MLX vs GGUF Mac Comparison
The discussion began on 18 May, when a LocalLLaMA post compared GGUF with multi-token prediction, or MTP, against MLX without MTP. It asked whether MLX remained the preferred route for Mac users. The unequal comparison makes the question useful and the implied conclusion less tidy.
By 13 September, another LocalLLaMA thread had widened the question to MLX versus GGUF on Mac. Related discussions have examined the benefits of llama.cpp and ik_llama over LM Studio, unexpectedly lower tokens-per-second results from smaller GGUF files, converting Qwen3.8-27B-pi from GGUF to MLX, and whether llama.cpp should support formats beyond GGUF. Other LocalLLaMA posts have covered MTP mmproj with uncensored GGUF, official sparse autoencoders for Qwen 3.5 models through Qwen-Scope, and using Ollama with GGUF in place. Dev.to has addressed which local LLM format Apple Silicon users should choose. The cited material shows a recurring practitioner debate shaped by model support, conversion needs, runtimes and observed performance. It offers no settled answer.
Assuming the reporting is factual, impact is 10%. Mac users running local models face a choice of format and tooling that can affect access to MTP, model conversion, runtime compatibility and tokens per second. The immediate effect falls on users of MLX, GGUF, llama.cpp, ik_llama, LM Studio and Ollama, especially those trying to move Qwen3.8-27B-pi between formats.
Watch for controlled Apple Silicon benchmarks comparing the same model, quantisation and hardware across MLX and GGUF, including MTP support and tokens per second. Broader llama.cpp format support or simpler in-place GGUF use in Ollama could also change the trade-off.
<!-- SC_OFF --><div class="md"><p>I’ve been testing LM Studio on my RTX 5070 Ti (16GB) and Ryzen 9800X3D, running Unsloth Qwen3.5 35B (UD Q4_K_XL).</p> <p>Initially, I thought LM
<p>If you run local models on a Mac, you eventually hit the same fork in the road: the same model is available as a GGUF file and as an MLX version, and something has to tell you
<!-- SC_OFF --><div class="md"><p>Hey y'all!</p> <p>I've recently written a text in Russian about my experience comparing Qwen-3.6-27B with lower tier cloud models on hard tasks
<!-- SC_OFF --><div class="md"><p>Noob here, Running Qwen3.6 35B A3B in LM Studio on a 3080 10GB + Ryzen 5 3600 on Windows 10.</p> <p>Tried some unsloth quants with identical
<!-- SC_OFF --><div class="md"><p>Will someone convert it to Mac format (MLX)? I have tried and have encountered an error</p> <p>"gguf2mlx --input Qwen3.8-27B-pi-Q6_K.gguf
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1uwkrq9/should_llamacpp_expand_format_support_beyond_gguf/"> <img alt="Should llama.cpp Expand Format
<!-- SC_OFF --><div class="md"><p>I'm quite confused how to migrate my models library to be mtp enabled.</p> <ul> <li><p>Can i use the non-mtp mmproj file as mmproj when using mtp
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vau4dh/pr_for_running_ternarybonsai8bq2_0gguf_in/"> <img alt="PR for running Ternary-Bonsai-8B-Q2_0.gguf in
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