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Iniciando benchmark de 11 modelos... |
[gemma4:e2b] General / 2B / Dense |
Run 1: prefill=635.98 decode=84.76 |
Run 2: prefill=1095.17 decode=84.09 |
Run 3: prefill=1092.58 decode=85.22 |
-> gemma4:e2b: decode=84.69 tok/s |
[gemma4:e4b] General / 4B / Dense |
Run 1: prefill=161.56 decode=20.91 |
Run 2: prefill=420.65 decode=21.48 |
Run 3: prefill=420.84 decode=21.31 |
-> gemma4:e4b: decode=21.23 tok/s |
[gemma4:26b] General / 26B / MoE |
Run 1: prefill=3.83 decode=8.97 |
Run 2: prefill=226.03 decode=9.55 |
Run 3: prefill=194.78 decode=9.97 |
-> gemma4:26b: decode=9.50 tok/s |
[qwopus] Reasoning / 27B / IQ3_XS |
Run 1: prefill=3.99 decode=3.70 |
Run 2: prefill=9.29 decode=3.79 |
Run 3: prefill=8.00 decode=3.79 |
-> qwopus: decode=3.76 tok/s |
[deepseek-r1:14b] Reasoning / 14B / Dense |
Run 1: prefill=30.47 decode=4.90 |
Run 2: prefill=73.63 decode=4.94 |
Run 3: prefill=74.34 decode=4.89 |
-> deepseek-r1:14b: decode=4.91 tok/s |
[qwen2.5:14b] General / 14B / Dense |
Run 1: prefill=93.92 decode=4.82 |
Run 2: prefill=196.16 decode=4.89 |
Run 3: prefill=196.91 decode=4.82 |
-> qwen2.5:14b: decode=4.84 tok/s |
[qwen2.5:7b] General / 7B / Dense |
Run 1: prefill=574.59 decode=60.49 |
Run 2: prefill=953.09 decode=61.39 |
Run 3: prefill=1483.54 decode=61.39 |
-> qwen2.5:7b: decode=61.09 tok/s |
[qwen2.5-coder:7b] Coding / 7B / Dense |
Run 1: prefill=222.64 decode=60.80 |
Run 2: prefill=1504.39 decode=61.20 |
Run 3: prefill=1492.61 decode=60.58 |
-> qwen2.5-coder:7b: decode=60.86 tok/s |
[mistral:7b] General / 7B / Dense |
Run 1: prefill=279.20 decode=59.85 |
Run 2: prefill=604.70 decode=60.60 |
Run 3: prefill=411.98 decode=60.62 |
-> mistral:7b: decode=60.36 tok/s |
[phi3:mini] General / 3.8B / Dense |
Run 1: prefill=27.76 decode=20.43 |
Run 2: prefill=283.88 decode=19.89 |
Run 3: prefill=390.73 decode=20.40 |
-> phi3:mini: decode=20.24 tok/s |
[llama3.2:3b] General / 3B / Dense |
Run 1: prefill=690.30 decode=107.59 |
Run 2: prefill=912.40 decode=107.21 |
Run 3: prefill=1893.20 decode=108.58 |
-> llama3.2:3b: decode=107.79 tok/s |
Listo: /home/gio/HF_repos/benchmarks/all_models/results.md |
AMD ROCm Benchmarks — Positronica Labs
Benchmarks de modelos locales corriendo en GPU AMD con ROCm en Linux. Para la comunidad LatAm que corre AI sin Apple ni NVIDIA.
Benchmarks disponibles
| Carpeta | Modelos | Descripción |
|---|---|---|
| gemma4/ | Gemma 4 E2B, E4B, 26B | Gemma 4 día 3 del lanzamiento |
| all_models/ | 11 modelos | Master benchmark con guía de uso |
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AMD RX 6700 XT 12GB + Ryzen 5 5600G + 16GB RAM + Pop!_OS 24.04
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