oMLX — LLM inference, optimized for your Mac

jundot/omlx

LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar

omlx/docs/experimental/dflash_mlx_integration.md at main · jundot/omlx

LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar - jundot/omlx

omlx/docs/oQ_Quantization.md at main · jundot/omlx

LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar - jundot/omlx

Exploring LLMs with MLX and the Neural Accelerators in the M5 GPU

Mac with Apple silicon is increasingly popular among AI developers and researchers interested in using their Mac to experiment with the…

Combining NVIDIA DGX Spark + Apple Mac Studio for 4x Faster LLM Inference with EXO 1.0

Disaggregating Prefill and Decode: Faster First Tokens, Faster Streams

Combining NVIDIA DGX Spark + Apple Mac Studio for 4x Faster LLM Inference with EXO 1.0

Disaggregating Prefill and Decode: Faster First Tokens, Faster Streams

apple-silicon-llm-bench/results/complete_results.html at main · AlexHiesch/apple-silicon-llm-bench

Systematic LLM inference benchmark for Apple Silicon: 8 backends, 7 models, 791 measurements - AlexHiesch/apple-silicon-llm-bench

ModelScope on Twitter / X

🤯 400 Token/S on a MacBook? Yes, you read that right!Shaohong Chen just fine-tuned the Qwen3-0.6B LLM in under 2 minutes using Apple's MLX framework. This is how you turn your MacBook into a serious LLM development rig. A step-by-step guide and performance metrics inside! 🧵… pic.twitter.com/31Cmycy8Mh— ModelScope (@ModelScope2022) October 13, 2025

ARahim3/mlx-tune

Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.

ARahim3/mlx-tune

Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.

Benchmarks | EXO

Transparent benchmarks for LLMs tested on real hardware. Coming soon.

predibase/lorax

Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs

vllm-project/vllm

A high-throughput and memory-efficient inference and serving engine for LLMs

Rohan Paul on Twitter / X

👨‍🔧 Github: Native, Apple Silicon–only local LLM server. Similar to Ollama, but built on Apple's MLX- OpenAI API compatible, - Ollama‑compatible- OpenAI‑style tools + tool_choice, with tool_calls parsing and streaming deltasgithub. com/dinoki-ai/osaurus pic.twitter.com/G0NbWnEJcQ— Rohan Paul (@rohanpaul_ai) September 3, 2025

Run LLMs locally on your Mac · mlx-optiq

Quantize, fine-tune and serve LLMs locally on Apple Silicon. MLX-native, no PyTorch, no cloud. On PyPI.

videlalvaro/ane-book

Production LLM inference on the Apple Neural Engine — a practitioner's guide, complete with converters, Swift runtimes, and validated model manifests