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How Nothing Could Destroy the Universe

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  Copyright: Sanjay Basu Nothing has always been more dangerous than it sounds. For most of daily life, nothing is a complaint, not a concept. You open the fridge. You sigh. There is nothing to eat. This sort of nothing is negotiable. It depends on hunger, expectations, and how brave you feel about expired yogurt. Physics is not interested in that kind of emptiness. Physics worries about stricter kinds of nothing. There is the modest version, what philosophers might call nothing with a lowercase n. You start with something and remove it piece by piece. Matter goes. Air follows. Radiation fades. What remains is a vacuum. Sparse. Cold. Seemingly empty. But still something. Then there is Nothing, capital N. Absolute absence. No space. No time. No fields. No laws waiting quietly in the wings. Not emptiness, but non-being. It is hard to imagine because imagination itself requires a stage. If true Nothing exists, it cannot be part of the universe. It cannot interact with it. It cannot ev...
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Fine-Tuning Language Models on NVIDIA DGX Spark

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 Complete How-To Guide Copyright: Sanjay Basu Overview This guide provides comprehensive instructions for fine-tuning open-source language models on the NVIDIA DGX Spark personal AI supercomputer. The DGX Spark’s unique 128GB unified memory architecture enables local training of models that would traditionally require cloud infrastructure. Fine-tuning allows you to customize pre-trained models for specific tasks, domains, or response styles while preserving their general capabilities. This guide covers three fine-tuning strategies: Full fine-tuning for maximum customization, LoRA for memory-efficient adaptation, and QLoRA for training even larger models within memory constraints. DGX Spark Hardware Advantages The NVIDIA DGX Spark provides several key advantages for local AI development: 128GB Unified Memory: CPU and GPU share the same memory pool via NVLink-C2C, eliminating memory transfer bottlenecks Grace Blackwell Architecture: Purpose-built for AI workloads with up to 1 PF...
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The Grammar of Structure

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What the Langlands Program Might Tell Us About Learning Machines Copyright: Sanjay Basu I. Introduction There is a persistent mystery at the heart of mathematics. Objects that appear entirely unrelated, defined in different languages, studied by different communities, sometimes turn out to encode the same information. A question about prime numbers becomes equivalent to a question about symmetries of certain functions. A problem in algebra transforms into a problem in analysis. The translation is not metaphorical. It is exact. This phenomenon troubles people who encounter it for the first time. Mathematics is supposed to be about definitions and consequences. If you define two things differently, why should they be the same? And yet they are. Again and again. Robert Langlands, working at the Institute for Advanced Study in the late 1960s, proposed something ambitious: that these scattered coincidences were not accidents but symptoms of a deeper unity. His conjectures suggested that...
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Run multiple LLMs on your DGX Spark with flashtensors

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  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 ...
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