LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar
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
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
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
Benchmarks | EXO
Transparent benchmarks for LLMs tested on real hardware. Coming soon.
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
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Alex Cheema on Twitter / X
.@karpathy shouted out my work on @exolabs at @ycombinator AI SUS!“we use LLMs similarly to mainframes in the ‘70s - compute is timeshared by having a slice in the batch dimension. models will compress over time, and with this we’ll be able to run more on-device” pic.twitter.com/UKSrquQGAL— Alex Cheema (@alexocheema) June 18, 2025
mzau/broke-cluster
A Poor Man's Apple Silicon LLM Cluster — tuned for MLX, scalable without shame.
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.
Doriandarko/MLX-GRPO
A pure MLX-based training pipeline for fine-tuning LLMs using GRPO on Apple Silicon.
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
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…
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
