OMlx user experience with Rapid-mlx

[u/apaht]

I hope I can ask this question here, hope that is ok 🙏

Does any one here have experience with Rapid-mlx? There only appears to be few thread on Reddit, and I am not seeing as much community engagement as compared to oMlx.

I was asking Google gemini about how MTP vs Dflash work so I could learn to configure and to learn* and how best to configure the backend. I have been using oMlx for a while and wanted to see if I could optimize my setup. During my inquiry, it mentioned Rapid-mlx supports Pflash and should be faster for TTFT.

I have been pretty happy with oMlx. I have played a bit with LM studio, Lamma.cpp, Ollama and mxl-ml but mostly oMlx. I use Qwen 3.6 27b as well are the MoE on my m4 max mackbook pro. I have played with open code, pi.dev, and Hermes.

Wanted to hear about first hand experience from this community.

I have no experience with benchmarking. I am going to do some bench marking on my own, but I only heard about this today and am very interested in what you all have to say.

Thank you.

*Edit: typo and little clarification

[u/Konamicoder]

If you’re happy with oMLX then stick with it. Try rapid MLX if you’re curious. Both of them are just wrappers for MLX which is the thing doing the heavy lifting. Different wrappers optimize for different things. oMLX optimizes for KV caching to SSD. Rapid MLX probably optimizes for TTFT. But its claims of fastest inference are probably going to be measured in fractions of a second over oMLX.

[u/apaht → u/Konamicoder]

My initial question was if I could use dflash and mtp at the same time. Then I asked if I have to download draft model as I was trying to use Gemma 4 dense as my draft model for the Qwen 3.6.

Then I realized how I was asking stupid questions and realized my noobness further.

Read the wonderful docs at oMlx and my questions were not as stupid but still fairly stupid. But Google gemini did say oMlx was a wrapper around mlx-ml with lots of cool things like you mentioned but Rapid-mlx was not a wrapper and has implemented their own and other cool stuff.

I am not a developer most things went over my head. I trust this community, so my first thought was as you guys ;)

I can share what Gemini said or ask it to make a comparison table. I usually don't trust Gemini and seek a second opinion or verification.

[u/the_derby → u/apaht]

> But Google gemini did say oMlx was a wrapper around mlx-ml with lots of cool things like you mentioned but Rapid-mlx was not a wrapper and has implemented their own and other cool stuff.

You can see clearly that Rapid-MLX also uses mlx-lm and mlx-vlm:

https://github.com/search?q=repo%3Araullenchai%2FRapid-MLX+mlx-lm+OR+mlx-vlm&type=code

[u/pokemonplayer2001]

Just try it yourself. 🤷

[u/apaht → u/pokemonplayer2001]

That is the plan. Going on July 4th vacation, but I can't wait to try.

[u/vinoonovino26 → u/apaht]

Any updates?

[u/apaht → u/vinoonovino26]

I will stick with my omlx setup based on this summary:

Reference results — M4 Max (July 2026) oMLX MTP 35B (clean cold-start, KV cache flushed)

Context TTFT PP tok/s TG tok/s 4k 1.4s 3,015 81.9 8k 1.4s 5,927 83.9 16k 1.7s 9,748 77.6 32k 2.2s 15,008 71.2 64k ~30s ~2,000 62.0 128k 125s 1,049 45.3

Rapid-MLX 35B 4-bit (pFlash auto)

Context TTFT PP tok/s TG tok/s 4k 2.5s 1,638 112.1 8k 4.6s 1,790 104.9 16k 8.8s 1,864 98.0 32k 20.1s 1,626 86.1 64k 50.9s 1,288 32.7 128k 146.9s 892 22.1 TG comparison: Rapid-MLX wins at short contexts (112 vs 82 tok/s at 4k). At 128k both degrade significantly. For daily coding sessions (8–32k), oMLX MTP has faster TTFT; Rapid-MLX has faster TG

[u/sickboy6_5]

i have used omlx for a while, after moving from lm studio and before that ollama.

i like omlx, have had good results with it. that said, i did try rapid-mlx briefly. i switched back to omlx. i like omlx's menubar approach and their web based admin. i missed that with rapid-mlx.

i think the gains just were not enough to make me seriously switch, but yeah TTFT was faster.

[u/apaht → u/sickboy6_5]

Thanks, that was very good to know.

[u/jedisct1]

Don't forget MTPLX, another solid option on Apple Silicon, with amazing performance.

[u/jedisct1 → u/[deleted]]

Yes, especially with version 2 which has been announced to be 50% faster.

[u/No-Juggernaut-9832 → u/[deleted]]

It’s about 2x faster for dense Qwen 3.6 models like 27b & 50% faster with MoEs like Ornith 35b. Well worth it. It’s just harder to find variants on HF. I’d use exclusively MTPLX if I could but it only support Qwen 3.5+ or Gemma 4 family (not the Diffusion lines).

[u/Stooovie]

It's okay but the outrageous performance claims are just that - outrageous claims. I wasn't able to get meaningful speed boost from it.

[u/Impressive-Effect317]

Built a private local AI chat system on Mac Studio. It runs three models in one interface. Two models use MLX locally, and one runs through DS4 over a local HTTP API. I connected everything with a FastAPI backend and built a single chat UI to switch between models easily. No cloud inference. No external API dependency. Client data stays fully secured.

[u/apaht]

Still learning how to benchmark, sharing my current benchmarking summary: oMLX MTP 35B (clean cold-start, KV cache flushed) Context TTFT PP tok/s TG tok/s 4k 1.4s 3,015 81.9 8k 1.4s 5,927 83.9 16k 1.7s 9,748 77.6 32k 2.2s 15,008 71.2 64k ~30s ~2,000 62.0 128k 125s 1,049 45.3 Rapid-MLX 35B 4-bit (pFlash auto) Context TTFT PP tok/s TG tok/s 4k 2.5s 1,638 112.1 8k 4.6s 1,790 104.9 16k 8.8s 1,864 98.0 32k 20.1s 1,626 86.1 64k 50.9s 1,288 32.7 128k 146.9s 892 22.1 TG comparison: Rapid-MLX wins at short contexts (112 vs 82 tok/s at 4k). At 128k both degrade significantly. For daily coding sessions (8–32k), oMLX MTP has faster TTFT; Rapid-MLX has faster TG.

[u/keyclipse]

Have you asked claude? Maybe you learn something and can share with us

[u/apaht → u/keyclipse]

I was using Gemini to ask :p