What building an LLM inference engine from scratch taught me about compiler design

Short summary

Author built ignis, a minimal-dependency LLM inference engine in Rust, showing that inference optimization mirrors compiler design fundamentals. Using SSA IR, fusion passes, and liveness analysis, they reduced operations by 17% and activation buffers by 76% (363→5), achieving 52 tok/s on M3. The project proves that framing inference as a compilation problem, not just linear algebra, unlocks unintuitive performance gains.

• •Built ignis: minimal-dependency inference engine applying SSA IR and compiler optimization techniques to LLM forward passes
• •Achieved 76% activation buffer reduction through register-allocation-inspired liveness analysis (363→5 buffers)
• •Demonstrates inference optimization as a compiler problem; transposes compiler design directly to tensor computation graphs

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