Run it yourself

r/LocalLLaMA moderator and founder of Osmantic, which builds the open-source ODS self-hosted AI stack. Long-running advocate for running open models on hardware you own; ran two workshops at the AI Engineer World's Fair 2026 and showed frontier open models on a DGX Station on stage. Handle confirmed from his GitHub profile and his own link page.

Creator of llama.cpp and the ggml library; maintains the community benchmark threads for Apple Silicon, CUDA and DGX Spark. GitHub profile lists Hugging Face as his employer.

The C/C++ inference engine most local tools build on (Ollama, LM Studio, many GUIs). About 130,000 stars.

Co-created MLX, Apple's array framework for Apple silicon, and mlx-lm; first to publish 1-trillion-parameter models running on two Mac Studios. GitHub bio now says he does research at Anthropic.

Apple's open-source framework and LLM runner for Apple silicon; the engine behind most MLX quants on Hugging Face.

Unsloth: dynamic GGUF quantisations that often appear on release day, plus run-it-locally guides for nearly every major open model.

Maintains the Strix Halo toolboxes and the local-llm-benchmarks site with reproducible speed measurements at different context depths.

Co-founder and CEO of EXO Labs; exo clusters Macs and other devices (including RDMA over Thunderbolt 5) to run models too large for one machine.

Open-source distributed inference project, about 47,000 stars, Apache 2.0.

Creator of oMLX, an Apple-silicon inference server with continuous batching and SSD caching, and a community benchmark database of real Mac results.

One of the most-used community GGUF quantisation publishers, with over 2,400 model repos.

Very large-scale GGUF and imatrix quantisation of community models (over 70,000 repos).

Ready-to-run GGUF and MLX conversions of new releases, usually within days of launch.

Author of ik_llama.cpp, a llama.cpp fork with state-of-the-art quantisation types and faster CPU and hybrid CPU+GPU inference for large MoE models.

Core llama.cpp CUDA and multi-GPU contributor.

vLLM, the standard high-throughput serving engine; its recipes site lists tested configurations for new models on 5090s, GB300s and more.

SGLang serving framework; the LMSYS team also published the DGX Spark review and the KTransformers hybrid-inference work.

CPU+GPU hybrid inference kernels for running huge MoE models with modest GPU memory; integrated into SGLang.

The easiest way to run open models locally for most people; about 180,000 GitHub stars.

Desktop app for local models with GGUF and MLX engines.

Widely read writer who documents hands-on results with local and open models, and relayed early MLX multi-Mac results.