Rapid-MLX – Run local LLMs on Mac, 2-3x faster than alternatives

Does the same job

VV
Vmctl/Vmcli – Easily Run Linux VMs on M1 Macs2021 · github.com · ▲198
BaseRTJul 2026 · basecompute.co · ▲222
6.4x faster than llama.cpp, 3.9x faster than MLX
MO
MicroMDM – Open Source MDM Server for Apple Devices2018 · micromdm.io · ▲146
PA
Phoenix – a macOS window and app manager scriptable with JavaScript2020 · github.com · ▲153
MA
Marta – A fast and minimalistic file manager for macOS2017 · marta.yanex.org · ▲106
TF
The fastest way to run Mixtral 8x7B on Apple Silicon Macs2024 · ▲18
I’d originally launched my app: Private LLM[1][2] on HN around 10 months ago, with a single RedPajama Chat 3B model. The app has come a long way since then. About a month ago, I added support for 4-bit OmniQuant quantized Mixtral 8x7B Instruct model, and it seems to outperform Q4 models at inference speed and Q8 models at text generation quality, while consuming only about 24GB of RAM[3] at 8k context length. The trick is: a) to use a better quantization algorithm and b) to use unquantized embeddings and the MoE gates (the overhead is quite small). Other notable features include many more…

TL
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BC
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AI · Apr 2026 · text.blogosphere.app