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Thoughts on mlx-lm: top-priority is making it the central registry of MLX model implementations, with tools for evaluation and profiling, and we should add vision models too. Inference engines can have their own schedulers and custom kernels, and do whatever hack to make inference ultra fast, while importing mlx-lm as a library of models. We can rely on community to contribute model implementations, but there would be a fixed procedure to verify correctness of the implementation, ideally automatically. Everything else except for critical bugs, should be irrelevant at the moment and I'm closing PRs and issues aggressively. Many people will be mad at this, and certainly I would be making mistakes closing legitimate things, but for the project to survive, and for the community to grow healthy, I don't see another way.

Kimi K3 is now live in LM Studio Bionic! A massive, open frontier model: 2.8T MoE with 1M context window. Kimi K3 in Bionic is hosted on US-based servers, with ZDR enabled by default.

i added native support for @NVIDIAAI's Nemotron Puzzle 75B to mlx-lm. it now runs natively on an M2 Max 64GB: ⚡️22 tok/s 💾45.5 GB peak memory usage 📚4-bit experts + 6-bit dense + BF16 head i also fixed an annoying numerical bug in mlx-lm. outputs were subtly wrong, cosine similarity was 0.8832 vs NVIDIA's reference (identical inputs). the culprit was one dtype cast in the Mamba layers happening in a different spot than NVIDIA's. once moved, the cosine similarity improved to a satisfying level (0.999...). related PR: weights:

Got Qwen3.8 running locally and made a particle visualization for it. The model runs in LM Studio on my PC with a 20GB RX 7900 XT. I work from my Mac, and Pi Agent talks to it over the local network. Current setup: - unsloth/Qwen3.8-27B-UD-Q3_K_XL - 128K context length - 24 offloaded to GPU, automatically set by LM Studio - Flash Attention on - Q4_0 K/V cache on GPU - Batch size 256, one concurrent prediction - Default RoPE - Speculative decoding and MTP off

They do a bunch of ablations on short, long contexts and just general LM evals. QSA seems better on language modelling (again, flops matched i assume), basically the same on RULER. Efficiency wise, its obviously better and doesn't affect MTP acceptance

Locally is now @lmstudio's mobile app, and we are bringing LM Link to your iPhone. Use your largest models from your phone, over a secure, end-to-end encrypted connection. Available on iPhone and iPad.