BaseRT claims to run local LLMs 6.4x faster than llama.cpp on Mac. Is it a paradigm shift for on-device AI or just another overhyped benchmark?
Hey fellow code monkeys! If you're tired of watching your hard-earned cash disappear into the bottomless pit of OpenAI or Anthropic API bills, raise your hand. We've all tried running local LLMs on our shiny M-series Macbooks, only to find our RAM thoroughly eaten, the fan sounding like a SpaceX Falcon 9 launch, and token generation slower than a government bureaucracy.
But hold on to your mechanical keyboards, because there’s a new kid on the block. BaseRT has officially landed on Product Hunt, claiming to be the fastest LLM runtime for Apple Silicon, leaving heavyweights like llama.cpp and Apple's own MLX in the dust. Let's dig into the metal and see if this is legit or just another vaporware hype-train.
It all started when Lukas, co-founder of BaseRT, dropped their product on Product Hunt with a bold promise: a brand new performance ceiling for local LLMs. By leveraging Apple's fresh Metal 4 Tensor API, they claim to have squeezed every ounce of power from the hardware.
Here are the eye-watering stats they threw at us:
Lukas stated they benchmarked this beast across 10 configurations using popular open weights like Qwen, Llama 3.2, and Gemma, ranging from tiny 0.6B helper models to beefy 35B parameter giants.
Usually, when a product claims a "6x speedup," the dev community shows up with pitchforks and skepticism. But this time, the early feedback is surprisingly cozy.
One dev running an M2 Macbook reported pulling and running a 7B model in under sixty seconds, stating it felt incredibly snappy even on battery power without roasting his thighs. It’s a huge win for those looking to bypass per-token cloud costs without sacrificing portability.
Of course, seasoned devs are already demanding power-user features:
As a cynical Senior Dev who has seen countless AI tools rise and fall, here’s my take: BaseRT is a genuinely cool project. On-device inference is indeed the future, and Apple Silicon is currently the best consumer hardware to run it on.
However, take the "6x speedup" with a grain of salt. Prefill speed (processing the prompt) is great, but actual generation speed (token-by-token output) under heavy context is where the real bottle-necks happen.
That said, the sheer simplicity of a one-command install makes BaseRT a no-brainer to try if you own a Mac. It’s driving healthy competition in the local AI space. But hey, if you don't own an M-series Mac or want to deploy things without melting your local machine, spin up a reliable vps and host your models there. It's still the safest bet for production workloads.
Source: Product Hunt
