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Sunday 15 November 2026 · 26 stories
AI technology
26

CUDA adoption: from early GPU experiments to the AlexNet breakthrough

AlexNet: the CUDA engineering behind the 2012 breakthrough

Inside cuda-convnet: AlexNet dual-GPU layers and the ImageNet era

Tensor Cores, Triton, residual networks and vision transformers

Self-attention, CUDA kernels and FlashAttention

FlashAttention scaling, multi-GPU training and weight quantization

PagedAttention, speculative decoding, MoE and Blackwell

CUDA Graphs, reasoning models and disaggregated serving

Rubin, custom AI chips and compressed KV caches

LLM inference, fused kernels and GPU networking

GPU benchmarks, 70B memory sizing and AI compilers

FSDP2, GPU profiling and mobile compute

Practical GPU engineering: Metal shaders, profiling and edge deployment

Choosing AI optimization strategies for your hardware

Modern CUDA C++ and Triton: control, productivity and workload choices

GEMM in CUDA C++ and Triton, torch.compile and ROCm portability

Generated Triton code, GEMM examples and ROCm warp-size pitfalls

SwiGLU fusion, FlexAttention and a GPU optimization cheat-sheet

Google expands multi-agent research workflows

Hospital-local AI highlights evidence-linked clinical support

Anthropic extends free Claude access for US teachers

OpenShell contribution puts agent governance in focus

SpaceX successfully catches Starship rocket returning to Earth

Musk warns Nvidia could lose critical supply-chain deals without Huang
