Performance comparison on full compute performance (Anima) and LLM prompt processing of 5090 (600,475 and 400W) vs 6000 PRO MaxQ shunt modded and water cooled (at 300, 400, 475 and 600W), and 6000 PRO WS/SE (600W).
Hello guys, hoping you're doing fine!
I'm continuing after this post some time ago, comparing stock MaxQ performance and such on Anima here.
This time, I shunt modded the 6000 PRO MaxQ, to use up to 2x amounts of power. These cards seems to be binned for high clocks and it is reflected after this.
(Note that you can also solder a R002 resistance on the empty pad and it would work the same)
I also did watercool them to manage the heat, with a Bykski block (this one) at 170USD each from Aliexpress and a GLZM 360mm AIO. So had to get the tubes, coolant and fittings.
For reference, at 300W it maxes at about 45°C, and at 600W it maxes at about 60°C.
I also rented on runpod, a 6000 PRO WS edition, which it's power limit ranges from 150W to 600W (yes, lower than the MaxQ)
Important note again: I did undervolt+overclock the 5090 and the 6000 PRO MaxQ. I can't modify the clocks or power on the rented GPUs on runpod.
So for this test, I ran these settings for the software for pytorch:
- Torch 2.14.0.dev20260612+cu132 for the 5090 and 6000 PRO MaxQ.
- Torch 2.13.0+cu132 stable for the 6000 PRO WS.
- Sageattention 2.1 (on commit e9b072f0fc2682f104abbda306af3d42fc33b969), self built on CUDA 13.3.
- Forge neo on commit 644450e8bf2df24f0ba87307604d0e9f4ae3a9f7
- Installed extensions for RTX Upscaling (https://github.com/Haoming02/sd-forge-nvidia-vfx) and for extra samplers (https://github.com/Panchovix/sd_forge_neo_extra_samplers)
- torch compile: max autotune no cudagraphs
I ran these settings for the samplers and steps:
On text:
- EXP Heun 2 x0 SDE for first 25 steps
- ER SDE for 10 hires pass steps
- Upscale by 1.5x
- 896x1088 resolution
- Batch size 4
- CFG 5
- Shift 3
- Denoise Strength: 0.2
- Upscaler: NVIDIA Ultra
- Seed: 50906000
Prompt used was:
Positive:
masterpiece, best quality, high quality, high resolution, absurdres, highres, very aesthetic, sfw,
[u/isshiki](/u/isshiki) \(ffmania7\),
1girl, solo, clothed,
aether foundation employee, pokemon, dark skin, black hair, short hair,
happy,
from above,
full body,
beige background
Negative:
worst quality, low quality, bad anatomy, (jpeg artifacts:0.8), watermark, sketch, no pupils
For LLMs, I ran llamacpp with a model offloaded to CPU, making the primary GPU the bottleneck when traversing the data, making it compute bound.
Models tested were (offloaded):
- Kimi K2 2.5 (IQ3_M)
- GLM 5.1 (IQ4_NL)
The LLM tests were only tested on my local machine, as testing on cloud via renting a GPU is not feasible or won't have accurate results.
For the hardware, I ran them headless, (with LACT), for Anima:
- RTX 5090 (Astral):
- 2930Mhz max core clock
- 1000Mhz core clock offset
- +4400Mhz on VRAM (total 16000Mhz)
- 400, 475 and 600W
- RTX 6000 PRO MaxQ (shunt modded, Watercooled):
- 2930Mhz max core clock
- 500Mhz core clock offset
- +5700Mhz on VRAM (total 16000Mhz)
- 300, 400 and 475W via undervolt + OC, 600W via TDP limit to 300W.
- RTX 6000 PRO WS:
- Stock
- 600W
For LLMs, used 500W for both GPUs, and for more reference I have this setup:
- RTX 6000 MaxQ (shunted) x2
- RTX 5090 x2
- RTX A6000
- NVIDIA A40
- RTX 4000 PRO SFF
- 192GB RAM DDR5 6000Mhz, Consumer AM5 + 9900X, PCIe 5.0 switch
So first, the results for the Anima ones look like this:
| GPU | Power | Notes | Core Clock | Time | vs 5090 at 600W |
|---|---|---|---|---|---|
| RTX 6000 PRO MaxQ | 600W | Shunt + watercooled (TDP) | 2442 Mhz | 32.7s | +12.8% |
| RTX 6000 PRO MaxQ | 475W | Shunt + watercooled (UV+OC) | 2160 Mhz | 35.3s | +5.9% |
| RTX 6000 PRO WS | 600W | Stock, rented | 2340 Mhz | 37.3s | +0.5% |
| RTX 5090 | 600W | UV+OC (baseline) | 2520 Mhz | 37.5s | - |
| RTX 6000 PRO MaxQ | 400W | Shunt + watercooled (UV+OC) | 1935 Mhz | 38.3s | -2.1% |
| RTX 5090 | 475W | UV+OC | 2160 Mhz | 42.9s | -14.4% |
| RTX 6000 PRO MaxQ | 300W | Watercooled (UV+OC) | 1530 Mhz | 46.6s | -24.3% |
| RTX 5090 | 400W | UV+OC | 1860 Mhz | 47.2s | -25.9% |
Or, using the 5090 at 400W for baseline:
| GPU | Power | Notes | Core Clock | Time | vs 5090 at 400W |
|---|---|---|---|---|---|
| RTX 6000 PRO MaxQ | 600W | Shunt + watercooled (TDP) | 2442 Mhz | 32.7s | +30.7% |
| RTX 6000 PRO MaxQ | 475W | Shunt + watercooled (UV+OC) | 2160 Mhz | 35.3s | +25.2% |
| RTX 6000 PRO WS | 600W | Stock, rented | 2340 Mhz | 37.3s | +21% |
| RTX 5090 | 600W | UV+OC | 2520 Mhz | 37.5s | +20.6% |
| RTX 6000 PRO MaxQ | 400W | Shunt + watercooled (UV+OC) | 1935 Mhz | 38.3s | +18.9% |
| RTX 5090 | 475W | UV+OC | 2160 Mhz | 42.9s | +9.1% |
| RTX 6000 PRO MaxQ | 300W | Watercooled (UV+OC) | 1530 Mhz | 46.6s | +1.3% |
| RTX 5090 | 400W | UV+OC (Baseline) | 1860 Mhz | 47.2s | - |
And then looking it from a efficiency perspective:
| GPU | Power | Notes | Energy/batch | Time | vs MaxQ at 300W |
|---|---|---|---|---|---|
| RTX 6000 PRO MaxQ | 300W | Watercooled (UV+OC) | 13.98 kJ | 46.6s | - |
| RTX 6000 PRO MaxQ | 400W | Shunt + WC (UV+OC) | 15.32 kJ | 38.3s | +9.6% |
| RTX 6000 PRO MaxQ | 475W | Shunt + WC (UV+OC) | 16.77 kJ | 35.3s | +19.9% |
| RTX 5090 | 400W | UV+OC | 18.88 kJ | 47.2s | +35.1% |
| RTX 6000 PRO MaxQ | 600W | Shunt + watercooled (UV+OC) | 19.62 kJ | 32.7s | +40.3% |
| RTX 5090 | 475W | UV+OC | 20.38 kJ | 42.9s | +45.8% |
| RTX 6000 PRO WS | 600W | Stock, rented | 22.38 kJ | 37.3s | +60.1% |
| RTX 5090 | 600W | UV+OC | 22.50 kJ | 37.5s | +60.9% |
And for the LLMs prompt processing ones, it look like this (remember all at 500W, but it uses way less, basically it reaches 2930Mhz on both GPUs:
| Model | GPU | t/s PP | vs 5090 |
|---|---|---|---|
| Kimi 2.5 IQ3_M (80GB offload) | RTX 6000 PRO MaxQ | 548.08 | +16.3% |
| Kimi 2.5 IQ3_M (80GB offload) | RTX 5090 | 471.40 | - |
| GLM 5.1 IQ4_NL (70GB offload) | RTX 6000 PRO MaxQ | 658.35 | +14.5% |
| GLM 5.1 IQ4_NL (70GB offload) | RTX 5090 | 574.98 | - |
So as can you see, we have these points:
- It really seems the MaxQ are binned for higher clocks, I guess it makes sense, so they don't lose much performance at low power.
- Now after a shunt, the sweet spot seems to be 475W on a mix between of performance and power. Most efficient one, and it makes sense, is 300W, as the card comes from the factory.
- 5090 seems to place quite behind, more than I would expect. Take in mind this is a "good" bin, which can do high clocks at low power.
- On LLMs, since it is not power limited, it is basically all what the core can give and just the difference of more CUDA cores, and when the active models are bigger, there is a bigger difference.
- At the same power on MaxQ shunt vs 5090:
- 400W: MaxQ is 23% faster.
- 475W: MaxQ is 21% faster.
- 600W: MaxQ is 15% faster.
Why you may ask? First, because I suspected MaxQ had better bins I expected, and indeed they were. It makes sense to have good bins to clock higher at 300-325W, and also to be manageable by the stock cooler.
Having the same power at 475W on both 5090 and 6000 PRO MaxQ but the latter being more than 20% faster is not something I expected, but that is a great surprise.
Also, because I'm just crazy, I have shunted a lot of cards already (5090, 4090, 3090, A6000, etc). Not recommended of course except if you know what you're doing, and are ready to lose the warranty.
Any question is welcome!
Posted Jul 11, 2026, 8:49 PM UTC