Someone turned the hardest parts of LLM inference engineering into a course where you…
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Someone turned the hardest parts of LLM inference engineering into a course where you have to fix the performance problems yourself. And you can do it on a Mac. The project is called tiny-llm, an open-source course for building a miniature vLLM-style inference engine around Qwen3. It starts with a simple implementation using matrix operations. You build attention, normalization, positional encoding, the Transformer layers, and the loop that generates tokens. You have a working model. Now the interesting engineering begins. During decoding, repeatedly processing the previous tokens wastes
Posted by Tech with Mak (48.3k followers) 1 days ago · 1.5k likes · 56.1k views · view the original post on X. Kept by the Dev Radar as AI dev tools. Tools mentioned: tiny-llm.
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