VSvikas sharmainvkdigiservices.hashnode.dev·4d ago · 1 min readGPU Cloud Providers for Modern AI ComputingAI and machine learning workloads require powerful computing infrastructure, especially when working with large models and datasets. GPU cloud providers offer access to high-performance GPU resources 00
VSvikas sharmainvkdigiservices.hashnode.dev·6d ago · 5 min readDedicated GPU Server Hosting: Designing Infrastructure for AI ApplicationsArtificial intelligence workloads are becoming more computationally demanding. Training models, running inference, processing large datasets, and developing GPU-accelerated applications can require in00
VSvikas sharmainvkdigiservices.hashnode.dev·Sep 5 · 6 min readGPU Server Hosting: Scalable Computing for AI and Modern WorkloadsGPU Server Hosting: Scalable Computing for AI and Modern Workloads The growing adoption of artificial intelligence and other computationally intensive technologies has increased the need for powerful 00
GVgpu vendoringpuvendors.hashnode.dev·Aug 19 · 5 min readWhat is JAWA Technology? GPU Acceleration ExplainedIn the rapidly evolving landscape of high performance computing and artificial intelligence, the quest for faster, more efficient data processing is constant. As developers and data scientists push th00
YKYash Karechainyash-karecha.hashnode.dev·Jul 5 · 21 min readI Built My Own ChatGPT Backend to Understand How AI Streaming Actually WorksEvery time I use Claude or ChatGPT I get stuck on the same question: how does the text just show up, word by word, like the model is thinking out loud in real time? Why does it feel instant when it's 00
PKPatrick Kearnsindotnetdigest.com·May 31 · 27 min readCan a .NET endpoint handle a million requests per second?There is a trap in this question. When someone asks whether a .NET endpoint can handle a million requests per second, the instinct is to jump straight into Minimal APIs, Kestrel tuning, JSON serialisa10
WWingEdge777inwingedge777.hashnode.dev·May 24 · 8 min read[CUDA in Practice] RoPE — Why Kernel Fusion in Hand-Written Operators Matters: Reducing Memory Traffic and Launch OverheadAI compilers are evolving fast. In many cases, torch.compile plus JIT optimization in PyTorch can deliver striking speedups, to the point that people often say, "hand-written operators are no longer n10
AMAnirudhan Madhavaninanirudhan.hashnode.dev·May 7 · 5 min readUnderstanding Memory AllocatorsIn most C++ applications, the default new and delete operators handle memory allocation seamlessly. However, for performance-critical applications like game engines, real-time systems, high-frequency 00
WWingEdge777inwingedge777.hashnode.dev·Apr 9 · 33 min read[CUDA in Practice] Matrix Transpose — From Padding to XOR Swizzle: The Art of Shared Memory Access OptimizationNote: Text translated by AI. Code crafted by human. Matrix transpose is one of the most fundamental operations in deep learning and high-performance computing. The deceptively simple coordinate swap 00
DDevOnBikeinoverfit.hashnode.dev·Apr 6 · 4 min read🚀 8x Faster Than ONNX Runtime: Zero-Allocation AI Inference in Pure C#The Myth: "C# is too slow for AI" For years, the narrative has been the same: if you want high-performance AI, you must use C++ or Python wrappers (like PyTorch/ONNX) that call into native kernels. Th00
