6d ago · 7 min read · TL;DR After del tensor; torch.cuda.empty_cache(), PyTorch’s caching allocator still holds 53.7 MB that it won’t release. We traced the CUDA Runtime and Driver APIs with eBPF uprobes to see exactly wh
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May 24 · 8 min read · I started this project thinking it would take a month. It took closer to five or six. Not because video calls are hard — WebRTC tutorials are everywhere. But because the moment you add a real-time ML
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May 21 · 6 min read · TL;DR PyTorch’s DataLoader can be 50-124x slower than direct tensor indexing for in-memory GPU workloads. We reproduced a real PyTorch issue on an RTX 4090 and traced every CUDA API call and Linux ke
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May 14 · 6 min read · TL;DR A .cpu().numpy() call buried inside a forward pass was forcing a full CPU-GPU synchronization on every batch, every loop iteration. The GPU would finish its work in milliseconds, then sit idle
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May 11 · 1 min read · This article analyzes the evolution of machine learning, which has evolved from a niche craft into the foundation of digital industry. The author points out that modern AI is not just algorithms but, above all, a complex MLOps infrastructure, encompa...
Join discussionMay 3 · 3 min read · Let's be honest: the current meta of AI development is just brute-forcing parameters until we run out of VRAM. It’s expensive, it’s inefficient, and it’s definitely not how a biological brain actually
Join discussionApr 28 · 3 min read · Introduction As the field of machine learning continues to blossom, the intricacies of neural network performance must be scrutinized more closely than ever. One phenomenon that has gained attention is gradient misalignment, which can severely affect...
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Apr 28 · 3 min read · Introduction In the realm of artificial intelligence and machine learning, model complexity often correlates with performance. However, the balance between interpretability and efficiency can be a challenging one. Tridiagonal models, particularly whe...
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