In the high-stakes world of AI production, the bottleneck isn't model architecture-it's execution efficiency. GPU Kernel Engineering for LLM Inference is the definitive engineering guide designed to close the gap between framework-level PyTorch code and hardware-optimal hardware performance.

Written specifically for machine learning engineers, systems software developers, and performance engineers, this practical manual provides deep, production-ready blueprints for building high-throughput AI serving infrastructure. You will move past theoretical concepts and dive straight into raw hardware-level optimizations that top AI teams use to slash latency and reduce infrastructure costs.

• Custom CUDA Kernels: Write and optimize high-performance CUDA C++ kernels tailored specifically for modern Transformer workloads.
• Flash Attention 2 & 3: Implement advanced IO-aware attention algorithms with Hopper-specific asynchronous memory and Tensor Core operations.
• Triton Development: Build optimized fusion kernels for layer normalization, activation functions, and positional encodings using Python-level Triton.
• Quantization & GEMM: Develop high-throughput INT8, INT4, and weight-only quantized GEMM kernels for optimized memory footprints.
• PagedAttention & KV-Cache: Design vLLM-style virtual memory management kernels to eliminate memory fragmentation.
• Multi-GPU Scaling: Coordinate tensor-parallel all-reduce operations with custom NCCL collectives to scale seamlessly across cluster nodes.
• Nsight Profiling: Locate hardware bottlenecks using Nsight Systems and Nsight Compute.

Stop relying on out-of-the-box configurations. Equip yourself with the systems engineering skills required to build the next generation of high-speed, cost-efficient AI infrastructure. Learn to build kernels that run at the absolute physical limits of NVIDIA silicon.