Patterns that Connect: AI, Management, Metaverse, Quantum, Philosophy, and Physics

  Leverage 128GB unified memory for instant model hot-swapping Copyright: Sanjay Basu The Model Loading Problem Waiting for a large AI model to initialize often involves a long, frustrating delay. During this time, your GPU remains idle as weights are transferred through multiple bottlenecks, leading to significant latency. For those operating local AI setups, this startup delay can determine whether the system feels quick and responsive or sluggish and vexing.. Now imagine running multiple large models on a single GPU, and switching between them in seconds. That’s exactly what flashtensors enables, and on the DGX Spark’s 128GB unified memory architecture, this capability becomes particularly powerful. Why DGX Spark is Ideal for flashtensors The DGX Spark’s Grace Blackwell architecture provides unique advantages for flashtensors’ direct memory streaming approach: Copyright: Sanjay Basu The shared memory architecture removes the old bottleneck caused by data transfers between ...

  At the Threshold of Consciousness, Computation, and the Questions We’re Afraid to Ask Copyright: Sanjay Basu Here’s a confession that will irritate the techno-utopians and the AI doomers in equal measure. I spent Thanksgiving break not thinking about artificial intelligence. I failed spectacularly. Between books that ostensibly had nothing to do with machine learning, between long walks through autumn leaves that should have cleared my head of tensor operations and attention mechanisms, between conversations with family members who still think “the cloud” is a weather phenomenon, the questions kept surfacing. Not the questions that dominate LinkedIn feeds and venture capital pitch decks. Not “Will AI take my job?” or “When will we achieve AGI?” Those are the wrong questions, asked by people who haven’t yet realized they’re asking the wrong questions. The real questions are older. Much older. They’re the questions philosophers have wrestled with for millennia, now dressed in ...

  Copyright: Sanjay Basu Freud’s Dark Idea That Explains More About Life Than Death Freud had a talent for dropping theoretical grenades into polite conversation. One of his most explosive? “That part of you that wants to die.” Imagine saying that at a dinner party. People would reach for the wine faster than you can say psychoanalysis. But that’s what Freud meant by Thanatos — the death drive. A quiet, persistent whisper inside us pulling toward dissolution, stillness, oblivion. Not in a dramatic gothic way, but in the subtle ways we sabotage progress, repeat bad patterns, and drift toward entropy when nobody’s watching. Thanatos, in Freud’s world, isn’t some spooky shadow lurking in your bedroom at night. It’s the reason you sometimes choose the option that harms you, confuses you, or makes no rational sense. It’s gravity for the psyche. And like gravity, you barely notice it until you trip. ☠️ We’re living in a golden age of self-optimisation. Mindfulness apps nudge us toward se...

  How LLM-D Parallel Runs Are Rewriting the Rules of Model Inference Copyright: Sanjay Basu When One Brain Isn’t Enough What if the secret to making AI faster wasn’t building bigger machines, but teaching it to think with two minds at once? For anyone who’s ever typed a prompt into ChatGPT and watched those little dots dance across the screen, there’s an invisible orchestra playing behind the curtain. Large language models don’t just materialize answers from thin air. They’re running a two-act play every single time: first, they digest your question (prefill), and then they generate your answer, token by token (decode). Traditionally, these two acts happened on the same stage, using the same resources. And like any double-booked theater, chaos ensued. Enter LLM-D, the distributed inference framework that said, “What if we gave each act its own theater?” The result? A system that can serve AI models faster, cheaper, and more reliably by splitting the inference process into spec...