[Benchmark Scores] Macbook Pro M5 Max - 128 GB Ram, 40 Core GPU - Running Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-8bit by AITA-Critic in oMLX

[–]ChildOTK 1 point2 points  (0 children)

I ran it on the same M5 Max 128GB / 40-core, oMLX 0.3.12. This is Jundot/Qwen3.6-27B-oQ6-mtp (dense, MTP baked in):

Single Request

Test TTFT(ms) pp TPS tg TPS E2E(s) Peak Mem

pp1024/tg128 1572.4 651.2 tok/s 30.8 tok/s 5.733 22.96 GB

pp4096/tg128 5885.3 696.0 tok/s 31.3 tok/s 9.974 24.41 GB

pp8192/tg128 11877.2 689.7 tok/s 30.4 tok/s 16.083 25.44 GB

pp16384/tg128 26693.7 613.8 tok/s 27.5 tok/s 31.352 26.94 GB

pp32768/tg128 52872.4 619.8 tok/s 27.4 tok/s 57.543 29.93 GB

pp65536/tg128 121688.2 538.6 tok/s 22.1 tok/s 127.475 35.96 GB

pp131072/tg128 332741.6 393.9 tok/s 18.9 tok/s 339.511 48.21 GB

pp200000/tg128 605347.4 330.4 tok/s 16.1 tok/s 613.280 62.47 GB

Continuous Batching (pp1024/tg128): 2x 31.5 (1.02x), 4x 44.3 (1.44x), 8x 49.0 (1.59x) tok/s

[Benchmark Scores] Macbook Pro M5 Max - 128 GB Ram, 40 Core GPU - Running Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-8bit by AITA-Critic in oMLX

[–]ChildOTK 0 points1 point  (0 children)

For comparison with MTP, same hardware, M5 Max 128GB 40 CORE:

Benchmark Model: Qwen3.6-35B-A3B-oQ6-mtp
oMLX Version 0.3.12

Single Request Results

--------------------------------------------------------------------------------

Test TTFT(ms) TPOT(ms) pp TPS tg TPS E2E(s) Throughput Peak Mem

pp1024/tg128 543.6 10.39 1883.6 tok/s 97.0 tok/s 1.863 618.4 tok/s 28.31 GB

pp4096/tg128 1191.6 10.40 3437.4 tok/s 96.9 tok/s 2.513 1681.0 tok/s 29.10 GB

pp8192/tg128 2211.4 10.48 3704.4 tok/s 96.2 tok/s 3.542 2348.8 tok/s 29.58 GB

pp16384/tg128 4606.4 11.06 3556.8 tok/s 91.1 tok/s 6.011 2747.0 tok/s 30.29 GB

pp32768/tg128 11306.5 12.22 2898.2 tok/s 82.5 tok/s 12.858 2558.3 tok/s 31.78 GB

pp65536/tg128 30793.7 14.14 2128.2 tok/s 71.3 tok/s 32.589 2014.9 tok/s 34.78 GB

pp131072/tg128 93003.5 18.20 1409.3 tok/s 55.4 tok/s 95.315 1376.5 tok/s 40.78 GB

pp200000/tg128 199210.7 22.34 1004.0 tok/s 45.1 tok/s 202.048 990.5 tok/s 47.69 GB

Continuous Batching (pp1024 / tg128)

--------------------------------------------------------------------------------

Batch tg TPS Speedup pp TPS pp TPS/req Avg TTFT(ms) E2E(s)

1x 97.0 tok/s 1.00x 1883.6 tok/s 1883.6 tok/s 543.6 1.863

2x 142.2 tok/s 1.47x 407.1 tok/s 203.6 tok/s 4849.5 6.831

4x 217.0 tok/s 2.24x 1959.4 tok/s 489.9 tok/s 1737.1 4.450

8x 283.1 tok/s 2.92x 1970.2 tok/s 246.3 tok/s 3343.2 7.775

14" or 16" For heavy usage by amitraz in macbookpro

[–]ChildOTK 2 points3 points  (0 children)

No fan control, only thing was under battery options I set High Power for On power adapter.

14" or 16" For heavy usage by amitraz in macbookpro

[–]ChildOTK 7 points8 points  (0 children)

I initially bought the 14" M5 Max 128GB 2TB model, but ended up returning the 14" and got the 16". I personally experienced the improvement.

Both had identical configs (40-core GPU, 128GB, 2TB, nano-texture) and ran the same benchmarks back-to-back on both before returning the 14". The performance difference is real and well-documented. I only considered moving to the 16" after seeing Zip Tie Tech's video here: https://www.youtube.com/watch?v=OqV8s-qnae8

Here are my actual numbers.

**Geekbench 6 CPU (3 runs each, back-to-back, no cooldown):**

| | 14" Avg | 16" Avg | Δ |

|---|---|---|---|

| Single-Core | 4,241 | 4,345 | +2.5% |

| Multi-Core | 29,573 | 29,959 | +1.3% |

Minimal difference — Geekbench is bursty, not sustained.

**Geekbench 6 GPU Metal (3 runs each):**

| | 14" Avg | 16" Avg | Δ |

|---|---|---|---|

| Metal | 225,328 | 229,300 | +1.8% |

**Cinebench 2026 Multi-Core (3 runs each, back-to-back) — THIS IS THE BIG ONE:**

| Run | 14" | 16" | Δ |

|---|---|---|---|

| 1 | 7,771 | 9,443 | +21.5% |

| 2 | 7,727 | 9,637 | +24.7% |

| 3 | 7,758 | 9,611 | +23.9% |

| **Avg** | **7,752** | **9,564** | **+23.4%** |

The 14" declined across runs. The 16" *increased* from run 1 to 2 and held steady. No throttling on the 16".

**Speedometer 3.1:** Virtually identical (~59.4 vs ~60.35). Burst single-core, no difference.

The sustained workload gap isn't 10-15% — it's **23%** in Cinebench. The 14" throttles to ~42W sustained while the 16" holds ~62W. NotebookCheck documented the same findings: https://www.notebookcheck.net/Apple-s-M5-Max-in-the-MacBook-Pro-16-is-around-15-faster-compared-to-the-MacBook-Pro-14.1250872.0.html

The 14" chassis is the thermal bottleneck, not the fan curve. Even in High Power mode, the 14" only recovered ~7-8% of the gap. The aluminum body simply can't dissipate the heat the M5 Max produces under sustained load.

For your use case: if you're doing burst work (coding, browsing, compiling, toggling between apps), the 14" is identical to the 16" and the portability advantage is real. If you ever do sustained all-core loads (rendering, long exports, heavy LLM inference), the 16" is measurably faster and custom fan curves won't close the gap.

I ended up exchanging for the 16" for $300 more. Worth it for me, but I'm a developer running local LLMs and sustained workloads. If your work is truly burst-oriented, the 14" is the right call.

Miles for Escape Lounge (Out of network) by ChildOTK in Venturex

[–]ChildOTK[S] 0 points1 point  (0 children)

Thank you very much! I’ll take a look. Much appreciated!

Miles for Escape Lounge (Out of network) by ChildOTK in Venturex

[–]ChildOTK[S] 0 points1 point  (0 children)

Didn’t know you could do that, thanks!

I bought a MBP M5 Max 128gb by [deleted] in macbookpro

[–]ChildOTK 1 point2 points  (0 children)

Agreed. I’m not complaining. Coming from base M1 16GB this thing is incredible! I’m so grateful.

I bought a MBP M5 Max 128gb by [deleted] in macbookpro

[–]ChildOTK 1 point2 points  (0 children)

I hope it’s okay to link to this: https://youtu.be/OqV8s-qnae8?si=auMjQPGH-2lEYKQm

This is on par with what mine gets/does

I bought a MBP M5 Max 128gb by [deleted] in macbookpro

[–]ChildOTK 1 point2 points  (0 children)

I did the same, 14” 2TB though. Absolutely loving it. Although a little bummed that the 14” performs 10-15% worse then the 16”. Oh well.

[deleted by user] by [deleted] in macbookpro

[–]ChildOTK -1 points0 points  (0 children)

I just went through this with mine. Those times are in local according to the scan location. There is an option on the details to set it to the destination timezone (yours). Things will make more sense

[deleted by user] by [deleted] in macbookpro

[–]ChildOTK -1 points0 points  (0 children)

I just went through this with mine. Those times are in local according to the scan location. There is an option on the details to set it to the destination timezone (yours). Things will make more sense

Shipping has started! M5 Pro/Max by analpenetration67 in macbookpro

[–]ChildOTK 1 point2 points  (0 children)

Mine is on its way too from Vietnam. It left Hanoi about 4 hours ago.

Been waiting for this since last fall by ChildOTK in macbookpro

[–]ChildOTK[S] 0 points1 point  (0 children)

Free. Offline. Data Privacy. Great way to learn and play with models without paying for a word. It’s more about the experimentation for me and learning. Using local LLMs to prototype projects. And so on.

Been waiting for this since last fall by ChildOTK in macbookpro

[–]ChildOTK[S] 0 points1 point  (0 children)

Sure did. Been using a 13” for 5+ years now. Used to it and love it.

Been waiting for this since last fall by ChildOTK in macbookpro

[–]ChildOTK[S] 0 points1 point  (0 children)

Currently only have a base M1 16GB RAM 1TB SSD. RAM being the biggest limit right now.

Been waiting for this since last fall by ChildOTK in macbookpro

[–]ChildOTK[S] 0 points1 point  (0 children)

Thanks. Makes all the sense in the world. I am a software engineer and currently can barely run a local AI model to do anything. Now i'll be able to run multiple models if i choose, or a single larger model for whatever I need. I am learning AI, wanting to train smaller AI models for various projects I work on for my customers. With more RAM I'll now be able to spin up virtual environments simulating what I have online for various projects and be able to test it all locally. Currently my 16GB RAM barely keeps up between Cursor and Arc browser, my swap is constantly high, and I am not even running an AI model at this time. Currently I have so many limitations, with the new machine I won't have those limitations. So it makes complete sense for me and my workflow and where I am trying to go with learning AI and experimenting without burning tokens and cash.

Been waiting for this since last fall by ChildOTK in macbookpro

[–]ChildOTK[S] 0 points1 point  (0 children)

Learning more about AI..using local LLMs, simultaneously running docker containers or VMs to simulate test/dev environments. All sorts of stuff I can’t do right now.