--- license: apache-2.0 pretty_name: Smarty Pants Mac Benchmarks tags: - mlx - apple-silicon - benchmark - quantization configs: - config_name: results data_files: results.json --- # Smarty Pants Mac Benchmarks Speed and quality for open models on real Apple Silicon Macs, made with our open [model factory](https://github.com/Howcanio/smarty-pants/tree/main/scripts/model-factory). Each section is one model on one Mac. The **bold** row is the base version; the rows marked ↳ are versions we built or tested to make it faster, smaller or smarter, measured the same way on the same Mac. A version counts as **better** only if it is faster or smaller *and* its answers stay as good, or if its answers get better at the same speed. Everything else is listed anyway, so you can see what did not work. ## Summary | Model | Mac | Base speed | Best version | Its speed | Its quality | |---|---|---|---|---|---| | Qwen3.5-4B (Smarty) | Apple M5 Pro (Mac mini) | 99.7 tok/s, quality 86.5% | base is still best | | | | Qwen3.5-4B (Smarty), GSM8K math | Apple M5 Pro, 64 GB (Mac mini) | 99.7 tok/s, quality 89.0% | base is still best | | | | Qwen3.5-4B (Smarty), draft-ahead decoding | Apple M5 Pro, 64 GB (Mac mini) | 95.9 tok/s, quality 89.0% | base is still best | | | | Qwen 3.8 27B | Apple M5 Pro (Mac mini) | 35.7 tok/s, quality 90.9% | base is still best | | | | Qwen 3.8 27B, draft-ahead decoding | Apple M5 Pro, 64 GB (Mac mini) | 17.8 tok/s, quality 91.5% | Standard 4-bit + guess 4 words ahead (test only, off in the app) | 32.2 tok/s | 91.5% | | Qwen3.6-35B-A3B | Apple M3 Max, 36 GB (MacBook Pro) | 61.4 tok/s, quality – | base is still best | | | | Qwen3.5-0.8B | Apple M5 Pro (Mac mini) | 120.0 tok/s, quality 7% | Qwen3.5-0.8B + MIT CSAIL's p50c weight edit (reproduced) | 120.0 tok/s | 33% | Quality means something different per test, so compare it only within one section. ## Results ### Qwen3.5-4B (Smarty) on Apple M5 Pro (Mac mini) Measured 2026-10-22. | Version | Speed (tok/s) | vs base | Peak memory | Quality (top-1 match vs 8-bit) | Perplexity | Checks | Verdict | |---|---|---|---|---|---|---|---| | **Standard 4-bit (what we serve)** | 99.7 | | 4.13 GB | 86.5% | 14.60 | 10/10 | base | | ↳ MXFP4 (4-bit float) | about 104 | +4% | – | – | about 15.2 | – | no gain: 4% faster but 4% worse quality | | ↳ 4-bit, finer groups (g32) | about 91 | -9% | – | – | 14.54 | – | slower: slightly better quality, 9% slower | | ↳ Mixed 3/6-bit | 13-20% faster | | 9-13% less | – | +31-36% | – | worse: too lossy | | ↳ 3-bit | 13-20% faster | | 9-13% less | – | +31-36% | 9/10 | worse: too lossy, 1 wrong answer | | ↳ Standard 4-bit + 8-bit attention memory | same | | – | – | – | – | no gain: no difference on short chats | | ↳ 8-bit copy (quality yardstick, not served) | 57.0 | -43% | – | 100.0% | – | – | reference: more bits, slower | How it was measured: Speed: output tokens per second on a short chat (median of 3 after a warm-up). Memory: peak during a 4,096-token prompt. Quality: how often the version picks the same next word as a near-lossless 8-bit copy (top-1 match), plus 10 short questions with one right answer. Takeaway: the standard 4-bit build is still the best tradeoff. Speed tracks how many bits each weight uses, and below 4 bits quality drops sharply. Next to try: DWQ 3-bit. _Entered from the factory report as posted on the day; the full result files are on the Mac mini. Rows marked 'about' will be exact after the next run._ ### Qwen3.5-4B (Smarty), GSM8K math on Apple M5 Pro, 64 GB (Mac mini) Measured 2026-11-05. | Version | Speed (tok/s) | vs base | Peak memory | Quality (GSM8K, 500 problems right) | Paired vs base | Verdict | |---|---|---|---|---|---|---| | **Standard 4-bit (what we serve)** | 99.7 | | 4.13 GB | 89.0% | | base | | ↳ DWQ 3-bit (tuned to copy the full model) | 120.1 | +20% | 3.62 GB | 83.6% | +14 / -41 (p < 0.001) | worse: faster and smaller, but 2.5 to 8.3 points fewer right | | ↳ DWQ 4-bit (tuned to copy the full model) | 100.4 | +1% | 4.13 GB | 88.4% | +20 / -23 (p = 0.76) | no gain: same speed, no real difference in right answers | How it was measured: 500 GSM8K grade-school math problems (openai/gsm8k test, seed 0), the same for every version, served the way Smarty Pants serves Smarty: thinking off, greedy, up to 1,024 tokens. DWQ builds: mlx-lm DWQ, 1,024 samples, group size 64. A version is better only with more right answers at McNemar p < 0.05; faster counts only if the 95% interval of the accuracy change stays above -2 points. Script: scripts/model-factory/benchcheck.py. Takeaway: neither DWQ build beats the one we serve. 3-bit DWQ is about 20% faster and 13% smaller but gets about 5 points fewer right; 4-bit DWQ is no different. The served build stays. We keep negative results on the page. ### Qwen3.5-4B (Smarty), draft-ahead decoding on Apple M5 Pro, 64 GB (Mac mini) Measured 2026-11-05. | Version | Speed (tok/s) | vs base | Peak memory | Quality (GSM8K, 500 problems right) | Paired vs base | Verdict | |---|---|---|---|---|---|---| | **Standard 4-bit (what we serve)** | 95.9 | | 2.90 GB | 89.0% | | base | | ↳ Standard 4-bit + guess 4 words ahead | 105.2 | +10% | 3.43 GB | 88.8% | +4 / -5 (p = 1.0) | no gain: 10% faster, same right answers, but 18% more memory | | ↳ Standard 4-bit + guess 2 words ahead | 93.2 | -3% | 3.43 GB | 88.2% | +4 / -8 (p = 0.39) | slower: 3% slower and 18% more memory | How it was measured: draft-ahead (speculative) decoding: a small guesser model, mlx-community/Qwen3.5-0.8B-MLX-4bit, proposes 2 or 4 words and Smarty checks them in one pass. Same 500 GSM8K problems and served settings as above (thinking off, greedy, up to 1,024 tokens); the standard build was timed again in the same sitting. Guesses accepted: 85% (2 at a time), 76% (4 at a time). Takeaway: no win yet. Guessing 4 ahead is 10% faster with the same right answers, but it needs 18% more memory, and our rule allows at most 5% more. Guessing 2 ahead is slower. ### Qwen 3.8 27B on Apple M5 Pro (Mac mini) Measured 2026-11-02. | Version | Speed (tok/s) | vs base | Peak memory | Quality (top-1 match vs 8-bit) | Perplexity | Checks | Verdict | |---|---|---|---|---|---|---|---| | **Standard 4-bit (mlx-community)** | 35.7 | | 18.26 GB | 90.9% | 7.95 | 10/10 | base | | ↳ Mixed 3/6-bit | slower | | – | – | – | – | worse: slower and lower quality | | ↳ Standard 4-bit + 8-bit attention memory | same | | – | – | – | – | no gain: no difference on short chats | | ↳ 8-bit copy (quality yardstick, not served) | – | | – | 100.0% | 7.67 | – | reference: quality yardstick | How it was measured: Speed: output tokens per second on a short chat (median of 3 after a warm-up). Memory: peak during a 4,096-token prompt. Quality: how often the version picks the same next word as a near-lossless 8-bit copy (top-1 match), plus 10 short questions with one right answer. mlx 0.32.4. Takeaway: the standard 4-bit build runs well on a 32 GB+ Mac; neither tested change helped. _Entered from the factory report as posted on the day; the full result files are on the Mac mini. Rows marked 'about' will be exact after the next run._ ### Qwen 3.8 27B, draft-ahead decoding on Apple M5 Pro, 64 GB (Mac mini) Measured 2026-11-06. Important: this gain was measured greedy with thinking off. When our Mac app served it live at its default settings (temperature 0.6, thinking on), we did not see the speed-up, so the app has draft-ahead turned off again while we find out why. Do not expect this speed from Smarty Pants today. | Version | Speed (tok/s) | vs base | Peak memory | Quality (GSM8K, 200 problems right) | Paired vs base | Verdict | |---|---|---|---|---|---|---| | **Standard 4-bit (mlx-community)** | 17.8 | | 15.80 GB | 91.5% | | base | | ↳ Standard 4-bit + guess 4 words ahead (test only, off in the app) | 32.2 | +81% | 16.31 GB | 91.5% | +1 / -1 (p = 1.0) | better: 81% faster, same right answers, 3% more memory (in this test setup; not yet in the live app) | How it was measured: draft-ahead (speculative) decoding: the small mlx-community/Qwen3.5-0.8B-MLX-4bit guesses 4 words at a time and Qwen 3.8 27B checks them in one pass; 76% of guesses were kept. 200 GSM8K problems, the same for both, measured with mlx-lm in Python, greedy, thinking off, up to 1,024 tokens. The standard build was timed again in the same sitting, with serving paused (this sitting's standard speed is lower than the 35.7 tok/s chat test above, which used a different prompt and length). Takeaway: draft-ahead made Qwen 3.8 27B 81% faster with the same right answers and only 3% more memory, the first speed-up to beat a standard build on this page. ### Qwen3.6-35B-A3B on Apple M3 Max, 36 GB (MacBook Pro) Measured 2026-09-08. | Version | Speed (tok/s) | vs base | Peak memory | Quality | Perplexity | Checks | Verdict | |---|---|---|---|---|---|---|---| | **Standard 4-bit (mlx-community)** | 61.4 | | 21.52 GB | – | – | – | base | How it was measured: speed only: one request at a time, 500 to 4,096-token prompts, 128 tokens out, mlx-vlm 0.7.0. With 8 requests at once the Mac produced 193 tok/s in total. No quality test yet. ### Qwen3.5-0.8B on Apple M5 Pro (Mac mini) Measured 2026-11-04. | Version | Speed (tok/s) | vs base | Peak memory | Quality (MATH-500, 30 problems) | Finished | Verdict | |---|---|---|---|---|---|---| | **Qwen3.5-0.8B, original** | 120.0 | | 2.22 GB | 7% | 7% | base | | ↳ Qwen3.5-0.8B + MIT CSAIL's p50c weight edit (reproduced) | 120.0 | same | 2.19 GB | 33% | 60% | better: finished 60% of answers (was 7%), ended in a loop 3% (was 87%) | How it was measured: 30 MATH-500 math problems (seeded random pick), thinking on, up to 32,768 tokens, MIT's sampling settings, 1 answer per problem, graded with math-verify 0.9.0 by MIT's rule. Runs on MLX in bfloat16 rather than vLLM, so single answers are not comparable one to one with MIT's. Takeaway: the edit changes 0.016% of the weights. On our Mac it cut loops from 87% to 3% and raised correct answers from 7% to 33%, with no change in speed or memory. Problem by problem, the edited model got 8 right that the original missed and lost none (sign test p ≈ 0.008). MIT reports 7.9% to 48.1% on all 500 problems with 4 answers each. Credit: this is a reproduction of MIT CSAIL's published edit (Takehi, Teoh, Zhang and Khattab, [weight-space programming](https://rikiyat.github.io/blog/weight-space-programming), edit and evaluation code from [RikiyaT/lm_understand-logs](https://github.com/RikiyaT/lm_understand-logs)). Smarty Pants did not make this edit; we rebuilt it bit for bit, hash-checked it and measured it on a Mac. ## Use these models without a Mac Smarty Pants serves models from Macs like these at `https://smartypants.network/v1` (OpenAI-compatible). Smarty (`smartypants/smarty`) is the Qwen3.5-4B standard 4-bit above; see https://smartypants.network for which models are open and their prices. ## Data `results.json` holds every number on this page, one entry per section, rebuilt by `scripts/model-factory/benchmarks.py` after each factory run. No prompts, answers or machine identifiers are included.