a community oriented 1:1, vLLM-alike (Continuous batching, paged KV) engine in C++ with additional features (GGUF, RadixAttention, Cache-aware scheduling, ...)
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Updated
Oct 28, 2026 - C++
a community oriented 1:1, vLLM-alike (Continuous batching, paged KV) engine in C++ with additional features (GGUF, RadixAttention, Cache-aware scheduling, ...)
A deterministic PyTorch autograd verification trap for catching silent KV-cache routing and block-alignment failures in vLLM and SGLang serving infrastructure.
This is a state‑of‑the‑art infrastructure that solves one of the most critical scalability bottlenecks in production LLM serving: distributed KV‑cache fragmentation. By extending SGLang's RadixAttention concept to a cluster‑aware, shared‑nothing topology, it transforms a set of independent vLLM workers into a cache‑conscious serving fabric.
Sovereign structured LLM generation engine. SGLang RadixAttention + Pydantic schema enforcement + Ollama fallback. Guaranteed JSON output. No hallucinated keys.
SGLang vs vLLM playground: FastAPI app benchmarks TTFT/throughput against your own servers, plus 2 notebooks
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