Hey Product Hunt! Lukas here, co-founder of BaseRT.
TLDR: a new performance ceiling for LLMs on Apple's M5 Pro.
The M5 introduces a new tensor core architecture, exposed through the Metal 4 tensor API. We tuned BaseRT to use all of it.
The results:
Up to 6.3× faster prompt processing (prefill) than llama.cpp
Up to 3.9× faster prefill than MLX
Tested on 10 configs across Qwen3/3.5/3.6, Llama 3.2, and Gemma 4, from 0.6B to 35B params
We built BaseRT because we think on-device inference is about to matter a lot, and Apple silicon is the best consumer hardware to run it on.
The software just hasn't been keeping up with the chips, and we're trying to close that gap. Happy to answer anything about the benchmarks, the Metal 4 tensor API, or where we're headed next. Would love your feedback, and if you've got an M5, try it and tell us what numbers you get.
About BaseRT on Product Hunt
“6.4x faster than llama.cpp, 3.9x faster than MLX”
BaseRT launched on Product Hunt on July 19th, 2026 and earned 238 upvotes and 45 comments, placing #4 on the daily leaderboard. BaseRT is the fastest LLM runtime on Apple Silicon. Install it with one command and run local models on your own device.
On the analytics side, BaseRT competes within Open Source, Artificial Intelligence and Apple — topics that collectively have 558.4k followers on Product Hunt. The dashboard above tracks how BaseRT performed against the three products that launched closest to it on the same day.
Who hunted BaseRT?
BaseRT was hunted by Lukas Wesemann. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of BaseRT including community comment highlights and product details, visit the product overview.
Lukas Wesemann