the launch of ChatGPT Health, I'm talking about the system where doctors trained ChatGPT to be a medical assistant.
00:29And today, the only obstacle is whether your job is one of the 1350 tasks that have been modeled by OpenAI?
00:36OpenAI has just entered into a $10 billion partnership with Cerebras.
00:41Here's what this changes and why Nvidia is likely to be the stock you should sell off your hands.
00:46Andrew Feldman gave a lecture. Here's what he explained.
00:49Let's see how it goes.
00:50Now that it's over, applaud enthusiastically for at least 30 seconds for the experience I'm about to share.
00:59And there you have it, thank you.
01:01It is not simply a specific model, but an entire range of inferential and non-inferential models.
01:10Larger, smaller models, before Cerebrus had an output of 200 tokens per second.
01:17Thanks to our intervention, each model has been considerably optimized in terms of order of magnitude.
01:25At the very moment I am speaking to you, you do not know it, but ChatGPT is now capable, with instructions, of reproducing the behaviors of a doctor.
01:33But also that of a pharmacist, a bank employee, a real estate agent.
01:38All of this is written within the framework of ChatGPT's new capabilities.
01:43The upcoming version 6 will completely revolutionize the world of work.
01:47The only thing currently slowing down the speed of deployment is the computing speed.
01:51And that's where the partnership came in. The time it takes for the model to process the information to analyze chest X-rays will be about 10 minutes, patient.
02:00Very well, the entire GPU community, there are between 212 and 200 tokens generated every second, according to the analysis.
02:09Exactly. Where are we at the moment in terms of approach and progress?
02:12In truth, we were so far to the right that people had difficulty noticing us.
02:16That's why the orange arrow had to be added for clarification. Okay?
02:20Imagine that, starting from the same image, the result appears in just a few seconds.
02:25ChatGPT is capable of diagnosing, analyzing, reporting to me, and producing content for the medical field.
02:32That's exactly what we can already do with ChatGPT.
02:34But this operation will take me between 5 and 25 minutes.
02:37Imagine that now, we can reduce this time to the order of a second. That's what happened.
02:41I will now present Mistral, located on the far right, Anthropik, running on GPU in the central position and OpenAI, using the GPU on the left.
02:53Now, the human Mistral is much weaker.
02:56Their model is significantly smaller, but its reasoning ability is significantly accelerated thanks to Cerebrus.
03:02This is how you can compete with bigger and richer people by doing it faster.
03:13Honestly, I can't wait any longer to let you explore all of ChatGPT. Take about 50 seconds.
03:19First sign, Nvidia, should we sell Nvidia?
03:22Nvidia was the benchmark, the Oper H100 chip, the H200 chip, the benchmark on all DLL server systems.
03:31But today, this paradigm has a limit.
03:34The limit, the speed at which the models respond.
03:36That's when a new partner appeared, Cerebrus.
03:39What are we doing? We are working tirelessly on bottom-up engineering,
03:44fearlessly solving extremely difficult problems.
03:48Our approach began with the creation of the most imposing processor in the industry.
03:53To give you an idea, that little gas on the right is the B200.
03:57These are 208 billion insignificant transistors.
04:01The one on our left is of course our third-generation platen scale engine.
04:05We put this chip, this chip at the level of the wafer in the system
04:10and we deliver it to you, our customers, whether on your premises or via our cloud offering.
04:17And you need to understand that the industry and the world of work are going to be impacted right now.
04:21Everything is being done to ensure that AI agents take our jobs.
04:25Not only will you fill these facilities with your work,
04:29but also the new partnership with Meta that we announced a week ago
04:35where we will make their new API service available to the world's largest open weight community.
04:42A few days later, we announced that we would also be offering baby services.
04:56Beyond the IB, Madam, the cloud is reaching the other end of the corporate demographic spectrum.
05:03the 100 or 150 largest organizations in the world served by IBM.
05:07Over the course of this year, our customer base has grown by leaps and bounds.
05:12because large companies like our friends at GlaxoSmithKline...
05:17We just need to realize that today, all the largest energy processing companies,
05:23health organizations are in the process of signing partnerships with Cerebras.
05:27What will change is the power. What you've seen is just the beginning.
05:30Pharmaceutical companies that are 300 years old, as well as startups with 4 people.
05:37joined our cloud and benefited from the advantages of extremely fast reasoning.
05:44So, should Nvidia be sold? For me, there are signs that India will no longer have a monopoly.
05:49Today, it is very clear, there is a new market and that market is that of AI agents.
05:54Whether it's AI agents or even robotics, our jobs are directly impacted.
05:58The question is, do you know how to convert ChatGPT into AI agents with your $20 per month subscription today?
06:05The instructions today are very different.
06:07Agentic systems are based on reasoning systems that will consume tokens.
06:12These tokens are precisely what we call inference time.
06:15And that's what Cerebras' chip will solve.
06:18Isn't that right? We're running out of data.
06:20Therefore, it is clear that pre-trained extension, while important,
06:24is not entirely sufficient to reach the next level of intelligence.
06:28We still need more calculation, but not just in training.
06:32We have now entered a new era focused on computationally oriented towards optimized inference time.
06:37And, unsurprisingly, this has fascinating implications.
06:40It also obeys its own scaling laws in the reasoning process.
06:43The more tokens used, the higher the intelligence.
06:46The processing power will increase to a rate of over 2600 tokens per second.
06:52where today we are limited to a ceiling of 200 tokens per second.
06:55The inference model is too slow.
06:57The O3 model, which is known as the inference model, is a quote and is extremely slow.
07:02Computing power will mean that time, production capacity, and work,
07:06You are capable of writing system instructions, and will only be limited by the power of the processors.
07:11And today, that processing power is instantaneous.
07:14I don't have to wait any longer.
07:16If I need to assign a task instantly, I can simply ask the AI to do it instantly.
07:20Imagine how many tasks per minute can be executed.
07:25That's because we were limited by the reasoning factor of the model.
07:28Time has led most people to not mention the models of reason.
07:31But reasoning models are only one element of the AI power loop.
07:35The real power lies in the instructions.
07:37That is why these models are called inference models,
07:40because they can think and reason reactively before responding.
07:45Today, every cutting-edge model is an inference model.
07:48This is the intelligence index of all popular models today.
07:52Purple represents reasoning, blue represents non-reasoning.
07:54As you can see, all the best models are inference models.
07:58In fact, it is interesting to note that even the worst inference model
08:01is better than the best standard model of non-inference.
08:04Reasoning is the only way to achieve the most advanced intelligence.
08:08My second prediction is that 2026 will be the year with the biggest impact
08:12on the work and figures of the shadow market.
08:15Today, we've all been told, learn to use artificial intelligence,
08:19But what we did was we didn't learn how to use artificial intelligence.
08:21We learned how to chat with chatbots.
08:23The era of chatbots is dead.
08:25Today, systems such as OpenAI, Anthropie, and Cérébras show us one thing.
08:29Today, we are architects of IAT structures.
08:32What we see here is a direct consequence of the law of inferential time scaling that we just saw.
08:38The more tokens used, the higher the intelligence.
08:41More tokens equals higher intelligence.
08:43But we also know that from a practical point of view,
08:45Faster reasoning equates to more tokens, given a fixed time budget.
08:49It's almost like demonstrating a mathematical proof where, thanks to the nature of transitivity,
08:54We can conclude that faster reasoning processes
08:58ultimately lead to a higher level of intelligence.
09:01We must imagine the system as a probabilistic system
09:04which will determine, based on your instructions, the actions to be taken.
09:08So, how was this work carried out by the teams?
09:10First, skills were provided.
09:12That is to say, humans, doctors, lawyers, real estate agents
09:16spent time, hours, programming the AI to behave,
09:20the processes that are used in the areas of business tasks.
09:23What is changing, what we need to imagine today,
09:25that's what models like OpenAI with CHGPT or Anthropie are for.
09:29They are currently the only two models that have these capabilities.
09:31What we do in terms of training,
09:32It's about giving them the ability to work.
09:34And you, in fact, need to know how to use the learning blocks.
09:38and coordinate them with each other.
09:39In this way, you will be able to build workflows.
09:42The model is already to create PowerPoint presentations.
09:44The model is already about searching for information.
09:46The model already outlines what to do in the fields of medicine.
09:49finance, management, accounting.
09:51But it's up to you to organize it in what way and in what order.
09:55In fact, criteria, you will use the links of these different functions.
09:58Imagine all of this as bricks, and you're going to assemble them.
10:01You go from a job where you execute,
10:03where you become, in fact, an architect who executes
10:05by a fleet of artificial intelligence that you will configure.
10:09That's my biggest prediction for 2020.
10:11If you want to learn how to use artificial intelligence
10:13in the medical field,
10:15Office automation, the best of AI,
10:1765 hours of courses on artificial intelligence,
10:20update included for all those who are intended
10:23to create agentic systems
10:25where AI takes control of business tasks.
10:28Prompt Engineering Excellence,
10:30This is the elite, today, of the I reasoning system.
10:33The information is in the description.
10:34Those who own Nvidia shares today, in my opinion.
10:36should ask themselves the question.
10:38The shock is real.
10:39For me, Nvidia is no longer the leader today.
10:41He was not a leader.
10:42It was simply erased by the arrival of CR.
10:45So, of course, the market will need
10:47to always have multiprocessors
10:49with several suppliers.
10:50But today, there is very clearly a gap
10:52who will put on the market
10:54those who have an immediate answer
10:55and those who will wait more than half an hour
10:57to get an answer with heavenly intelligence.
10:59If, until now,
11:00We were limited by computing power.
11:02Today, it becomes instantaneous.
11:05The servers are already deployed.
11:06currently in use.
11:08The question is,
11:09Is that what you learned
11:10in art-sky intelligence
11:12will allow you today to really
11:13to use this computing power?
11:14And if you don't do it,
11:16Your colleague will take your place.
11:17the one who excels.
11:18It's obvious, the public authorities
11:20do not have fire speed
11:21to monitor the evolution of strategies
11:23of the use of tools.
11:25Europe is considerably behind.
11:27We realize that we are still,
11:28at the time, influencers.
11:29Let me explain: press a button
11:30to arrive at an answer
11:32of artificial intelligence.
11:33But today,
11:34true competence,
11:35it's about understanding
11:36how do they work
11:37the systems architectures
11:38and to be able
11:39to organize workflows.
11:40Those who already use systems
11:42such as ChatGPT,
11:43Agents,
11:43with systems like Claude's,
11:45Coworks are already able
11:46to perform tasks
11:47for more than an hour
11:48with Vinière.
11:49We delegate business tasks.
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