Live Coding: Finalizing Custom Memory Allocator for Tensors and LLM Projections | Part 1

In this deep-dive live coding session, we push toward the finish line of a high-performance custom memory allocator designed specifically for tensor operations and LLM projection layers. If you're interested in squeezing out every bit of efficiency from your machine learning systems, this stream is for you. We’ll walk through: 🔧 Designing a memory allocator optimized for tensor workloads ⚡ Reducing fragmentation and improving cache locality 🧠 Handling projection-heavy workloads in large language models 🛠️ Debugging, profiling, and benchmarking performance gains 📈 Real-world considerations for scaling ML systems Whether you're into systems programming, deep learning infrastructure, or just want to see how low-level optimizations impact high-level AI models, you’ll find practical insights and hands-on techniques here. 💬 Feel free to ask questions during the stream — we’ll be exploring, debugging, and optimizing in real time. 👉 https://github.com/umairgillani93/miniTorch 🌐 Connect with me: • GitHub → https://github.com/umairgillani93 • LinkedIn → https://linkedin.com/in/umairgillani93 • Twitter/X → https://x.com/UmairGillani93 🔥 Don’t forget to: 👍 Like the video 💬 Comment your thoughts or questions #LiveCoding #MachineLearning #DeepLearning #LLM #Transformers #Tensor #MemoryManagement #SystemsProgramming #Cplusplus #Python #AIEngineering #MLInfrastructure #PerformanceOptimization #LowLevelProgramming #CUDA #GPUProgramming #NeuralNetworks #ArtificialIntelligence #TechStream #CodingStream #SoftwareEngineering #Allocator #MemoryAllocator #Optimization #Profiling #Debugging #HighPerformanceComputing #HPC #CacheOptimization #DataStructures #Algorithms #DevStream #ProgrammerLife #CodeNewbie #LearnToCode #TechYouTube #OpenSource #Coding #EngineerLife #BackendEngineering #AI #ML #CodeOptimization