Atlas · GenAI 2026
Inference Optimization
Throughput optimization (PagedAttention, continuous batching, KV cache)
conceptPeak: 2024Inference OptimizationAI consensus: 2/3
Prerequisites
PagedAttention and continuous batching are optimizations implemented IN inference engines like vLLM
KV cache optimization requires understanding how key-value pairs are computed and reused in self-attention
Recommended reference
Kwon et al. (2023) 'Efficient Memory Management for LLM Serving with PagedAttention' — SOSP; the paper behind vLLM's core innovation
Notes from AI deep research
Anthropic Opus
PagedAttention, continuous batching, KV cache. Kwon (2023) SOSP paper. Oszczednosci w serving > w modelu
OpenAI Deep Research
Największe oszczędności z serving [OA#55]
Google Deep Think
Zarządzanie blokami pamięci GPU [G#73]
Related skills
- → is part of: LLM Inference Serving(3/3)
- ← is part of: Prompt Caching(3/3)
- ← is part of: Speculative Decoding(3/3)
- ← is an instance of: ONNX(0/3)
- ← is an instance of: OpenVINO(0/3)
- ← is an instance of: TensorRT(0/3)
- ← is an instance of: FlashAttention(0/3)