Results from testing my new Omarchy server. Open test bench, no case yet.
| Component | Model |
|---|---|
| CPU | AMD Ryzen 7 9800X3D |
| AIO | Cooler Master 240 Core II |
| MBD | Gigabyte B850I Aorus Pro |
| RAM | Crucial Pro 128GB (2x64GB) DDR5-5600 CL46 |
| SSD | Samsung 2TB 990 EVO PLUS |
| GPU | MSI RTX 5090 Ventus 3X OC |
| PSU | Corsair SF1000 |
| MON | UPERFECT UColor O2 16" 2880x1800 OLED 120Hz |
| Benchmark | Score | Unit |
|---|---|---|
| geekbench6 | 3440 | single |
| geekbench6 | 18769 | multi |
| cyberpunk 2077 | 83 | fps |
| wan 2.2 t2v | 40 | sec |
| sysbench cpu | 49752 | ops/s |
| sysbench memory | 29600 | MiB/s |
| gemma3:4b | 302 | tok/s |
| gemma3:27b | 77 | tok/s |
| qwen3-coder:30b | 241 | tok/s |
| gpt-oss:20b | 255 | tok/s |
https://browser.geekbench.com/v6/cpu/15307954
- CPU: AMD Ryzen 7 9800X3D (8c/16t @ 5.27 GHz)
- Board: Gigabyte B850I AORUS PRO
- RAM: 123 GB
- Geekbench 6.5.0
| Test | Single | Multi |
|---|---|---|
| Overall | 3440 | 18769 |
5 min combined CPU + GPU stress (open test bench, no case)
stress-ng --cpu $(nproc) --timeout 5m & gpu_burn 300tuned = CPU -35 Curve Optimizer, GPU 480W power limit (LACT)
| Config | CPU Temp | CPU Power | GPU Temp | GPU Power |
|---|---|---|---|---|
| Stock | 92°C | 128W | 75°C | 572W |
| Tuned | 72°C | 100W | 71°C | 480W |
2880x1800 (62.5% of 4K, 140% of 1440p)
| Preset | Run | Avg | Min | Max |
|---|---|---|---|---|
| RT: Overdrive | 1 | 83.17 | 73.77 | 92.87 |
| RT: Overdrive | 2 | 82.71 | 73.78 | 91.79 |
| RT: Ultra | 1 | 122.83 | 109.13 | 139.11 |
| RT: Ultra | 2 | 122.60 | 108.27 | 138.82 |
video_wan2_2_14B_t2v.json (default)
| Run | Time |
|---|---|
| 1 | 47.62s |
| 2 | 39.80s |
| 3 | 40.40s |
$ sysbench cpu --threads=$(nproc) run
CPU speed:
events per second: 49752.26
General statistics:
total time: 10.0003s
total number of events: 497564
Latency (ms):
min: 0.16
avg: 0.32
max: 12.82
95th percentile: 0.32$ sysbench memory --threads=$(nproc) run
Total operations: 104857600 (30310208.21 per second)
102400.00 MiB transferred (29599.81 MiB/sec)
General statistics:
total time: 3.4589s
total number of events: 104857600
Latency (ms):
min: 0.00
avg: 0.00
max: 12.68
95th percentile: 0.00$ ollama run gemma3:4b --verbose "Write a haiku"
Green leaves softly sway,
Sunlight dances on the breeze,
Peaceful, quiet day.
total duration: 185.012485ms
load duration: 104.40441ms
prompt eval count: 13 token(s)
prompt eval duration: 3.990041ms
prompt eval rate: 3258.11 tokens/s
eval count: 21 token(s)
eval duration: 69.511282ms
eval rate: 302.11 tokens/s$ ollama run gemma3:27b --verbose "Write a haiku"
Green leaves softly sway,
Sunlight dances on the breeze,
Summer's gentle kiss.
total duration: 418.834894ms
load duration: 105.766998ms
prompt eval count: 13 token(s)
prompt eval duration: 34.768012ms
prompt eval rate: 373.91 tokens/s
eval count: 21 token(s)
eval duration: 272.539731ms
eval rate: 77.05 tokens/s$ ollama run qwen3-coder:30b --verbose "Write a haiku"
Cherry blossoms fall
Soft petals dance on warm breeze
Spring's gentle farewell
total duration: 128.89472ms
load duration: 41.735052ms
prompt eval count: 13 token(s)
prompt eval duration: 5.566987ms
prompt eval rate: 2335.19 tokens/s
eval count: 19 token(s)
eval duration: 78.643085ms
eval rate: 241.60 tokens/s$ ollama run gpt-oss:20b --verbose "Write a haiku"
Thinking...
We need to write a haiku. Haiku is a 5-7-5 syllable Japanese poetic form, typically with nature. We'll produce a haiku. The user just says "Write a haiku". So we give a short poem. Let's produce a nature-themed haiku, 5-7-5. We'll do something like:
Autumn wind sighs,
Leaves dance, a silver river,
Night cradles the hush.
Let's count: "Autumn wind sighs," - 5 syllables? Let's count: Au-tumn (2), wind (1) =3, sighs (1) =4. Actually "Autumn" has 2 syllables, "wind" 1, "sighs" 1 = 4. We need 5. We can add "softly" maybe. Let's produce a proper one.
Better: "Autumn wind softly" = 2+1+2 =5. Good. Then "Leaves dance, a silver river" Count: Leaves (1), dance (1) =2, a (1)=3, sil-ver (2)=5, ri-ver (2)=7. Yes 7. Then "Night cradles the hush." Count: Night(1), cradles(1)=2, the(1)=3, hush(1)=4. Need 5. Add "soft" maybe. "Night cradles soft hush." Count: Night(1), cradles(1)=2, soft(1)=3, hush(1)=4. Still 4. Add "deep" maybe. "Night cradles deep soft hush." Count: Night(1), cradles(1)=2, deep(1)=3, soft(1)=4, hush(1)=5. Good.
Alternatively produce a classic haiku like "Frost on the pond / The fish lies dormant below / Silence fills the air." Let's check syllable counts: "Frost on the pond" Frost(1), on(1)=2, the(1)=3, pond(1)=4. Need 5. Add "quiet" maybe. "Frost on the quiet pond" 1+1+1+1+1 =5. Then second line: "The fish lies dormant below". The(1) fish(1)=2, lies(1)=3, dor-mant(2)=5, be-low(2)=7. Third: "Silence fills the air" Silence(2)=2, fills(1)=3, the(1)=4, air(1)=5. That works.
Let's produce a haiku. Provide 3 lines. Probably no explanation needed.
Ok produce.
...done thinking.
Frost on the quiet pond
The fish lies dormant below—
Silence fills the air.
total duration: 2.529069643s
load duration: 92.59669ms
prompt eval count: 72 token(s)
prompt eval duration: 4.553645ms
prompt eval rate: 15811.51 tokens/s
eval count: 595 token(s)
eval duration: 2.333744834s
eval rate: 254.96 tokens/s