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Still buzzing from our live #COMPUTEX demos! Watching Chang Liu speak Mandarin while an AMD AI PC translated her words into English — live, on-device, with zero cloud assistance — was a true milestone. This demo proved real-time, voice-to-voice translation can run privately and with low latency on a...

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Jack Huynh

27,693 subscribers

14,634 просмотров • 1 год назад •via X (Twitter)

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Фото профиля DaPoets
DaPoets1 год назад

@AMD I was there!

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Study Bunny Games2 лет назад

Learn Japanese through "mild immersion" in a game Like this? Repost and follow us!

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💥AI is no longer confined to the cloud. At Computex, Jack Huynh showcased the AMD Ryzen AI Halo and Ryzen AI Max PRO 400 Series processors. The next wave of innovation will be built, trained, and run locally. As AI evolves from chatbots to intelligent agents, developers and enterprises need the performance, memory capacity, and flexibility to bring these experiences to life directly on the device. ⚡ Ryzen AI Halo enables local AI development with support for models up to 200B parameters, up to 128GB of unified memory, ROCm optimization, and Windows + Linux support. ⚡ Ryzen AI Max PRO 400 Series combines AI, graphics, and compute in a single architecture, delivering workstation-class performance for next-gen Agent Computers and AI PCs. Some of the most impactful AI experiences will happen right at your desk, where latency, privacy, and control matter most. We're just getting started.
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💥AI is no longer confined to the cloud. At Computex, Jack Huynh showcased the AMD Ryzen AI Halo and Ryzen AI Max PRO 400 Series processors. The next wave of innovation will be built, trained, and run locally. As AI evolves from chatbots to intelligent agents, developers and enterprises need the performance, memory capacity, and flexibility to bring these experiences to life directly on the device. ⚡ Ryzen AI Halo enables local AI development with support for models up to 200B parameters, up to 128GB of unified memory, ROCm optimization, and Windows + Linux support. ⚡ Ryzen AI Max PRO 400 Series combines AI, graphics, and compute in a single architecture, delivering workstation-class performance for next-gen Agent Computers and AI PCs. Some of the most impactful AI experiences will happen right at your desk, where latency, privacy, and control matter most. We're just getting started.

AMD

24,391 просмотров • 3 месяцев назад

BREAKING $AMD is ready to ship Ryzen AI Halo 🆕 in June 2026!!!! 🚀🚀🚀 AKA Personal/Local AI system. Ryzen AI Max+ 395 (Strix Halo APU) with 128 GB of unified LPDDR5X memory, 40 RDNA 3.5 GPU compute units, and a full ROCm software stack preloaded for both Windows and Linux. AMD Halo is $2,000-$3,000 vs $NVDA Spark $4,699 The main purpose of the AMD Ryzen AI Halo is to serve as a compact, developer focused workstation for running intensive AI workloads locally, specifically building, testing, prototyping, fine-tuning, and deploying generative AI models (like LLMs) and Agentic AI applications without relying on the cloud. Run models with up to 200 billion parameters on your desk using 128 GB of unified memory (shared across CPU, GPU, and NPU). This eliminates cloud costs, latency, data privacy concerns, and internet dependency. ~This is for AI developers, researchers, engineers, and creators. ~Teams or individuals wanting faster iteration, data privacy, and cost savings compared to cloud APIs. ~Users experimenting with open-source models, fine-tuning, inference, and production-like local deployment Source: AMD CES 2026
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BREAKING $AMD is ready to ship Ryzen AI Halo 🆕 in June 2026!!!! 🚀🚀🚀 AKA Personal/Local AI system. Ryzen AI Max+ 395 (Strix Halo APU) with 128 GB of unified LPDDR5X memory, 40 RDNA 3.5 GPU compute units, and a full ROCm software stack preloaded for both Windows and Linux. AMD Halo is $2,000-$3,000 vs $NVDA Spark $4,699 The main purpose of the AMD Ryzen AI Halo is to serve as a compact, developer focused workstation for running intensive AI workloads locally, specifically building, testing, prototyping, fine-tuning, and deploying generative AI models (like LLMs) and Agentic AI applications without relying on the cloud. Run models with up to 200 billion parameters on your desk using 128 GB of unified memory (shared across CPU, GPU, and NPU). This eliminates cloud costs, latency, data privacy concerns, and internet dependency. ~This is for AI developers, researchers, engineers, and creators. ~Teams or individuals wanting faster iteration, data privacy, and cost savings compared to cloud APIs. ~Users experimenting with open-source models, fine-tuning, inference, and production-like local deployment Source: AMD CES 2026

Mike

16,097 просмотров • 3 месяцев назад

✨ Personal AI is the next computing platform. AI is shifting from something you access to something you build with, locally, at the edge, and across systems. We’re unlocking new possibilities for developers: • AMD Ryzen AI Halo, a local-first developer system, preorder starting in June, develop AI without limits on your desk • Gorgon Halo with up to 192GB unified memory, supporting 300B+ parameter models locally, run massive models locally We’re excited to partner with clem 🤗🤗, Co-founder and CEO of Hugging Face, to advance open-source AI for Ryzen AI. Our focus is seamless AI, from model to deployment. Cloud, edge, device. One continuum. Multi-agent systems, local inference at scale, open models as infrastructure. This is the next computing era 🚀
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✨ Personal AI is the next computing platform. AI is shifting from something you access to something you build with, locally, at the edge, and across systems. We’re unlocking new possibilities for developers: • AMD Ryzen AI Halo, a local-first developer system, preorder starting in June, develop AI without limits on your desk • Gorgon Halo with up to 192GB unified memory, supporting 300B+ parameter models locally, run massive models locally We’re excited to partner with clem 🤗🤗, Co-founder and CEO of Hugging Face, to advance open-source AI for Ryzen AI. Our focus is seamless AI, from model to deployment. Cloud, edge, device. One continuum. Multi-agent systems, local inference at scale, open models as infrastructure. This is the next computing era 🚀

Jack Huynh

49,619 просмотров • 3 месяцев назад

Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:
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Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:

Andrew Ng

87,711 просмотров • 1 год назад

AI that responds in under 100ms doesn't happen by accident. Together AI VP of Kernels Dan Fu shares how they use NVIDIA CUDA, TensorRT-LLM, Dynamo and Together ATLAS on NVIDIA Blackwell to power ultra-low-latency inference and long context code generation for Cursor. From building megakernels that fuse an entire model's forward pass into a single launch to enabling real-time voice agents, Together AI is optimizing latency at every layer of the stack.
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AI that responds in under 100ms doesn't happen by accident. Together AI VP of Kernels Dan Fu shares how they use NVIDIA CUDA, TensorRT-LLM, Dynamo and Together ATLAS on NVIDIA Blackwell to power ultra-low-latency inference and long context code generation for Cursor. From building megakernels that fuse an entire model's forward pass into a single launch to enabling real-time voice agents, Together AI is optimizing latency at every layer of the stack.

NVIDIA AI Infrastructure

20,712 просмотров • 2 месяцев назад

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries
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As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

Daniel

151,698 просмотров • 8 месяцев назад

Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away. At Nexa AI, we’ve built our foundation on deep on-device AI technology—turning that vision into reality. Today, GPT-OSS is running fully local on mobile devices through our app, Nexa Studio. Real performance on Snapdragon Gen 5: - 17 tokens/sec decoding speed - < 3 seconds Time-to-First-Token Developers can now use NexaSDK to build their own local AI apps powered by GPT-OSS. What this unlocks: - Real reasoning models running locally - The next wave of AI Agent use cases—without cloud limits - True data privacy: your data never leaves your device Appreciation to OpenAI Sam Altman Greg Brockman and the entire leadership team for pushing the boundaries of the open-source AI community. Thanks to Qualcomm for the partnership, and to Manoj Khilnani and Chun-Po (Jerry) Chang for the incredible collaboration.
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Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away. At Nexa AI, we’ve built our foundation on deep on-device AI technology—turning that vision into reality. Today, GPT-OSS is running fully local on mobile devices through our app, Nexa Studio. Real performance on Snapdragon Gen 5: - 17 tokens/sec decoding speed - < 3 seconds Time-to-First-Token Developers can now use NexaSDK to build their own local AI apps powered by GPT-OSS. What this unlocks: - Real reasoning models running locally - The next wave of AI Agent use cases—without cloud limits - True data privacy: your data never leaves your device Appreciation to OpenAI Sam Altman Greg Brockman and the entire leadership team for pushing the boundaries of the open-source AI community. Thanks to Qualcomm for the partnership, and to Manoj Khilnani and Chun-Po (Jerry) Chang for the incredible collaboration.

NEXA AI

197,453 просмотров • 11 месяцев назад

💡Innovate. Create. Invent. That’s the spirit behind AMD University Program (AUP) in Taiwan. On Overheard at Computex, Jack Huynh walks through the AUP, and how we provide students, researchers, and educators greater access to the technologies shaping the future of computing. That means resources like: • AI & HPC Clusters • FAR Research grants • Low-cost dev kits But access is only the beginning. With Ryzen AI Max and our open AI development ecosystem, users can enable ROCm across the entire platform. This means more students building intelligent apps, developing advanced robotics, and exploring the full potential of AI. The most exciting breakthroughs won’t come from a single company. They will come from millions of curious minds pushing the boundaries of what’s possible. From Taiwan and beyond, the future of AI will be built by the next generation of innovators. AMD is here to put the right tools in their hands.
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💡Innovate. Create. Invent. That’s the spirit behind AMD University Program (AUP) in Taiwan. On Overheard at Computex, Jack Huynh walks through the AUP, and how we provide students, researchers, and educators greater access to the technologies shaping the future of computing. That means resources like: • AI & HPC Clusters • FAR Research grants • Low-cost dev kits But access is only the beginning. With Ryzen AI Max and our open AI development ecosystem, users can enable ROCm across the entire platform. This means more students building intelligent apps, developing advanced robotics, and exploring the full potential of AI. The most exciting breakthroughs won’t come from a single company. They will come from millions of curious minds pushing the boundaries of what’s possible. From Taiwan and beyond, the future of AI will be built by the next generation of innovators. AMD is here to put the right tools in their hands.

AMD

11,324 просмотров • 3 месяцев назад

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The QVAC SDK is the "LEGO block" of the next era of computing. It’s a modular, local-first framework designed to turn anything—from a simple robot to an industrial server—into a sovereign, autonomous mind. Why build with QVAC? Atomic Intelligence: AI as a raw material embedded directly into your hardware. No Cloud Dependency: 0 latency and total privacy. If the internet breaks, your world keeps thinking. Infinite Scale: A single API for local AI that runs on any device, anywhere. From a child’s toy to the fabric of the universe, if you can dream it, you can build it. Start building the future: 🚀

QVAC

4,707,376 просмотров • 4 месяцев назад

A voice agent powered by gpt-oss. Running locally on my macBook. Demo recorded in a Waymo with WiFi turned off. I'm still on my space game voice AI kick, obviously. Code link below. For conversational voice AI, you want to set the gpt-oss reasoning behavior to "low". (The default is "medium".) Notes on how to do that and a jinja template you can use are in the repo. The LLM in the demo video is the big, 120B version of gpt-oss. You can use the smaller, 20B model for this, of course. But OpenAI really did a cool thing here designing the 120B model to run in "just" 80GB of VRAM. And the llama.cpp mlx inference is fast: ~250ms TTFT. Running a big model on-device feels like a time warp into the future of AI.
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A voice agent powered by gpt-oss. Running locally on my macBook. Demo recorded in a Waymo with WiFi turned off. I'm still on my space game voice AI kick, obviously. Code link below. For conversational voice AI, you want to set the gpt-oss reasoning behavior to "low". (The default is "medium".) Notes on how to do that and a jinja template you can use are in the repo. The LLM in the demo video is the big, 120B version of gpt-oss. You can use the smaller, 20B model for this, of course. But OpenAI really did a cool thing here designing the 120B model to run in "just" 80GB of VRAM. And the llama.cpp mlx inference is fast: ~250ms TTFT. Running a big model on-device feels like a time warp into the future of AI.

kwindla

202,211 просмотров • 1 год назад

This is the fastest text-to-speech and speech-to-text API out there. It's under 250 ms to first-byte latency. That's what we need to deploy conversational AI applications everywhere! (I can't wait for the death of automated voice machines.) Look at the attached video. For the first time, I could have a conversation with an AI agent in real time without awkward pauses. This technology is Aura from Deepgram. It's the fastest text-to-speech option I've seen. Here is a GitHub repository with the demo in the video: You can use it to build a conversational AI application using natural voice. You'll need a Deepgram API Key and an OpenAI API Key. Thanks to Deepgram for giving me access to Aura and collaborating with me on this post. If you want to learn more about Aura, here is the announcement post:
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This is the fastest text-to-speech and speech-to-text API out there. It's under 250 ms to first-byte latency. That's what we need to deploy conversational AI applications everywhere! (I can't wait for the death of automated voice machines.) Look at the attached video. For the first time, I could have a conversation with an AI agent in real time without awkward pauses. This technology is Aura from Deepgram. It's the fastest text-to-speech option I've seen. Here is a GitHub repository with the demo in the video: You can use it to build a conversational AI application using natural voice. You'll need a Deepgram API Key and an OpenAI API Key. Thanks to Deepgram for giving me access to Aura and collaborating with me on this post. If you want to learn more about Aura, here is the announcement post:

Santiago

185,640 просмотров • 2 лет назад

It takes a lot of time for technology to mature and become magical. 🤩 AI is there now. When we envisaged AI Soundbox, soon it was clear that it won’t be just a payment device anymore. 🤖 It would rather be like a smartphone in the age of feature phones. QR was like a rotary phone, Soundbox a cellular, and this: a smartphone. 📱 A superhuman capability for a small and micro business that has never been available to these businesses. A very smart and capable business partner on the counter for 100s of millions of small businesses. At Paytm, we are very proud to lead innovations for small and micro businesses in India. Today we bring the perfect AI device for millions of businesses. Making technology and AI more inclusive. An AI system, with power and training of their payment and business data. It doesn’t anymore remain a payment device, rather becomes a business partner on the counter. Proud to launch and reinforcing our commitment to small and medium businesses to make Made-in-India AI 🇮🇳 🤖. Here is the launch demo of Paytm AI. Paytm AI Soundbox. You will love the use case which even we discovered in the launch. 🚀
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It takes a lot of time for technology to mature and become magical. 🤩 AI is there now. When we envisaged AI Soundbox, soon it was clear that it won’t be just a payment device anymore. 🤖 It would rather be like a smartphone in the age of feature phones. QR was like a rotary phone, Soundbox a cellular, and this: a smartphone. 📱 A superhuman capability for a small and micro business that has never been available to these businesses. A very smart and capable business partner on the counter for 100s of millions of small businesses. At Paytm, we are very proud to lead innovations for small and micro businesses in India. Today we bring the perfect AI device for millions of businesses. Making technology and AI more inclusive. An AI system, with power and training of their payment and business data. It doesn’t anymore remain a payment device, rather becomes a business partner on the counter. Proud to launch and reinforcing our commitment to small and medium businesses to make Made-in-India AI 🇮🇳 🤖. Here is the launch demo of Paytm AI. Paytm AI Soundbox. You will love the use case which even we discovered in the launch. 🚀

Vijay Shekhar Sharma

74,065 просмотров • 11 месяцев назад

Real-time translation that sounds like you 🚀 We've been building speech-to-speech simultaneous translation for a while. The newest piece is on-the-fly voice cloning: the model picks up your voice as you speak and carries it into the other language. Here's the alpha running live during our all-hands: you speak in Japanese, the room hears you in English, and it still sounds like you. It's early and the edges are rough, but this is the experience we've been building toward. Coming very soon. Demo video below 👇 #VoiceAI #SpeechAI #RealTimeTranslation #VoiceCloning #AI
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Real-time translation that sounds like you 🚀 We've been building speech-to-speech simultaneous translation for a while. The newest piece is on-the-fly voice cloning: the model picks up your voice as you speak and carries it into the other language. Here's the alpha running live during our all-hands: you speak in Japanese, the room hears you in English, and it still sounds like you. It's early and the edges are rough, but this is the experience we've been building toward. Coming very soon. Demo video below 👇 #VoiceAI #SpeechAI #RealTimeTranslation #VoiceCloning #AI

Kotoba

69,704 просмотров • 11 дней назад

There's a very small YouTube channel that uses stock footage and narration, where the voice seems to be AI trained on my voice. I was told about it from one of my viewers that said when he started watching this channel he realized it was my voice, but with AI. It for sure doesn't sound perfect, and it still has that "AI" sound to it, but this stuff is going to get a lot more convincing really soon. There's not really any tools to deal with this currently, as there's no reporting system, and it's not an exact copy because the narration is different. But I feel like this may become a much bigger problem for YouTube in the future. They are going to have to adapt their copyright system to include AI voice and video copies of people under impersonation.
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There's a very small YouTube channel that uses stock footage and narration, where the voice seems to be AI trained on my voice. I was told about it from one of my viewers that said when he started watching this channel he realized it was my voice, but with AI. It for sure doesn't sound perfect, and it still has that "AI" sound to it, but this stuff is going to get a lot more convincing really soon. There's not really any tools to deal with this currently, as there's no reporting system, and it's not an exact copy because the narration is different. But I feel like this may become a much bigger problem for YouTube in the future. They are going to have to adapt their copyright system to include AI voice and video copies of people under impersonation.

Joseph Carlson

46,024 просмотров • 1 год назад

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.
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I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.

Johnny

27,179 просмотров • 1 год назад

Presenting: $PALM AI Applications System Hardware 📹 Watch the Reveal Teaser from below ✅ The first Web3 related microcontroller-based device that combines offline voice recognition with in-built AI profiles, performing on electronic circuits straight from the breadboard it was incepted from. 1️⃣ The next steps are to document the use cases of our system, which can be integrated into existing devices and physical services in real life. We will be sure to document and publish processes as the device goes into factory production all the way to shipping them out and showcasing the real-world adoption of our products. 2️⃣ Our hardware is built for flexibility and to innovate on, and we will be working with the top experts in the field to make the best out of it and is built to allow the device structure to be embedded as the smart component of any and all devices. ⌛️Stay tuned for the first use-case reveal of our AI Applications System inside an AI-enhanced, by default offline hardware wallet, fully built from scratch by our talented team. 🙏 Finally, thank you for the continued support. We will keep working as hard as ever. Enjoy the reveal.
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Presenting: $PALM AI Applications System Hardware 📹 Watch the Reveal Teaser from below ✅ The first Web3 related microcontroller-based device that combines offline voice recognition with in-built AI profiles, performing on electronic circuits straight from the breadboard it was incepted from. 1️⃣ The next steps are to document the use cases of our system, which can be integrated into existing devices and physical services in real life. We will be sure to document and publish processes as the device goes into factory production all the way to shipping them out and showcasing the real-world adoption of our products. 2️⃣ Our hardware is built for flexibility and to innovate on, and we will be working with the top experts in the field to make the best out of it and is built to allow the device structure to be embedded as the smart component of any and all devices. ⌛️Stay tuned for the first use-case reveal of our AI Applications System inside an AI-enhanced, by default offline hardware wallet, fully built from scratch by our talented team. 🙏 Finally, thank you for the continued support. We will keep working as hard as ever. Enjoy the reveal.

PaLM AI - $PALM

166,276 просмотров • 2 лет назад

🎥 Behind the Scenes of Alibaba Cloud Partner Connect 2026 We’re excited to advance our AI Alliance Program — a strategic initiative to empower AI innovation and accelerate next-gen solutions for global enterprises. Working with AI partners like ATOS, we’re helping organizations harness cloud-native data and AI capabilities to drive business transformation at scale globally. In this video, hear from Purshottam Purswani, Head of Data and AI & CTO, APAC at ATOS, on how Partner Connect 2026 is shaping the future of AI-powered collaboration — and why strategic cloud partnerships are essential for future-ready enterprises. 💬 AI Partner - Your Voice, Our Connection #AlibabaCloud #PartnerConnect2026 #AIAlliance #ATOS #CloudComputing #AI #AInnovation #LLM
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🎥 Behind the Scenes of Alibaba Cloud Partner Connect 2026 We’re excited to advance our AI Alliance Program — a strategic initiative to empower AI innovation and accelerate next-gen solutions for global enterprises. Working with AI partners like ATOS, we’re helping organizations harness cloud-native data and AI capabilities to drive business transformation at scale globally. In this video, hear from Purshottam Purswani, Head of Data and AI & CTO, APAC at ATOS, on how Partner Connect 2026 is shaping the future of AI-powered collaboration — and why strategic cloud partnerships are essential for future-ready enterprises. 💬 AI Partner - Your Voice, Our Connection #AlibabaCloud #PartnerConnect2026 #AIAlliance #ATOS #CloudComputing #AI #AInnovation #LLM

Alibaba Cloud

19,233 просмотров • 6 месяцев назад

Ai Day last week was a blast! I had a great time presenting alongside many colleagues from InstaDeep and BioNTech SE, some of our ongoing Bio+AI initiatives. We made several announcements, introducing Kyber, our new supercomputing cluster (more on this soon), as well as BFN, our new paradigm for generative AI, with competitive results on protein and antibody generation. During Ai Day we also delivered two live demos of Laila, our new AI agent, including one from BioNTech's TechLab in Mainz, Germany! To our knowledge, this was one of the first demos of its kind featuring a GPT-4 level AI agent acting in the Lab, interacting with scientists and technicians and helping them enhance their productivity! Ai Day was a significant moment for all of us, and it was exciting to see both the event and Laila featured by several publications including Financial Times ( Curious to see it in action? Check out the recording of the Laila in the Lab demo below, worth watching and don't miss the end! 😉
6:30

Ai Day last week was a blast! I had a great time presenting alongside many colleagues from InstaDeep and BioNTech SE, some of our ongoing Bio+AI initiatives. We made several announcements, introducing Kyber, our new supercomputing cluster (more on this soon), as well as BFN, our new paradigm for generative AI, with competitive results on protein and antibody generation. During Ai Day we also delivered two live demos of Laila, our new AI agent, including one from BioNTech's TechLab in Mainz, Germany! To our knowledge, this was one of the first demos of its kind featuring a GPT-4 level AI agent acting in the Lab, interacting with scientists and technicians and helping them enhance their productivity! Ai Day was a significant moment for all of us, and it was exciting to see both the event and Laila featured by several publications including Financial Times ( Curious to see it in action? Check out the recording of the Laila in the Lab demo below, worth watching and don't miss the end! 😉

Karim Beguir

16,539 просмотров • 1 год назад

What Lisa Su actually held on stage: A mini PC the size of a lunchbox running Qwen3-235B locally, with no cloud and no discrete GPU Inside: the Ryzen AI Max+ 395, 128GB unified memory, 110GB usable as VRAM on Linux The first x86 chip that handles 200+ billion parameters on a single die AMD claims it beats the RTX 5080 by several times on memory-bound models — because the 5080 simply cannot fit them $1,400 to $2,500 once. cloud bills run $200 to $400 a month It pays for itself in a few months, then costs nothing per request This is not a faster GPU. it is the first real argument that your AI does not belong in someone else's data center
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What Lisa Su actually held on stage: A mini PC the size of a lunchbox running Qwen3-235B locally, with no cloud and no discrete GPU Inside: the Ryzen AI Max+ 395, 128GB unified memory, 110GB usable as VRAM on Linux The first x86 chip that handles 200+ billion parameters on a single die AMD claims it beats the RTX 5080 by several times on memory-bound models — because the 5080 simply cannot fit them $1,400 to $2,500 once. cloud bills run $200 to $400 a month It pays for itself in a few months, then costs nothing per request This is not a faster GPU. it is the first real argument that your AI does not belong in someone else's data center

plutos

1,237,742 просмотров • 2 месяцев назад