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Drug discovery is one of the most expensive and time-consuming endeavors in human history. It takes roughly 10 to 15 years to go from target discovery to regulatory approval for a new drug in the United States. Most of that time is spent not in breakthrough moments, but in painstaking analytical work — sifting through […]
The intersection of many-body physics and deep learning has opened a new frontier: Neural Quantum States (NQS). While traditional methods struggle with high-dimensional frustrated systems, the global attention mechanism of Transformers provides a powerful tool for capturing complex quantum correlations. In this tutorial, we implement a research-grade Variational Monte Carlo (VMC) pipeline using NetKet and
Building Transformer-Based NQS for Frustrated Spin Systems with NetKet Read More »
In this tutorial, we implement NVIDIA PhysicsNeMo on Colab and build a practical workflow for physics-informed machine learning. We start by setting up the environment, generating data for the 2D Darcy Flow problem, and visualizing the physical fields to clearly understand the learning task. From there, we implement and train powerful models such as the
Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or Qwen3 works through a complex math problem, it can generate tens of thousands of tokens before arriving at an answer. Every one of those tokens must be stored in what is called the KV cache
Complex prediction problems often lead to ensembles because combining multiple models improves accuracy by reducing variance and capturing diverse patterns. However, these ensembles are impractical in production due to latency constraints and operational complexity. Instead of discarding them, Knowledge Distillation offers a smarter approach: keep the ensemble as a teacher and train a smaller student
Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment you move beyond plain text and start mixing in images and videos, the whole approach starts to buckle. Visual data is token-heavy, semantically sparse relative to a specific query, and grows unwieldy fast during multi-step
In this tutorial, we take a detailed, practical approach to exploring NVIDIA’s KVPress and understanding how it can make long-context language model inference more efficient. We begin by setting up the full environment, installing the required libraries, loading a compact Instruct model, and preparing a simple workflow that runs in Colab while still demonstrating the
In this tutorial, we implement an advanced, practical implementation of the NVIDIA Transformer Engine in Python, focusing on how mixed-precision acceleration can be explored in a realistic deep learning workflow. We set up the environment, verify GPU and CUDA readiness, attempt to install the required Transformer Engine components, and handle compatibility issues gracefully so that
By Spritle Software Engineering Team Workplace safety isn’t negotiable. But manual safety compliance monitoring is slow, inconsistent, and doesn’t scale. We built a real-time Personal Protective Equipment (PPE) detection app that runs entirely on your smartphone — no cloud, no expensive hardware, no delays. The Problem We’re Solving Every year, thousands of workplace accidents happen
Open Sourcing Our Real-Time PPE Detection Mobile App Read More »
Run Google’s latest omni-capable open models faster on NVIDIA RTX AI PCs, from NVIDIA Jetson Orin Nano, GeForce RTX desktops to the new DGX Spark, to build personalized, always-on AI assistants like OpenClaw without paying a massive “token tax” for every action. The landscape of modern AI is shifting rapidly. We are moving away from
