AI Dev Skills
Efficiently serving LLM predictions at scale — optimizing for throughput (tokens/second), latency (time to first token), and cost (dollars per million tokens).
Inference cost is typically 60-80% of AI product cost. PagedAttention in vLLM reduced serving costs by 10x for many teams. This directly impacts your product economics.
vLLM dominates production serving. llama.cpp and Ollama power local inference. SGLang is emerging for structured generation workloads. The gap between open and closed inference is closing fast.
4+ inference repos signals deep investment in serving efficiency. These teams are squeezing maximum performance from their hardware and have explored the full inference stack.
No repos in this skill area yet.