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Ethernet is DEAD?? Mac Studio is 100x FASTER!! 34 min 
How To Manage Token Costs While Keeping Agentic Goals On Track 5 min 
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AI News: The AI Agent Race Just Exploded 35 min 
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GTA 6 ПЗДЦ, The Witcher 4, METRO 2039, Успіх STALKER 2, Провал Sony, Days Gone, Клоуни IGN, Rockstar 35 min 
Lessons From Training Composer At Cursor And Building Meta/Nvidia Compute Clusters | YC Paper Club 77 min 
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IBM’s mainframe chip collab, NVIDIA’s Poolside deal & Ox Alpha’s reveal 31 min 
NVIDIA CEO Jensen Huang's Vision for the Future 64 min 
How Lifelike AI Became Amarillo's Face — And A New Gateway To City Services 4 min 
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Terra9. Short fantasy Sci-fi story about one of Humanities first Extra Terrestrial settlements 2 min 
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Solar Farms in Space - Reflect Orbital 32 min 
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Samsung S26 Ultra Review - Breaking Point. 17 min 
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The new Samsung INVISIBLE Display! 1 min Hey…just try Twingate….you'll never look at VPN the same: https://ntck.co/twingate-networkchuck I built another AI supercomputer with 4 Mac Studios... but this time it actually works. Earlier this year, I clustered 5 Mac Studios and it was 91% SLOWER. Everyone said clustering was stupid. But Apple just dropped a software update that changes everything - RDMA over Thunderbolt 5. Latency dropped from 300 microseconds to 3 microseconds. Now we're running trillion-parameter models locally at speeds that actually make sense. 🔥🔥Join the NetworkChuck Academy!: https://ntck.co/NCAcademy RESOURCES / LINKS: Docs/walkthrough: https://github.com/theNetworkChuck/mac-studio-cluster Exo Labs: https://github.com/exo-explore/exo MLX (Apple's ML Framework): https://github.com/ml-explore/mlx My First Cluster Video (the failure): https://youtu.be/Ju0ndy2kwlw RDMA Networking Explained: https://youtu.be/fb69FyW2KLk TIMESTAMPS: 0:00 - The $50,000 AI Supercomputer 0:53 - What Apple Changed 3:05 - Connecting the Cluster 4:17 - Pipeline vs Tensor Parallelism 7:52 - RDMA: The 100x Latency Fix 10:02 - Twingate (Sponsor) 11:39 - Exo Labs is BACK 14:42 - Single Node vs Cluster Testing 17:58 - Qwen 3 Coder 480B Testing 19:03 - Kimi K2 (1 Trillion Parameters) 21:09 - Stacking Multiple Models 25:22 - Real Apps: Open WebUI + Xcode 27:57 - Final Thoughts 28:47 - How MLX Makes This Possible **Sponsored by Twingate THE SPECS: • 4x Mac Studio M4 Ultra (512GB RAM each) • 2TB unified memory / 320 GPU cores / 32TB storage • $50,000 (vs $780,000+ for equivalent NVIDIA H100s) THE RESULTS: • Llama 3.3 70B: 16 tok/s (3x faster than before) • Kimi K2 (1T params): 28 tok/s • DeepSeek V3.1 671B: 27 tok/s • Qwen 3 Coder 480B: 40 tok/s SUPPORT NETWORKCHUCK --------------------------------------------------- 🎓🎓 Sign up for NetworkChuck Academy: https://ntck.co/NCAcademy ☕☕ COFFEE and MERCH: https://ntck.co/coffee 🌐🌐 Use the MOST SECURE Web Browser, NetworkChuck Cloud Browser: https://browser.networkchuck.com/ 🧠🧠 Use n8n, my favorite automation tool: https://ntck.co/n8n 🆘🆘 NEED HELP?? Join the Discord Server: https://discord.gg/networkchuck STUDY WITH ME on Twitch: https://bit.ly/nc_twitch READY TO LEARN?? --------------------------------------------------- -Sign up for NetworkChuck Academy: https://ntck.co/NCAcademy -Get your CCNA: https://bit.ly/nc-ccna FOLLOW ME EVERYWHERE --------------------------------------------------- Instagram: https://www.instagram.com/networkchuck/ Twitter: https://twitter.com/networkchuck Facebook: https://www.facebook.com/NetworkChuck/ Join the Discord server: http://bit.ly/nc-discord Do you want to know how I draw on the screen?? Go to https://ntck.co/EpicPen and use code NetworkChuck to get 20% off!! clustering works now. thank Apple and Exo Labs. # # # TAGS: mac studio cluster, ai supercomputer, local ai, rdma, exo labs, apple silicon, m4 ultra, unified memory, tensor parallelism, llm, kimi k2, deepseek, llama, mlx, thunderbolt 5, home lab ai, self hosted ai, 2tb ram, gpu cluster, apple ai
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