SyGra: The One-Stop Framework for Building Data for LLMs and SLMs

How To Build Your Own LLM Runtime From Scratch

If you have ever wanted to actually build an LLM inference runtime yourself — pack your own weights, own every barrier, capture your own CUDA graphs — this is what that journey looks like on an H100. A step-by-step tour of a small runtime called annotated-llm-runtime, and the three bugs that produced most of the annotations.

Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures

arXiv:2505. 24069v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making.

Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB

The paper introduces FlockMTL, an extension for database management systems that deeply integrates large language models and retrieval‑augmented generation into DuckDB. It provides model‑driven scalar and aggregate functions, cost‑based optimizations like batching and caching, and new SQL DDL abstractions (PROMPT and MODEL) to treat LLMs as first‑class schema objects. These features aim to simplify the development of knowledge‑intensive analytical applications by reducing the effort required to orchestrate heterogeneous data systems and manage LLM context.

Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

LLM4RTL: Tool-Assisted LLM for RTL Generation

arXiv:2606. 15500v1 Announce Type: cross Abstract: Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems.

StarCoder: A State-of-the-Art LLM for Code