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Structured Weight Sparsity

Zeros accelerate inference only when a supported representation and kernel skip their work. Separate pruning quality, compressed storage, dispatch and end-to-end benefit.

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RELATED CONCEPTS

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LESSONS THAT TEACH THIS

Inference EngineeringCapstone: a reproducible serving teardown240 min→Inference EngineeringA quantized format needs a compatible and measured execution path20 min→

PRACTICE THIS IN REAL QUESTIONS

GPU & Accelerator ArchitectureHalf the weights are zero. Why did serving not get faster?→GPU & Accelerator ArchitectureIf you could change one thing about GPU architecture for LLMs, what would it be, and what argues against it?→LLM Inference & ServingFirst-token latency is fast, but the reasoning model's answer takes seconds. What do you check?→GPU & Accelerator ArchitectureWhy are GPUs so much faster than CPUs for deep learning? Be specific about what the silicon is doing differently.→Behavioral & OwnershipTell me about a performance win you are proud of. How did you measure it?→GPU & Accelerator ArchitectureWhat is a tensor core, and what does a kernel have to do to actually use one?→