AI Dev Skills
Inference & Serving
✗ Missing — critical gap
What is it?
Efficiently serving LLM predictions at scale — optimizing for throughput (tokens/second), latency (time to first token), and cost (dollars per million tokens).
Why it matters for AI PMs
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.
The 2026 landscape
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.
What strong coverage looks like
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.
Your library coverage (0 repos)
No repos in this skill area yet.
Key concepts to know
- •PagedAttention and KV cache management
- •Continuous batching
- •Quantization (GGUF, GGML, AWQ)
- •Speculative decoding
- •Token throughput vs latency tradeoffs