What models and quantizations have you been trying? I've had great success with the larger Qwen 3.x models at 6-bit levels. Using 6 bit quantization is really the bare minimum to give local models a fair shot at agentic flows. Once you start pushing below that the models become more "dumb" from the limited bit space.
Have been using Qwen 3.6 27b recently along with various other models the last month and it is very capable for writing code at a level I haven't need to use a subscription for 95% of what I throw at it. Been using it to write extensions for Pi to expand tool kit without much fuss as one example. Is it as fast or SOTA? No, but you can't ignore how functional it is on hardware you own. Where it can begin to struggle is giving too open ended prompts or investigating complex technical issues. At that level its knowledge is not high enough to solve those problems on its own.
Some broad assumptions are being made that plans give you a precise equivalent to API cost. This is not the case with reverse engineering plan usage showing cached input is free [0]. If you re-run the math removing cached input the usage cost is ~5-34% more. Was the token plan budget increase [1] proportional to account for this? Can’t say with certainty. Those paying API costs though the price hike is real.
It is possible but requires a very specific model design to utilize. As this reverse engineering effort has shown [0] "The ANE is not a GPU. It’s not a CPU. It’s a graph execution engine." To build one requires using a specific pipeline specifically for CoreML [1].
Unsloth quantizations are available on release as well. [0] The IQ4_XS is a massive 361 GB with the 754B parameters. This is definitely a model your average local LLM enthusiast is not going to be able to run even with high end hardware.
With that you are taking a significant performance penalty and become severely I/O bottlenecked. I've been able to stream Qwen3.5-397B-A17B from my M5 Max (12 GB/s SSD Read) using the Flash MoE technique at the brisk pace of 10 tokens per second. As tokens are generated different experts need to be consulted resulting in a lot of I/O churn. So while feasible it's only great for batch jobs not interactive usage.
The latest release v0.3.2 has partial support, generation is supported but not all special tokens are handled. I've done some personal testing to add tool calling and <|channel> thinking support. https://github.com/Yukon/omlx
The model does have the format specified but there is no _one_ standard. For this model it’s defined in the [
tokenizer_config.json [0]. As for llama.cpp they seem to be using a more type safe approach to reading the arguments.
Good to see Ollama is catching up with the times for inference on Mac. MLX powered inference makes a big difference, especially on M5 as their graphs point out.
What really has been a game changer for my workflow is using https://omlx.ai/ that has SSD KV cold caching. No longer have to worry about a session falling out of memory and needing to prefill again. Combine that with the M5 Max prefill speed means more time is spend on generation than waiting for 50k+ content window to process.
That’s exactly what I thought about. Getting my hands on an M5 Max this week and going to see hows Dan’s experiment performs with faster I/O. Also going to experiment with running active parameters at Q6 or Q8 since output is I/O bottlenecked there should room for higher accuracy compute.
Related, Intel was showing off Thunderbolt Share at CES[1]. Allows Thunderbolt 4/5 device-to-device transfer of files. Theoretical speeds in the 20Gbps and 40Gbps for Thunderbolt four and five respective.
One idea for why they were only able to reach 11Gbps is having only one Thunderbolt/USB4 controller[2], meaning the two USB4 ports split the 40Gbps PCIe lane. Throw in a full-duplex connection and you get 10Gbps in one direction.
Edit: Keyboard input does not work :/ it was a good effort.
Found a workaround throw it in an iFrame and have the frame load with a user interaction. Here is a jsFiddle link, just click "Run" after the page loads.
Firefox prevents audio from playing with no initial user interaction like a play button. Can see the warning if you pop open the dev console. Don't know of workaround besides sites not creating an AudioContext on page load.
Little late but though I would say hi. I too got started programming thanks to Minecraft. My first real job was working at Overcast Network (oc.tc). I remember having to scale out our infrastructure to seven dedicated servers after a popular YouTuber featured us. At the time that felt crazy for a Minecraft server and here you are now with hundreds of servers. Huge congrats on scaling to where you are today.
Have lots of fond memories of those early years, especially Minecon 2013.