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Cloud inference will never be fast enough for robotics? Here's MolmoAct2 VLA model running over cloud on an SO-101 arm. Smooth motions despite the DC being 1000km away.

Chris Yoo

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11,309 просмотров • 2 месяцев назад •via X (Twitter)

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Dreamscale Labs (YC F26) (YC F26) is the first inference provider to serve NVIDIA’s DreamZero robot model over the cloud! And we beat NVIDIA's own reported inference speed by 8%, with just half the silicon. DreamZero is a model that pioneered the new wave of “World Action Models” that has taken over generalist robotics today. It promises to generalize better to new tasks, learn physics from human video, and transfer well to new embodiments. Normally, DreamZero requires 2x GB200 superchips (each costing $60K) or is painfully slow (waits 5.7s before moving 1.6s), which made it infeasible to deploy to robots in practice. Now, with Dreamscale’s real-time cloud inference infrastructure, we’ve demonstrated fast, smooth motion on actual robots with sub-second latency. As scaling laws become more relevant in robotics, frontier physical AI models are growing more computationally expensive than ever. With Dreamscale, we are finally making World Action Models (WAMs) accessible to startups and robot developers across the world. Reach out if you are interested in cloud robot inference.

Dreamscale Labs (YC F26) (YC F26) is the first inference provider to serve NVIDIA’s DreamZero robot model over the cloud! And we beat NVIDIA's own reported inference speed by 8%, with just half the silicon. DreamZero is a model that pioneered the new wave of “World Action Models” that has taken over generalist robotics today. It promises to generalize better to new tasks, learn physics from human video, and transfer well to new embodiments. Normally, DreamZero requires 2x GB200 superchips (each costing $60K) or is painfully slow (waits 5.7s before moving 1.6s), which made it infeasible to deploy to robots in practice. Now, with Dreamscale’s real-time cloud inference infrastructure, we’ve demonstrated fast, smooth motion on actual robots with sub-second latency. As scaling laws become more relevant in robotics, frontier physical AI models are growing more computationally expensive than ever. With Dreamscale, we are finally making World Action Models (WAMs) accessible to startups and robot developers across the world. Reach out if you are interested in cloud robot inference.

Chris Yoo

19,639 просмотров • 1 месяц назад

In Mario Kart Wii, the Thunder Cloud item can be passed off to other players. While this appears to be able to go on forever, there is actually an internal limit to this. After being passed around 255 times, the cloud will have had enough and strike the last player to receive it.

In Mario Kart Wii, the Thunder Cloud item can be passed off to other players. While this appears to be able to go on forever, there is actually an internal limit to this. After being passed around 255 times, the cloud will have had enough and strike the last player to receive it.

Supper Mario Broth

133,835 просмотров • 6 месяцев назад

💥 LeRobot × Zenoh Remote inference just landed in LeRobot: the policy runs on a GPU server or cloud, the robot keeps moving. Cameras, motors and recording stay on the robot computer, actions stream back. What this unblocks: 🤖 Robot side: a laptop or a small board is enough for cameras, motors, teleop and recording. 🖥️ Server side: big model checkpoints run on a GPU server or in the cloud. 🏃 Smooth motion, with or without RTC: the next action chunk is predicted while the current one executes, and new chunks are aligned and blended into the ongoing motion. 🔌 Works on the same machine, over the lab network, or across the internet through a Zenoh router with TLS.

💥 LeRobot × Zenoh Remote inference just landed in LeRobot: the policy runs on a GPU server or cloud, the robot keeps moving. Cameras, motors and recording stay on the robot computer, actions stream back. What this unblocks: 🤖 Robot side: a laptop or a small board is enough for cameras, motors, teleop and recording. 🖥️ Server side: big model checkpoints run on a GPU server or in the cloud. 🏃 Smooth motion, with or without RTC: the next action chunk is predicted while the current one executes, and new chunks are aligned and blended into the ongoing motion. 🔌 Works on the same machine, over the lab network, or across the internet through a Zenoh router with TLS.

LeRobot

13,256 просмотров • 10 дней назад

This massive smoke cloud was all over Varthur yesterday. And caused much consternation and problems for residents all around. The smoke is from garbage being burnt in a private property, which has been ongoing for the last few months. Yesterday, the smoke and irritation was felt all the way in Ramagondanahalli too, despite the source area being ~2km away

This massive smoke cloud was all over Varthur yesterday. And caused much consternation and problems for residents all around. The smoke is from garbage being burnt in a private property, which has been ongoing for the last few months. Yesterday, the smoke and irritation was felt all the way in Ramagondanahalli too, despite the source area being ~2km away

Whitefield Rising

77,028 просмотров • 2 лет назад

📢 Impossible Cloud Network's Node Sale is coming in December! 📢 The countdown begins! This is your chance to become a node operator with ICN, helping to build a secure, decentralized cloud infrastructure while earning rewards for your contribution. More info on the Node Sale will be released soon, so stay tuned! 🚀

📢 Impossible Cloud Network's Node Sale is coming in December! 📢 The countdown begins! This is your chance to become a node operator with ICN, helping to build a secure, decentralized cloud infrastructure while earning rewards for your contribution. More info on the Node Sale will be released soon, so stay tuned! 🚀

Impossible Cloud Network

91,360 просмотров • 1 год назад

Meet Gemma 3n, a model that runs on as little as 2GB of RAM 🤯 It shares the same architecture as Gemini Nano, and is engineered for incredible performance. We added audio understanding, so now it’s multimodal, fast and lean, and runs on-device (no cloud connection required!)

Meet Gemma 3n, a model that runs on as little as 2GB of RAM 🤯 It shares the same architecture as Gemini Nano, and is engineered for incredible performance. We added audio understanding, so now it’s multimodal, fast and lean, and runs on-device (no cloud connection required!)

Google AI

228,990 просмотров • 1 год назад

Long mired in the shallows, yet never broken, it waits for spring. To rise as a dragon, one must brave the current and leap for the heavens. New [Cloud Steps] Mystic Skill VFX [Transcendence Gate] will be available on Oct 25 at 0:00, with a limited-time discount running until Nov 25 at 4:59:59 (UTC+8)! #WhereWindsMeet

Long mired in the shallows, yet never broken, it waits for spring. To rise as a dragon, one must brave the current and leap for the heavens. New [Cloud Steps] Mystic Skill VFX [Transcendence Gate] will be available on Oct 25 at 0:00, with a limited-time discount running until Nov 25 at 4:59:59 (UTC+8)! #WhereWindsMeet

Where Winds Meet

32,809 просмотров • 3 дней назад

MolmoAct2 is landing in LeRobot! Ai2's open Action Reasoning Model combines a Molmo2-ER vision-language backbone with a flow-matching continuous action expert to predict robot action chunks from images, language instructions, and proprioceptive state. An open robot foundation model built for real-world control, with strong out-of-the-box performance and easy fine-tuning in LeRobot. Pick-and-place inference running on NVIDIA DGX Spark! Blog: Paper: Thanks to Ai2 Jiafei Duan Haoquan Fang

MolmoAct2 is landing in LeRobot! Ai2's open Action Reasoning Model combines a Molmo2-ER vision-language backbone with a flow-matching continuous action expert to predict robot action chunks from images, language instructions, and proprioceptive state. An open robot foundation model built for real-world control, with strong out-of-the-box performance and easy fine-tuning in LeRobot. Pick-and-place inference running on NVIDIA DGX Spark! Blog: Paper: Thanks to Ai2 Jiafei Duan Haoquan Fang

LeRobot

25,163 просмотров • 4 месяцев назад

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

Guri Singh

2,180,357 просмотров • 7 месяцев назад

AI agents have arrived on Bittensor. Be the first to launch one. It's fast and easy. TAO AGENTS - Smart contracts on Openτensor Foundaτion - Decentralized inference by Eternal AI - AI Model by AI at Meta (Llama 3.3) You can issue an instantly tradable token for your agent, too.

AI agents have arrived on Bittensor. Be the first to launch one. It's fast and easy. TAO AGENTS - Smart contracts on Openτensor Foundaτion - Decentralized inference by Eternal AI - AI Model by AI at Meta (Llama 3.3) You can issue an instantly tradable token for your agent, too.

Eternal AI

36,552 просмотров • 1 год назад

Ekane says Aspen kartier can never be cancelled despite all the hate she has been getting on the internet for ab6s!ng her dog and she just need enough time away from the internet so they can forget about the incident🫣🥀 “She’s not cancelled, y’all still worried about her”

Ekane says Aspen kartier can never be cancelled despite all the hate she has been getting on the internet for ab6s!ng her dog and she just need enough time away from the internet so they can forget about the incident🫣🥀 “She’s not cancelled, y’all still worried about her”

flame⛧

27,478 просмотров • 8 месяцев назад

Thesis: In 3 years time or less, the most viral games will be hosted on the YOM cloud gaming network, because the risk for experimentation has dropped nearly to the floor, and the ability to share the game has gone through the roof. Why are we so bold? Read on.

Thesis: In 3 years time or less, the most viral games will be hosted on the YOM cloud gaming network, because the risk for experimentation has dropped nearly to the floor, and the ability to share the game has gone through the roof. Why are we so bold? Read on.

YOM

84,295 просмотров • 1 год назад

i spent $26,600 on cloud GPU rentals over 14 months before i found a NVIDIA DGX Spark at $2,999 (founder's edition) or $3,999 (shipping price) it paid for itself in 6 weeks i run 200B parameter models locally now and my old cloud provider keeps sending me loyalty discount emails the math on that $26,600 is embarrassing to type out loud $1,900/month for 14 months, H100 instances on a specialist cloud provider, because anything bigger than a 70B model simply would not fit anywhere else i paid the invoices like they were a utility bill and told myself it was just the cost of doing serious AI work it took me over a year to find out it wasn't 14 months, broken down: → months 1-4: $1,400-1,600/month - felt like manageable infrastructure overhead → months 5-9: crept to $1,900-2,100 as i started running DeepSeek-class experiments, costs tracking directly with model size → months 10-12: one agent loop ran for 36 hours against a 130B model while i slept, that month hit $2,400 → month 13: ran the cumulative total for the first time, saw $23,800, felt physically sick → month 14: another $2,800 month while i waited for the hardware to ship the box is the NVIDIA DGX Spark - roughly the footprint of a large mac mini, powered by a GB10 Grace Blackwell chip with 128GB of unified LPDDR5X memory that unified memory is the whole thing an RTX 4090 has 24GB of VRAM, which means a 70B model in full BF16 precision physically does not fit, you're quantizing down or you're renting cloud, those are your options this box loads a 200B parameter model quantized and serves it through vLLM over localhost, same API interface the cloud endpoint used the migration took one line of code - i changed the base URL from the provider's endpoint to 127.0.0.1:8000 and everything just worked electricity to run continuous 200B inference locally comes out to about $12/month the payback arithmetic is almost too clean: $2,999 hardware cost against $1,900/month saved, the box paid for itself before i'd owned it two months what i didn't account for was how completely the cost model changes your behavior when there's no hourly meter running, you greenlight experiments you'd never approve on cloud - agent loops that churn for hours, running 10,000 documents through a reasoning pass at 3am, speculative fine-tuning jobs you'd normally skip because the cost felt unjustifiable i ran more experiments in the first 30 days after the box arrived than in the four months before it the loyalty discount email landed about 8 weeks after i cancelled the cloud subscription 15% off my next three months, valued customer, we'd love to have you back i didn't reply the box was already running

i spent $26,600 on cloud GPU rentals over 14 months before i found a NVIDIA DGX Spark at $2,999 (founder's edition) or $3,999 (shipping price) it paid for itself in 6 weeks i run 200B parameter models locally now and my old cloud provider keeps sending me loyalty discount emails the math on that $26,600 is embarrassing to type out loud $1,900/month for 14 months, H100 instances on a specialist cloud provider, because anything bigger than a 70B model simply would not fit anywhere else i paid the invoices like they were a utility bill and told myself it was just the cost of doing serious AI work it took me over a year to find out it wasn't 14 months, broken down: → months 1-4: $1,400-1,600/month - felt like manageable infrastructure overhead → months 5-9: crept to $1,900-2,100 as i started running DeepSeek-class experiments, costs tracking directly with model size → months 10-12: one agent loop ran for 36 hours against a 130B model while i slept, that month hit $2,400 → month 13: ran the cumulative total for the first time, saw $23,800, felt physically sick → month 14: another $2,800 month while i waited for the hardware to ship the box is the NVIDIA DGX Spark - roughly the footprint of a large mac mini, powered by a GB10 Grace Blackwell chip with 128GB of unified LPDDR5X memory that unified memory is the whole thing an RTX 4090 has 24GB of VRAM, which means a 70B model in full BF16 precision physically does not fit, you're quantizing down or you're renting cloud, those are your options this box loads a 200B parameter model quantized and serves it through vLLM over localhost, same API interface the cloud endpoint used the migration took one line of code - i changed the base URL from the provider's endpoint to 127.0.0.1:8000 and everything just worked electricity to run continuous 200B inference locally comes out to about $12/month the payback arithmetic is almost too clean: $2,999 hardware cost against $1,900/month saved, the box paid for itself before i'd owned it two months what i didn't account for was how completely the cost model changes your behavior when there's no hourly meter running, you greenlight experiments you'd never approve on cloud - agent loops that churn for hours, running 10,000 documents through a reasoning pass at 3am, speculative fine-tuning jobs you'd normally skip because the cost felt unjustifiable i ran more experiments in the first 30 days after the box arrived than in the four months before it the loyalty discount email landed about 8 weeks after i cancelled the cloud subscription 15% off my next three months, valued customer, we'd love to have you back i didn't reply the box was already running

Argona

22,467 просмотров • 4 месяцев назад

most of you don't know how hard hermes agent is optimized for local AI at the system level. watch the full setup flow on screen. you paste an openai-compatible v1 endpoint, hermes auto-detects every model running behind it. doesn't matter if it's llama.cpp or vllm or any compatible server, all your models surface and become selectable in seconds. no config gymnastics, no manual model list. then it goes deeper. hermes ships with per model parsers, prompt template auto-handling, tool call format detection per model architecture, thinking mode awareness, all the small friction points other harnesses leak on. these were not built for cloud apis with one canonical model. they were built for builders running 10 different local models across 10 different stacks. cloud first harnesses bolt local support on top. hermes agent is local first from the architecture out. that's the system level gap. if you're getting started on local AI, this is the harness you start with. try for yourself and find out. anyone serious about local AI lands here eventually.

most of you don't know how hard hermes agent is optimized for local AI at the system level. watch the full setup flow on screen. you paste an openai-compatible v1 endpoint, hermes auto-detects every model running behind it. doesn't matter if it's llama.cpp or vllm or any compatible server, all your models surface and become selectable in seconds. no config gymnastics, no manual model list. then it goes deeper. hermes ships with per model parsers, prompt template auto-handling, tool call format detection per model architecture, thinking mode awareness, all the small friction points other harnesses leak on. these were not built for cloud apis with one canonical model. they were built for builders running 10 different local models across 10 different stacks. cloud first harnesses bolt local support on top. hermes agent is local first from the architecture out. that's the system level gap. if you're getting started on local AI, this is the harness you start with. try for yourself and find out. anyone serious about local AI lands here eventually.

Sudo su

14,407 просмотров • 5 месяцев назад

We just ran one of the largest robot models that exists today, all without a local GPU. And it’s running real-time at <50 ms overhead. Scaling laws are becoming real for physical AI. Perceptron’s Isaac 0.5 is one of the largest open-source models, sitting at 36B total parameters. Perceptron AI joins other frontier robotics labs like Dyna Robotics and Generalist in showing the need to massively scale pre-training data, from a 1000s to millions of hours of video. Specifically, they show that the more egocentric video the model is trained on, the less teleop data that it needs to achieve the same loss. At Dreamscale Labs (YC F26) (YC F26), we’ve noticed an important truth here: data and training compute do not just scale in isolation. The model sizes must increase too, and GPT-6 Astra has given a glimpse into what a trillion-parameter robotics model could do. We’re building to serve that future - one that is not constrained by the bottlenecks of on-board compute, but rather, allowing large models to run fast over the cloud. Reach out if you’re interested in our work.

We just ran one of the largest robot models that exists today, all without a local GPU. And it’s running real-time at <50 ms overhead. Scaling laws are becoming real for physical AI. Perceptron’s Isaac 0.5 is one of the largest open-source models, sitting at 36B total parameters. Perceptron AI joins other frontier robotics labs like Dyna Robotics and Generalist in showing the need to massively scale pre-training data, from a 1000s to millions of hours of video. Specifically, they show that the more egocentric video the model is trained on, the less teleop data that it needs to achieve the same loss. At Dreamscale Labs (YC F26) (YC F26), we’ve noticed an important truth here: data and training compute do not just scale in isolation. The model sizes must increase too, and GPT-6 Astra has given a glimpse into what a trillion-parameter robotics model could do. We’re building to serve that future - one that is not constrained by the bottlenecks of on-board compute, but rather, allowing large models to run fast over the cloud. Reach out if you’re interested in our work.

Chris Yoo

13,623 просмотров • 22 дней назад

Ready to bring AI into the physical world? 🤖 Join our livestream on Tuesday, August 11th at 9:00am PT to learn how to: 🧠 Run and fine-tune NVIDIA GR00T 1.7 🔗 Connect models, sensors, and actuators with ROS 2 🦾 Deploy a model on the SO-101 robot arm to execute a real-world task 🎙️Guest speaker Nikodem Bartnik will also share the latest advances and highlights from the open-source robotics community. Add to calendar:

Ready to bring AI into the physical world? 🤖 Join our livestream on Tuesday, August 11th at 9:00am PT to learn how to: 🧠 Run and fine-tune NVIDIA GR00T 1.7 🔗 Connect models, sensors, and actuators with ROS 2 🦾 Deploy a model on the SO-101 robot arm to execute a real-world task 🎙️Guest speaker Nikodem Bartnik will also share the latest advances and highlights from the open-source robotics community. Add to calendar:

NVIDIA Robotics

23,747 просмотров • 2 месяцев назад

Both China and the US have created their own Military AI models using commercially available AI.📜 China for instance is using DeepSeek as the foundational model, trained in air-gapped, physically isolated networks. Using supercomputers and datacenters for fine-tuning the AI model on classified military data; Running secure inference in operational environments, and integrating AI into command-and-control, intelligence analysis, targeting, logistics, and autonomous systems. This is why datacenters are like centrifuges in nuclear weapon manufacturing, but for AI. With these AI models, the PLA can utilize AI to plan a military campaign, from drawing out the optimal supply route, to risk assessment in a specific strike mission, to controlling a drone swarm 1000km away. All can be done in an instant. Somewhere in China, DeepSeek is being fed classified data on how to plan a military operation or recognize a M1 Abram tank on a IIR sensor or the optimal altitude and speed to drop a bunker buster bomb onto target.

Both China and the US have created their own Military AI models using commercially available AI.📜 China for instance is using DeepSeek as the foundational model, trained in air-gapped, physically isolated networks. Using supercomputers and datacenters for fine-tuning the AI model on classified military data; Running secure inference in operational environments, and integrating AI into command-and-control, intelligence analysis, targeting, logistics, and autonomous systems. This is why datacenters are like centrifuges in nuclear weapon manufacturing, but for AI. With these AI models, the PLA can utilize AI to plan a military campaign, from drawing out the optimal supply route, to risk assessment in a specific strike mission, to controlling a drone swarm 1000km away. All can be done in an instant. Somewhere in China, DeepSeek is being fed classified data on how to plan a military operation or recognize a M1 Abram tank on a IIR sensor or the optimal altitude and speed to drop a bunker buster bomb onto target.

Zhao DaShuai 东北进修🇨🇳

14,727 просмотров • 6 дней назад

Hindus continue to visit the Golden Temple at Amritsar to be humiliated by the gurdwara staff. This woman is so lacking in awareness that she continues to smile for the camera despite being pushed by a thug. I dare these staff to touch a Malayali woman in Kerala. He will go back to Punjab without his arm.

Hindus continue to visit the Golden Temple at Amritsar to be humiliated by the gurdwara staff. This woman is so lacking in awareness that she continues to smile for the camera despite being pushed by a thug. I dare these staff to touch a Malayali woman in Kerala. He will go back to Punjab without his arm.

Rakesh Krishnan Simha

45,723 просмотров • 1 месяц назад

Proud to announce the in-depth collaboration between Kingnet and Alibaba Cloud in AI Gaming. Alibaba Cloud provides world-leading cloud computing, big data, and AI services, with disclosed revenue exceeding $15 billion in 2024, which is one of the most renowned global server providers. When two superpowers collide, the game changes. 🌊AI Gaming R&D By integrating Qwen 's LLM and Alibaba Cloud 's PAI platform (including PAI-iTAG, PAI-Designer, PAI-DSW, PAI-DLC, and PAI-EAS), Kingnet has emerged as one of the gaming industry's pioneers in AIGC-powered content generation and AI rendering. Together, we are accelerating the realization of no-code game development. 🌊GPU Computing Resources Alibaba Cloud delivers GPU-accelerated elastic computing services with exceptional processing power, supporting diverse workloads including deep learning, scientific computing, graphics visualization, and video processing - providing robust GPU computing capabilities for KingnetAI's demanding requirements. 🌊Cloud Service Optimization Cloud server deployment has become the mainstream choice for small and mid-sized game studios in global operations. Leveraging Alibaba Cloud server advantages, we will develop and deploy more cloud-native games to meet user demands. The disruptive innovation we're bringing to the industry: 🔸Minute-scale game asset production replaces traditional week/month-long cycles 🔸Single-digit dollar development costs VS traditional four-figure entry thresholds 🔸AI-powered NPCs with behavioral engines deliver dynamic player interactions, breaking static story constraints, etc. 🔜Kingnet AI V2 is approaching launch. The Agent system and game generation engine will be officially deployed across 3 chains: 🔹Leveraging Solana high throughput and low gas fee , Solana has consistently been a developer favorite, latest product will be deployed on Solana - with users paying $SOL for on-demand asset creation fees. 🔹Another key partner is BNB Chain ,We are actively participating in both the #BNBAIHack and the latest MVB 10. Powered by BNB Chain long-standing support for AI innovation. Kingnet V2 and NFT drop will be deployed on BNB Chain, providing developers and the community with comprehensive game-generation tools and support. 🔹As an early strategic partner of Kingnet, TON 💎 @TONEastAsia was one of the earliest chain to connect Web2 and Web3, Kingnet V2 will be deployed on TON, providing TON game developers with low-cost, high-efficiency asset generation, and supporting users to use $TON as an asset generation cost. The Future of AI Gaming is coming.

Proud to announce the in-depth collaboration between Kingnet and Alibaba Cloud in AI Gaming. Alibaba Cloud provides world-leading cloud computing, big data, and AI services, with disclosed revenue exceeding $15 billion in 2024, which is one of the most renowned global server providers. When two superpowers collide, the game changes. 🌊AI Gaming R&D By integrating Qwen 's LLM and Alibaba Cloud 's PAI platform (including PAI-iTAG, PAI-Designer, PAI-DSW, PAI-DLC, and PAI-EAS), Kingnet has emerged as one of the gaming industry's pioneers in AIGC-powered content generation and AI rendering. Together, we are accelerating the realization of no-code game development. 🌊GPU Computing Resources Alibaba Cloud delivers GPU-accelerated elastic computing services with exceptional processing power, supporting diverse workloads including deep learning, scientific computing, graphics visualization, and video processing - providing robust GPU computing capabilities for KingnetAI's demanding requirements. 🌊Cloud Service Optimization Cloud server deployment has become the mainstream choice for small and mid-sized game studios in global operations. Leveraging Alibaba Cloud server advantages, we will develop and deploy more cloud-native games to meet user demands. The disruptive innovation we're bringing to the industry: 🔸Minute-scale game asset production replaces traditional week/month-long cycles 🔸Single-digit dollar development costs VS traditional four-figure entry thresholds 🔸AI-powered NPCs with behavioral engines deliver dynamic player interactions, breaking static story constraints, etc. 🔜Kingnet AI V2 is approaching launch. The Agent system and game generation engine will be officially deployed across 3 chains: 🔹Leveraging Solana high throughput and low gas fee , Solana has consistently been a developer favorite, latest product will be deployed on Solana - with users paying $SOL for on-demand asset creation fees. 🔹Another key partner is BNB Chain ,We are actively participating in both the #BNBAIHack and the latest MVB 10. Powered by BNB Chain long-standing support for AI innovation. Kingnet V2 and NFT drop will be deployed on BNB Chain, providing developers and the community with comprehensive game-generation tools and support. 🔹As an early strategic partner of Kingnet, TON 💎 @TONEastAsia was one of the earliest chain to connect Web2 and Web3, Kingnet V2 will be deployed on TON, providing TON game developers with low-cost, high-efficiency asset generation, and supporting users to use $TON as an asset generation cost. The Future of AI Gaming is coming.

Kingnet AI

149,965 просмотров • 1 год назад