PyTorch

An in-depth analysis of how Shopify leverages PyTorch and vLLM to create high-efficiency, low-cost continual learning pipelines for production-grade AI.

Go beyond simple imports by building a functional machine learning framework from scratch. Learn how TinyTorch demystifies tensors, autograd, and transformer architectures.

Learn how to achieve 99%+ training efficiency and second-scale GPU recovery on Amazon EKS by integrating NVIDIA Resiliency Extension (NVRx) with PyTorch FSDP, asynchronous checkpointing, and in-process restart logic.

An in-depth technical analysis of PyTorch Conference North America 2026, covering TorchDynamo graph capture, TorchInductor codegen, cross-hardware Triton DSL kernels, FSDP2, torchao quantization, and high-performance LLM serving.

An in-depth technical analysis of FlashAttention-4 utilizing OCP MXFP8 microscaling block formats on NVIDIA Blackwell GPUs, achieving up to 2.85 PF/s forward and 2.0 PF/s backward performance.

An analysis of the strategic implications of Cambricon's Platinum membership in the PyTorch Foundation and what it means for heterogeneous hardware support in AI development.

A deep technical review of Core PyTorch sessions at PyTorch Conference North America 2026, covering compiler architecture, distributed communication, device portability, and low-latency LLM serving.

An in-depth technical analysis of DeepSeek's Multi-Head Latent Attention (MLA) architecture. Learn how low-rank latent compression, matrix absorption, and decoupled RoPE eliminate KV cache bottlenecks and reduce memory footprint by up to 93%.

An in-depth technical analysis of vLLM sessions at PyTorch Conference North America 2026, covering disaggregated serving, KV cache optimization, PyTorch integration, MoE scaling, and production deployment.

An in-depth technical analysis of executing NVIDIA CUDA binaries and frameworks on AMD Radeon GPUs under Windows using translation layers like ZLuda, HIP SDK, and SCALE, alongside cloud API fallbacks.

A technical deep dive into PyTorch Conference China 2026 in Shanghai, exploring PyTorch 2.x compiler advancements, cloud-native distributed training, cross-hardware optimizations, and enterprise inference integration.

An in-depth technical dive into how Hugo Vergnes pre-trained a competitive 3.8B parameter language model on a strict $998 budget, achieving a 0.384 CORE benchmark score.