Blogpost: A Mental Model for GPU Engineering for LLMs (modelcraft.substack.com)

A new blogpost lays out a practical “mental model” for GPU engineering for LLMs, arguing that system design — not immediate CUDA kernel hacking — is the most valuable skill for most engineers. The author breaks the stack into five layers: Model Definition, Parallelization, Runtime Orchestration, Compilation & Optimization, and Hardware. Through examples like FlashAttention (memory‑bound fixes), DeepSpeed ZeRO (sharded state vs. comms overhead), and real-world straggler issues at scale, the post shows how common bottlenecks shift from compute to memory, communication, scheduling, compiler fusion, and ultimately physical interconnect limits (NVLink/PCIe/InfiniBand). Key tools and techniques called out include profiling with framework tools, fused kernels, all‑reduce behavior, TorchInductor/TensorRT for inference fusion and quantization, Triton Server batching, and cluster orchestrators like Ray/Kubernetes. The main takeaway for the AI/ML community is prescriptive: start at the top of the stack and only descend when profiling proves it necessary. Layers 1–3 demand system design skills (synchronization, sharding, scheduling) to avoid wasted GPU cycles; Layer 4 is where compilers, fusion, and quantization meaningfully cut latency; hand‑written kernels are an edge‑case optimization for hot paths that survive all prior fixes. At the bedrock, hardware constraints are often architectural limits you must design around. The post reframes many disparate papers and posts into a unified troubleshooting map, guiding engineers to focus effort where it yields the biggest returns.