yoniq-org/compute

Sharing benchmarks from DGX stations

Local AI registry: one validated recipe per machine, with the evidence attached

Community recipes for serving LLMs on RTX 3090/4090/5090 CUDA gpus. Multi-engine (vLLM, sglang, llama.cpp and other custom engines) and model-agnostic. Currently shipping Qwen3.8-27B Qwen3.6 35B Gemma 4 26B Gemma 4 31B configs for 1×/2×/4x/8x cards.

~1300 line, minimal, extensible LLM inference engine built from scratch.

Curated collection of AI inference engineering resources — LLM serving, GPU kernels, quantization, distributed inference, and production deployment. Compiled from the AER Labs community.

A high-throughput and memory-efficient inference and serving engine for LLMs - Optimized for AMD gfx906 GPUs, e.g. Radeon VII / MI50 / MI60

A suite to benchmark CPU/GPU Python performance in training ML models and running local LLMs

One line of NInfer for the RTX 3090, RTX 4090 and RTX 5090, consolidated from the forks that carry it: GGUF block formats, ternary Bonsai 2, device route profiles, MTP and DFlash2.

100 days of LLM inference engineering — daily posts, experiments, and visualizations