Почему новый ИИ-чип Apple стал кошмаром для NVIDIA

Apple's M5 Ultra Mac Studio can run AI models that are simply too large to fit inside Nvidia's flagship gaming GPU. With up to 512GB of unified memory, Apple's newest chip offers a major advantage for running large AI models locally. Nvidia's RTX 5090, by comparison, has just 32GB of graphics memory. But does having more memory mean Apple can beat Nvidia at AI? In this video, we compare Apple's M5 Ultra Mac Studio against Nvidia's RTX 5090, DGX Spark, and RTX Pro 6000 to uncover the real battle over local AI, memory capacity, processing speed, software, and price. The results reveal why Apple could become a serious option for developers who want to run powerful AI models privately, without relying on expensive cloud services. 💻 WHY APPLE'S UNIFIED MEMORY MATTERS Large AI models require enormous amounts of memory to load and generate responses. Apple's unified memory lets its CPU, GPU, and AI accelerators share one large pool, allowing the Mac Studio to handle models that exceed the capacity of a typical gaming graphics card. ⚡ APPLE M5 ULTRA VS. NVIDIA RTX 5090 Independent testing reveals a more complicated picture. Nvidia remains faster when a model fits inside its GPU memory, while Apple's Mac Studio can maintain much higher performance when handling workloads that overwhelm the RTX 5090's memory capacity. We explore AI prompt processing, token generation, long-context conversations, and why memory bandwidth can matter just as much as raw computing power. 🧠 THE RACE TO RUN AI LOCALLY From AI coding assistants to autonomous agents, more developers want to run models on their own computers. Local AI can help protect sensitive code, legal documents, and research while reducing recurring cloud costs. Apple's M5 Ultra is designed for exactly this kind of workload. 💰 WHY NVIDIA'S AI HARDWARE IS GETTING MORE EXPENSIVE We examine Nvidia's DGX Spark and RTX Pro 6000, including the rising prices of high-memory AI hardware. As data centers consume increasing amounts of advanced memory, desktop GPUs and compact AI systems face growing supply pressures. 🔧 CUDA VS. APPLE MLX Nvidia's CUDA platform remains a major advantage because much of the AI ecosystem is built around it. Apple's MLX framework is making local AI more accessible on Apple silicon, but software compatibility and developer adoption remain critical challenges. 🔌 CAN MULTIPLE MAC STUDIOS WORK AS ONE AI SYSTEM? Apple says several Mac Studios can be connected to pool memory and run larger AI workloads together. We examine this approach, its potential for small AI labs, and the questions that remain about real-world performance. ☁️ LOCAL AI VS. CLOUD AI Running AI locally offers privacy and predictable hardware costs, but cloud services can deliver dramatically faster responses and access to more powerful models. We explore when buying expensive hardware makes sense—and when renting AI power is still the better choice. THE VERDICT: Nvidia continues to lead in raw speed, AI software, and data-center computing. Apple's advantage is memory capacity in a compact, quiet desktop. Whether that advantage can reshape the local AI market depends on performance, memory prices, and how developers choose to build their tools. 📺 WATCH NEXT: IBM's experimental 0.7-nanometer chip uses a two-level transistor design. Discover how this approach could help fit the high-speed memory needed by AI chips into less silicon and potentially reduce costs. Subscribe to Tomorrow Engineered for deep dives into AI chips, semiconductors, Nvidia, Apple silicon, computing hardware, and the technology shaping the future. #Apple #M5Ultra #Nvidia #RTX5090 #LocalAI #ArtificialIntelligence #MacStudio #AIChips #CUDA #AppleSilicon #MLX #Semiconductors #DGXSpark #Technology