Acing AI — AI education, tutorials, research and datasets for data scientists
PagedAttention Is Gone — What vLLM's Model Runner V2 Replaced It With
vLLM v0.25.0 removed the legacy PagedAttention code and made Model Runner V2 the default for dense models. What changed, and whether it moves your numbers.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
RLVR in Practice: Build a Verifiable-Reward Loop with GRPO
Hands-on RLVR tutorial: build a verifiable-reward loop with GRPO in TRL, write a math verifier, and learn the DAPO and GSPO fixes that keep it stable.
Linear Attention at Frontier Scale: Kimi K3's KDA Claim, Fact-Checked
Kimi K3 runs linear attention in 3 of 4 layers and claims it beats full attention. What the 48B evidence shows, and what stays unverified at 2.8T scale.
ArticleAgent Memory Beyond RAG: Why Your Agent Needs a Write Path, Not a Retriever
ArticleAgent Evaluation for Tool Use: Why pass@1 Lies, and How to Measure Reliability
ArticleKV-Cache Engineering: The Memory Wall of LLM Serving
Browse by Type
Tutorials
Step-by-step guides from neural network basics to advanced LLM fine-tuning.
Research Papers
Peer-reviewed insights and white papers defining the frontier of artificial intelligence.
Datasets
High-fidelity training sets for natural language processing and computer vision.
Start Learning
Guided sequences through our best content — structured to build understanding from the ground up.
LLM Inference in Production
A practical route through the serving stack. Start with the map of where inference cost and latency actually come from, take the quantization lever apart, speed up decoding with speculative drafts and measure it yourself, confront what long context really delivers, and finish at the 2026 state of the art. Every step names its tradeoffs.
Understanding Modern LLM Architectures
A guided journey from transformer fundamentals to the cutting edge of LLM engineering. You will build an intuition for how modern language models are designed, scaled, optimized, and deployed, by covering attention mechanisms, Mixture of Experts, architectural innovations like DeepSeek's MLA, reasoning capabilities, inference optimization, and the open-source ecosystem reshaping AI.