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GPU Kernel Engineering for LLM Inference: CUDA, Triton, and Flash Attention Optimization for High-Throughput AI Production Systems (AI Infrastructure, Hardware & Compiler Engineering Series) - Softcover
Book 6 of 6: AI Infrastructure, Hardware & Compiler Engineering Series
Synopsis
Master GPU Kernel Engineering for Production LLM Inference
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.
What You Will Master:- 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.
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GPU Kernel Engineering for LLM Inference: CUDA, Triton, and Flash Attention Optimization for High-Throughput AI Production Systems (AI Infrastructure, Hardware & Compiler Engineering Series)
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GPU Kernel Engineering for LLM Inference
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GPU Kernel Engineering for LLM Inference
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GPU Kernel Engineering for LLM Inference (Paperback)
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Paperback. Condition: new. Paperback. Master GPU Kernel Engineering for Production LLM InferenceIn 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.What You Will Master: 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798185800379
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