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CapIX × SuperGemma We've partnered with SuperGemma to host their model catalog on CapIX GPUs. SuperQwen-Agentworld-35B, SuperHY3, and SuperGemma4-26B-multimodal are now deployable as private OpenAI-compatible endpoints on the CapIX network. Each deploy runs on isolated, decentralized GPU capacity Uncensored open-weight models running end-to-end on fully private compute. Shipped with... show more

CapIX Protocol

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.CapIX Dev's vision is to bring quantum computing to retail The three layers underneath: - Layer 1: GPU mesh aggregating Surplus Intelligence, Pod the Squire, OpenRouter - Layer 2: Smart router (heavy tasks go to the GPU, light tasks go to the CPU, quantum computing in the future?) - Layer 3: Community cloud with end-to-end flows Ruqaiyah built it solo with an 86-agent dev The whole EasyA Kickstart stack is forming under the $CPX umbrella - ARTHUR LABS builds the 3D model - Bit Agents handles the commerce - Covenant Foundation governs the agents - Factory in China prints the physical product - All running on CapIX compute

.CapIX Dev's vision is to bring quantum computing to retail The three layers underneath: - Layer 1: GPU mesh aggregating Surplus Intelligence, Pod the Squire, OpenRouter - Layer 2: Smart router (heavy tasks go to the GPU, light tasks go to the CPU, quantum computing in the future?) - Layer 3: Community cloud with end-to-end flows Ruqaiyah built it solo with an 86-agent dev The whole EasyA Kickstart stack is forming under the $CPX umbrella - ARTHUR LABS builds the 3D model - Bit Agents handles the commerce - Covenant Foundation governs the agents - Factory in China prints the physical product - All running on CapIX compute

MCG

13,184 görüntüleme • 2 ay önce

AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:

AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:

Uomi

18,979 görüntüleme • 2 ay önce

Claude Fable 5 is INSANE 🤯 So we asked it to build the perfect private AI infrastructure, where: → 500+ open models run behind a single API, not one company's black box → An open data marketplace → You fine-tune and serve models on decentralized GPU compute → Agents pay per call in USDC via x402 → Every model, dataset, and payment settles on Base It failed. Luckily, we've spent years building exactly that. Private Inference. Data. Compute. Settlement. One private AI infrastructure layer. Cluster Protocol.

Claude Fable 5 is INSANE 🤯 So we asked it to build the perfect private AI infrastructure, where: → 500+ open models run behind a single API, not one company's black box → An open data marketplace → You fine-tune and serve models on decentralized GPU compute → Agents pay per call in USDC via x402 → Every model, dataset, and payment settles on Base It failed. Luckily, we've spent years building exactly that. Private Inference. Data. Compute. Settlement. One private AI infrastructure layer. Cluster Protocol.

Cluster Protocol

25,820 görüntüleme • 3 ay önce

Arbius V5 is now LIVE on Arbitrum ✨ The trustless AI revolution starts with Arbius V5. Use AI free from centralization and censorship. Live now: - V5 Mainnet: Deploy any open-source AI model to the Arbius network. Vote on models and gauges. - Gobius miner: Use your GPU to contribute compute to the network and earn fees. Miners will need 420 $AIUS to get started. - Qwen 32B QWQ: Live now on Arbius Playground.

Arbius V5 is now LIVE on Arbitrum ✨ The trustless AI revolution starts with Arbius V5. Use AI free from centralization and censorship. Live now: - V5 Mainnet: Deploy any open-source AI model to the Arbius network. Vote on models and gauges. - Gobius miner: Use your GPU to contribute compute to the network and earn fees. Miners will need 420 $AIUS to get started. - Qwen 32B QWQ: Live now on Arbius Playground.

Arbius

42,054 görüntüleme • 1 yıl önce

Here is Ternary Bonsai 27B running an end-to-end agentic workflow locally with Hermes on an NVIDIA GeForce RTX 5090 GPU. The model reasons, calls tools, reads outputs, modifies files, and surfaces insights - all on consumer hardware, while all private files, intermediate states, and iterations remain local.

Here is Ternary Bonsai 27B running an end-to-end agentic workflow locally with Hermes on an NVIDIA GeForce RTX 5090 GPU. The model reasons, calls tools, reads outputs, modifies files, and surfaces insights - all on consumer hardware, while all private files, intermediate states, and iterations remain local.

PrismML

154,473 görüntüleme • 2 ay önce

AIxBlock – A Decentralized Platform for End-to-End AI Development & Workflow Automation What can you actually build with AIxBlock? Here’s the full flow — fully customizable and self-hostable: [Curate Data] → [Label] → [Train] → [Deploy] → [Automate] → [Monetize] Pull models from Hugging Face, bring in your own data, train on decentralized GPUs, and automate everything with low-code builders. 🧪 Explore the platform: 🎥 Want to see a real use-case demo? Check our full gallery here:

AIxBlock – A Decentralized Platform for End-to-End AI Development & Workflow Automation What can you actually build with AIxBlock? Here’s the full flow — fully customizable and self-hostable: [Curate Data] → [Label] → [Train] → [Deploy] → [Automate] → [Monetize] Pull models from Hugging Face, bring in your own data, train on decentralized GPUs, and automate everything with low-code builders. 🧪 Explore the platform: 🎥 Want to see a real use-case demo? Check our full gallery here:

AIxBlock

14,330 görüntüleme • 1 yıl önce

Introducing Unsloth Desktop 🦥 The first desktop app to run and train models locally. • Open-source. Runs on Mac, Windows and Linux • Supports MLX, diffusion image/video, audio, GGUF • Connect Claude Code and Codex to local LLMs • 50% more accurate, self-healing tool calls + sandboxed code exec • Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac • Train models 2× faster with 70% less VRAM • Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF) • Use Unsloth’s OpenAI-compatible API and cloud models • Securely deploy LLMs remotely and access anywhere Unsloth Desktop is now available on and GitHub. GitHub: Blog and Guide:

Introducing Unsloth Desktop 🦥 The first desktop app to run and train models locally. • Open-source. Runs on Mac, Windows and Linux • Supports MLX, diffusion image/video, audio, GGUF • Connect Claude Code and Codex to local LLMs • 50% more accurate, self-healing tool calls + sandboxed code exec • Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac • Train models 2× faster with 70% less VRAM • Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF) • Use Unsloth’s OpenAI-compatible API and cloud models • Securely deploy LLMs remotely and access anywhere Unsloth Desktop is now available on and GitHub. GitHub: Blog and Guide:

Unsloth AI

855,849 görüntüleme • 1 ay önce

We're releasing ACE-Step-v1.5(2B), a fast, high-quality open-source music model. It runs locally on a consumer-grade GPU, generates a full song in under 2 seconds(on an A100), supports LoRA fine-tuning, and beats SUNO on common eval metrics. GitHub: Key traits: Quality: beats Suno on common eval scores Speed: full song under 2s on A100 Local: ~4GB VRAM, under 10s on RTX 3090 LoRA: train your own style with a few songs License: MIT, free for commercial use Data: fully authorized plus synthetic The music AI space lacks commercial-grade open models. Many creators are forced to rely on closed-source services, and can’t fully own, run locally, or fine-tune their own models. We want to help change that.

We're releasing ACE-Step-v1.5(2B), a fast, high-quality open-source music model. It runs locally on a consumer-grade GPU, generates a full song in under 2 seconds(on an A100), supports LoRA fine-tuning, and beats SUNO on common eval metrics. GitHub: Key traits: Quality: beats Suno on common eval scores Speed: full song under 2s on A100 Local: ~4GB VRAM, under 10s on RTX 3090 LoRA: train your own style with a few songs License: MIT, free for commercial use Data: fully authorized plus synthetic The music AI space lacks commercial-grade open models. Many creators are forced to rely on closed-source services, and can’t fully own, run locally, or fine-tune their own models. We want to help change that.

ACE Music

1,059,128 görüntüleme • 7 ay önce

AIRian is thrilled to announce a strategic partnership with io.net, a leading decentralized GPU compute platform. 🤝 This collaboration marks a significant milestone for AIRian as we embark on building the largest Web 3.0 running community. 🚀 With the support of we’ll be deploying and expanding GPU compute resources to power an active user community. AIRian’s application and platform will collect all the data people generate in their daily lives including walking, running, marathon, and healthcare related data and create extensive Sports-AI models focused on running behavior, statistics, etc. AIRian will also expand into GPS-based applications including competitions, Web3 games, and more. 🏃🏻🏃‍♂️🏃‍♀️ The frictionless access to GPU compute will allow us to easily accommodate our rollout and meet compute demands when we need it, as we need it. AIRian. Binance $AIR #AIRian #AIR #ionet #DePin #AI #SportsAI #dApp #socialFi

AIRian is thrilled to announce a strategic partnership with io.net, a leading decentralized GPU compute platform. 🤝 This collaboration marks a significant milestone for AIRian as we embark on building the largest Web 3.0 running community. 🚀 With the support of we’ll be deploying and expanding GPU compute resources to power an active user community. AIRian’s application and platform will collect all the data people generate in their daily lives including walking, running, marathon, and healthcare related data and create extensive Sports-AI models focused on running behavior, statistics, etc. AIRian will also expand into GPS-based applications including competitions, Web3 games, and more. 🏃🏻🏃‍♂️🏃‍♀️ The frictionless access to GPU compute will allow us to easily accommodate our rollout and meet compute demands when we need it, as we need it. AIRian. Binance $AIR #AIRian #AIR #ionet #DePin #AI #SportsAI #dApp #socialFi

AIRian.

66,192 görüntüleme • 2 yıl önce

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.

QVAC

20,932,195 görüntüleme • 3 ay önce

We've just unveiled ERNIE 4.5 & X1! 🚀 As a deep-thinking reasoning model with multimodal capabilities, ERNIE X1 delivers performance on par with DeepSeek R1 at only half the price. Meanwhile, ERNIE 4.5 is our latest foundation model and new-generation native multimodal model. Plus, our AI chatbot ERNIE Bot has now been made free to individual users ahead of schedule. Both models are now freely accessible to all ERNIE Bot users via its official website:

We've just unveiled ERNIE 4.5 & X1! 🚀 As a deep-thinking reasoning model with multimodal capabilities, ERNIE X1 delivers performance on par with DeepSeek R1 at only half the price. Meanwhile, ERNIE 4.5 is our latest foundation model and new-generation native multimodal model. Plus, our AI chatbot ERNIE Bot has now been made free to individual users ahead of schedule. Both models are now freely accessible to all ERNIE Bot users via its official website:

Baidu Inc.

1,712,541 görüntüleme • 1 yıl önce

HERMES AGENT NOW RUNS ON AN 8GB LAPTOP GPU JUST AS EASILY AS IT RUNS ON A 128GB MINI PC Nous Research shipped the official Hermes Agent Desktop App this week. Someone pointed it at a local llama server running on an RTX 4060 with 16GB system RAM. The integration took two minutes The model behind it: Gemma 4 26B MoE, QAT quantized, running on 8GB of VRAM. A 60k token prompt held a stable 20 tokens a second, flat, no slowdown as context grew. The flags were nothing exotic, just -cmoe -c 248000 on llama.cpp What that 8GB setup does out of the box: reads and patches its own code, runs it in a terminal, debugs errors, manages GitHub repos, spawns sub-agents for parallel work. Browses the web with vision to debug a UI. Schedules cron jobs in plain language. Connects to Notion, Google Workspace, Linear, and Obsidian to manage tasks on its own That's the same agent layer running on a Minisforum MS-S1 MAX with 128GB of unified memory, 96GB of it to the GPU, holding a 120B model at 56 tokens a second instead of a 26B model at 20. Same software, same tool execution, same zero API key. The only thing that changes between an $800 laptop and a $2,000 mini PC is how big a model you can afford to run underneath it The barrier to running a real autonomous agent locally didn't just drop. It dropped all the way down to hardware most people already own

HERMES AGENT NOW RUNS ON AN 8GB LAPTOP GPU JUST AS EASILY AS IT RUNS ON A 128GB MINI PC Nous Research shipped the official Hermes Agent Desktop App this week. Someone pointed it at a local llama server running on an RTX 4060 with 16GB system RAM. The integration took two minutes The model behind it: Gemma 4 26B MoE, QAT quantized, running on 8GB of VRAM. A 60k token prompt held a stable 20 tokens a second, flat, no slowdown as context grew. The flags were nothing exotic, just -cmoe -c 248000 on llama.cpp What that 8GB setup does out of the box: reads and patches its own code, runs it in a terminal, debugs errors, manages GitHub repos, spawns sub-agents for parallel work. Browses the web with vision to debug a UI. Schedules cron jobs in plain language. Connects to Notion, Google Workspace, Linear, and Obsidian to manage tasks on its own That's the same agent layer running on a Minisforum MS-S1 MAX with 128GB of unified memory, 96GB of it to the GPU, holding a 120B model at 56 tokens a second instead of a 26B model at 20. Same software, same tool execution, same zero API key. The only thing that changes between an $800 laptop and a $2,000 mini PC is how big a model you can afford to run underneath it The barrier to running a real autonomous agent locally didn't just drop. It dropped all the way down to hardware most people already own

NO1ennn

40,079 görüntüleme • 3 ay önce

🗓️ Release Week Recap Big week. mlx-audio and mlx-vlm are now among some of the fastest-growing OSS projects. Here’s what we shipped last week. Gemma 4 on Apple Silicon Two awesome releases by our partner Google DeepMind & Google Gemma : > Gemma 4 12B — their new dense, unified multimodal model. It uses an encoder free audio path and simplified vision encoder. > Gemma 4 QAT — quantization-aware training checkpoints, optimized to run locally on consumer GPUs and edge devices, compressing the model while preserving the quality you expect from Gemma 4. On the audio 🎧 side we added support for 15+ new TTS, ASR & VAD models, faster long-form transcription, and an expanded OpenAI-compatible audio server. All local on Apple Silicon. Huge thanks to every contributor and my co-maintainer Lucas Newman. 🙏🏽

🗓️ Release Week Recap Big week. mlx-audio and mlx-vlm are now among some of the fastest-growing OSS projects. Here’s what we shipped last week. Gemma 4 on Apple Silicon Two awesome releases by our partner Google DeepMind & Google Gemma : > Gemma 4 12B — their new dense, unified multimodal model. It uses an encoder free audio path and simplified vision encoder. > Gemma 4 QAT — quantization-aware training checkpoints, optimized to run locally on consumer GPUs and edge devices, compressing the model while preserving the quality you expect from Gemma 4. On the audio 🎧 side we added support for 15+ new TTS, ASR & VAD models, faster long-form transcription, and an expanded OpenAI-compatible audio server. All local on Apple Silicon. Huge thanks to every contributor and my co-maintainer Lucas Newman. 🙏🏽

Prince Canuma

11,810 görüntüleme • 3 ay önce

Karp claims that LLMs are inherently unsafe for critical infrastructure, battlefields, or regulated manufacturing environments on their own. He says Palantir's Ontology is the necessary application layer to prevent data caching, stop unauthorized model training on private data, and make LLMs safe and precise for enterprise deployment. Palantir's alliance with Nvidia is engineered to give technical and defense customers absolute control over their compute, data stack, and proprietary models without bleeding their alpha to outside providers. $PLTR $NVDA

Karp claims that LLMs are inherently unsafe for critical infrastructure, battlefields, or regulated manufacturing environments on their own. He says Palantir's Ontology is the necessary application layer to prevent data caching, stop unauthorized model training on private data, and make LLMs safe and precise for enterprise deployment. Palantir's alliance with Nvidia is engineered to give technical and defense customers absolute control over their compute, data stack, and proprietary models without bleeding their alpha to outside providers. $PLTR $NVDA

CapexAndChill

11,924 görüntüleme • 2 ay önce

📢 NEURALAI X 🤩 NeuralAI is extraordinarily excited to reveal that we have started working closely with Akash Network, the world's premier decentralized compute marketplace! ✔️ The offerings of align perfectly with the vision and roadmap of NeuralAI, making this collaboration extremely significant for NeuralAI. For a start, NeuralAI will run inference on Akash based on our 3D #AI model and prepare TAO miners for reward-yielding subnets. NeuralAI will also leverage Akash's powerful GPUs to enhance our #AI #model training. Currently, provides us with 56 powerful A6000 GPUs, scalable on demand. ✔️ NeuralAI also gets highly competitive pricing from Akash Network, significantly lower than any other compute provider in the world. The lower pricing is possible partially thanks to the crypto-driven incentives within Akash Network, making it an excellent opportunity for NeuralAI to leverage. The synergy with Akash Network is a big leap forward, fueling our mission of becoming the #1 AI-driven 3D asset generator subnet on Bittensor. ✔️ We look forward to our close work with , we are certain with their support we will be able to secure a unique edge in the market! Stay tuned for exciting updates and developments.

📢 NEURALAI X 🤩 NeuralAI is extraordinarily excited to reveal that we have started working closely with Akash Network, the world's premier decentralized compute marketplace! ✔️ The offerings of align perfectly with the vision and roadmap of NeuralAI, making this collaboration extremely significant for NeuralAI. For a start, NeuralAI will run inference on Akash based on our 3D #AI model and prepare TAO miners for reward-yielding subnets. NeuralAI will also leverage Akash's powerful GPUs to enhance our #AI #model training. Currently, provides us with 56 powerful A6000 GPUs, scalable on demand. ✔️ NeuralAI also gets highly competitive pricing from Akash Network, significantly lower than any other compute provider in the world. The lower pricing is possible partially thanks to the crypto-driven incentives within Akash Network, making it an excellent opportunity for NeuralAI to leverage. The synergy with Akash Network is a big leap forward, fueling our mission of becoming the #1 AI-driven 3D asset generator subnet on Bittensor. ✔️ We look forward to our close work with , we are certain with their support we will be able to secure a unique edge in the market! Stay tuned for exciting updates and developments.

NeuralAI

241,364 görüntüleme • 2 yıl önce

six months ago this wasn't happening on 8gb vram. running unsloth's Q4_K_XL quant of gemma 4 26b-a4b-it-qat, a sparse MoE model with only 4b active params on a single rtx 4060 laptop gpu, 8gb vram, 20+ tok/s decode. no cloud, no api, no offload hacks. just a gaming laptop on battery. what makes it fit: google's QAT (quantization aware training), plus MTP (multi token prediction) support in the latest llama.cpp builds. that combo is the single biggest unlock for local inference on low vram. rtx 3060, rtx 3070, gtx 1070, gtx 1080, rtx 4050, rtx 4060, rtx 5050, rtx 5060 — any 6-8gb consumer gpu, old or new — this model runs on it. world cup season, so i told it to build a soccer themed flappy bird clone. one shot, zero iteration, fully playable. six months ago an 8gb model could barely clone vanilla flappy bird. now it's shipping a themed game from a sparse MoE model running locally on a laptop battery. inference benchmarks: - decode throughput: 30 tok/s - context: 64k. this is the real unlock. 64k ctx is what makes a hermes agent loop viable locally on this model, not just single-turn chat. llama.cpp flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 -cmoe --port 8080 game's deployed on my own site, built and shipped end to end with open source llm, zero closed source api dependency in the pipeline. link in the description. gguf weights on huggingface, link in the comments. pull it down, run it on whatever 8gb card is sitting in your rig. try the game and tell me your score and what you want in v2. local llms on consumer gpus stopped being a meme.

six months ago this wasn't happening on 8gb vram. running unsloth's Q4_K_XL quant of gemma 4 26b-a4b-it-qat, a sparse MoE model with only 4b active params on a single rtx 4060 laptop gpu, 8gb vram, 20+ tok/s decode. no cloud, no api, no offload hacks. just a gaming laptop on battery. what makes it fit: google's QAT (quantization aware training), plus MTP (multi token prediction) support in the latest llama.cpp builds. that combo is the single biggest unlock for local inference on low vram. rtx 3060, rtx 3070, gtx 1070, gtx 1080, rtx 4050, rtx 4060, rtx 5050, rtx 5060 — any 6-8gb consumer gpu, old or new — this model runs on it. world cup season, so i told it to build a soccer themed flappy bird clone. one shot, zero iteration, fully playable. six months ago an 8gb model could barely clone vanilla flappy bird. now it's shipping a themed game from a sparse MoE model running locally on a laptop battery. inference benchmarks: - decode throughput: 30 tok/s - context: 64k. this is the real unlock. 64k ctx is what makes a hermes agent loop viable locally on this model, not just single-turn chat. llama.cpp flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 -cmoe --port 8080 game's deployed on my own site, built and shipped end to end with open source llm, zero closed source api dependency in the pipeline. link in the description. gguf weights on huggingface, link in the comments. pull it down, run it on whatever 8gb card is sitting in your rig. try the game and tell me your score and what you want in v2. local llms on consumer gpus stopped being a meme.

Alok

61,660 görüntüleme • 2 ay önce

Microsoft AI CEO Mustafa Suleyman explains the exit plan behind the biggest partnership in AI. It began with a Satya Nadella message surfaced in the trial: "I don't want to be Intel and have OpenAI be Microsoft." The escape hardware is already shipping. "Partnerships like that don't last forever. OpenAI wants to be a trillion-dollar public company... They want to have the freedom to operate and be able to buy compute from all sorts of other places, build their own compute, partner with whoever they want." "We've made the decision the early part of last year, which obviously informed all the contract negotiations, which then all got resolved and signed in October." "We now are able to manufacture and ship a chip that is 30% cheaper than a GB200 inside of our own clusters." "The MAI-Thinking-1 model that we've just released actually delivers 1.4x performance per watt improvement on top of the 30% improvement that you get from running on a Maia 200, once we co-optimize the models for our tasks." Microsoft watched Windows commoditize Intel and is refusing the Intel seat this time: own silicon, own frontier model, co-designed end to end. The open question is which gap closes first: OpenAI building Microsoft's enterprise distribution, or Microsoft catching OpenAI's frontier models. - Mustafa Suleyman (Mustafa Suleyman), CEO of Microsoft AI, on Decoder with Nilay Patel (nilay patel)

Microsoft AI CEO Mustafa Suleyman explains the exit plan behind the biggest partnership in AI. It began with a Satya Nadella message surfaced in the trial: "I don't want to be Intel and have OpenAI be Microsoft." The escape hardware is already shipping. "Partnerships like that don't last forever. OpenAI wants to be a trillion-dollar public company... They want to have the freedom to operate and be able to buy compute from all sorts of other places, build their own compute, partner with whoever they want." "We've made the decision the early part of last year, which obviously informed all the contract negotiations, which then all got resolved and signed in October." "We now are able to manufacture and ship a chip that is 30% cheaper than a GB200 inside of our own clusters." "The MAI-Thinking-1 model that we've just released actually delivers 1.4x performance per watt improvement on top of the 30% improvement that you get from running on a Maia 200, once we co-optimize the models for our tasks." Microsoft watched Windows commoditize Intel and is refusing the Intel seat this time: own silicon, own frontier model, co-designed end to end. The open question is which gap closes first: OpenAI building Microsoft's enterprise distribution, or Microsoft catching OpenAI's frontier models. - Mustafa Suleyman (Mustafa Suleyman), CEO of Microsoft AI, on Decoder with Nilay Patel (nilay patel)

Karl Mehta

56,899 görüntüleme • 1 ay önce

VAAP Highlights: Devium, built on zkPass. Devium enables private order data + AI by storing users' order data on Devium's decentralized network nodes. It categorizes the data into vertical sectors and trains specialized AI large models for these vertical domains. These large models support enterprise operations, product development, and upgrades within their respective industries. By replacing data hosting with verifiable proofs, Devium reduces platform risks and minimizes user friction. Devium empowers teams to build real-world applications on verifiable infrastructure such as zkTLS.

VAAP Highlights: Devium, built on zkPass. Devium enables private order data + AI by storing users' order data on Devium's decentralized network nodes. It categorizes the data into vertical sectors and trains specialized AI large models for these vertical domains. These large models support enterprise operations, product development, and upgrades within their respective industries. By replacing data hosting with verifiable proofs, Devium reduces platform risks and minimizes user friction. Devium empowers teams to build real-world applications on verifiable infrastructure such as zkTLS.

zkPass

10,856 görüntüleme • 7 ay önce