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oMLX - LLM inference, optimized for your Mac
https://github.com/jundot/omlx
Benchmark Model: Qwen3.8-27B-oQ4e-mtp
Engine: Auto
Context: Code (Python)
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Single Request Results
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Test TTFT(ms) TPOT(ms) pp TPS tg TPS E2E(s) Throughput Peak Mem
pp1024/tg128 4060.8 36.46 252.2 tok/s 27.6 tok/s 8.703 132.4 tok/s 16.41 GB
pp4096/tg128 16274.7 37.61 251.7 tok/s 26.8 tok/s 21.065 200.5 tok/s 17.97 GB
pp8192/tg128 33035.2 38.41 248.0 tok/s 26.2 tok/s 37.925 219.4 tok/s 18.88 GB
pp16384/tg128 68216.7 40.47 240.2 tok/s 24.9 tok/s 73.369 225.1 tok/s 20.71 GB
pp32768/tg128 144868.1 42.34 226.2 tok/s 23.8 tok/s 150.260 218.9 tok/s 24.35 GB
pp65536/tg128 326137.5 48.35 200.9 tok/s 20.8 tok/s 332.294 197.6 tok/s 31.68 GB
pp131072/tg128 811615.2 59.03 161.5 tok/s 17.1 tok/s 819.132 160.2 tok/s 46.79 GB
Continuous Batching
pp1024 / tg128
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Batch tg TPS Speedup pp TPS pp TPS/req TTFT(ms) E2E(s)
1x 27.6 tok/s 1.00x 252.2 tok/s 252.2 tok/s 4060.8 8.703
2x 51.6 tok/s 1.87x 195.4 tok/s 97.7 tok/s 10478.6 15.444
4x 76.1 tok/s 2.76x 246.0 tok/s 61.5 tok/s 16488.5 23.377
8x 85.4 tok/s 3.09x 244.7 tok/s 30.6 tok/s 32866.9 45.474Intelligence Benchmark Comparison
Mode Sampled Qwen3.8-27B-oQ4e-mtp
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MMLU Sample 1000/14042 89.7%
TRUTHFULQA Full 817 88.1%
HUMANEVAL Full 164 14.0%
LIVECODEBENCH Sample 300/1055 4.7%
--- Detail ---
Model: Qwen3.8-27B-oQ4e-mtp
Benchmark Accuracy Correct Total Time(s) Think
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MMLU 89.7% 897 1000 18034.6 Yes
TRUTHFULQA 88.1% 720 817 16666.3 Yes
HUMANEVAL 14.0% 23 164 3856.5 Yes
LIVECODEBENCH 4.7% 14 300 37976.1 Yes