🔐 Where does my data go with AI? How do I protect IP? Short answer: 𝐫𝐮𝐧 𝐀𝐈 & 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐥𝐨𝐜𝐚𝐥𝐥𝐲 on your own machines with the right GPU. ->(brief summary, watch video)<-
𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐟𝐞𝐥𝐭 𝐥𝐢𝐤𝐞 𝐀𝐥𝐚𝐝𝐝𝐢𝐧’𝐬 𝐥𝐚𝐦𝐩: instant wishes, instant wins. My job as a CEO is simple—deliver results and boost efficiency. If an AI agent can do it faster, better, cheaper—that’s a real game changer (NVIDIA AI).
𝐖𝐡𝐲 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐜𝐚𝐫𝐞
- Keep sensitive data in-house (No internet connection required)
- Faster results, lower latency (Fastest solution)
- Quietness of the Workstation Edition (Office Use)
But what do decision-makers need to consider when operating AI models themselves?
🧠 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐰𝐚𝐭𝐜𝐡 𝐟𝐨𝐫 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐧𝐠 𝐀𝐈 𝐦𝐨𝐝𝐞𝐥𝐬
- VRAM = brain size → plan ~+20% above model size
- TFLOPS/TOPS = thinking speed
- Training vs. Inference: use pre-trained open-source models
- AI model Parameters ≈ expertise: bigger models → better answers → stronger hardware (GPU)
💼 𝐌𝐲 𝐞𝐱𝐞𝐜 𝐩𝐢𝐜𝐤 𝐟𝐨𝐫 𝐥𝐨𝐜𝐚𝐥 𝐀𝐈
NVIDIA RTX PRO 6000 (Blackwell) – Workstation Edition
- 96 𝐆𝐁 𝐆𝐃𝐃𝐑7 (ECC) for large models & reliability
- 𝐔𝐩 𝐭𝐨 4 𝐆𝐏𝐔𝐬 (MIG) → 𝐭𝐨 384 𝐆𝐁 𝐕𝐑𝐀𝐌, multi-user, secure isolation
- ≈4,000 𝐓𝐎𝐏𝐒 & 1,792 𝐆𝐁/𝐬 𝐛𝐚𝐧𝐝𝐰𝐢𝐝𝐭𝐡h → faster cycles, fewer bottlenecks
- 𝐂𝐔𝐃𝐀 𝐞𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 = compatibility, efficiency, reliability
I’ll keep breaking this down for decision-makers.
𝐏𝐒: What should I cover next AI Video? Comment below?
𝐏𝐏𝐒: What do you think of the combination of the AI voice and my own voice in the video? Is the future a combination of hybrid AI-human teams?