This is my RX 550 running at 25% power rating calculating v 2.10 OpenCL 1 WU at a ttime:

Task: 3826495
WU: 1969904
2 Feb 2021, 14:35:04 UTC
3 Feb 2021, 22:40:15 UTC
Completed and validated
Run: 20716.73
CPU: 7.25
Credit: 2939.68
Multichunk population seed cactus stacking v2.10 (opencl_amd)
windows_x86_64

This is my NVidia 1060 3GB running at 40% power usage calculating on ver 2.12 OpenCL 8 WU's at a time.

Task: 4582705
WU: 2321899
6 May 2021, 10:33:03 UTC
6 May 2021, 18:50:02 UTC
Completed and validated
Run: 20835.89
CPU: 20835.89
Credit: 3753.25
Multichunk population seed cactus stacking v2.12 (opencl_nvidia)
windows_x86_64

I do not remember all other project NVidia OpenCL WU's requiring 100% of a CPU thread.
CUDA WU's never use this much CPU.

The CPU's are needed for other projects and the AMD in this box is 1:3 FP64, so needed for MilkyWay@Home.
(The RX 550 died.... energy efficient, cheap and worked it's ass off. I will miss you buddy.)

Answer from dev Hy on another thread:

In general the Nvidia driver's interaction/usage of OpenCL is a nightmare as it happily locks a whole thread and does nothing inside the locked thread aside from syncing stuff. AMD and Intel GPUs don't have this problem.

Anyway known to tame this behavior with custom drivers, particular driver versions, other techniques?
BOINC's CPU usage setting in the app_config.xml is completely ignored.

Only solution I currently have is to starve Kaktwoos CPU usage by using Process Hacker to lower Kaktwoos process to IDLE and raising the competing CPU project WU process to NORMAL or BELOW NORMAL giving it precedence.
I'll have to run tests (probably with Asteroids@Home CPU in competition) to see how much of a performance hit the NVidia WU's take and let you know my test results.

At some point in 2.13 also I swapped the kernel out, so your GTX 1060 should be utilized like 5-10% more than before, because reasons. RTX GPUs and GTX 1600 series cards will use the same Nvidia kernel found in 2.11/2.12 so everyone can get boosted speed, or higher avg speed of seed calculations without weaker or older cards missing out. Note that Kaktwoos is only designed to run one instance per GPU, so if you mean "8X" to be "8 kaktwoos-cl tasks on 1 GTX 1060", then there's no guarantee of any good outcomes / usable systems / efficient calculations from that. It's a resource issue and more isn't better, unlike some other GPU projects.

Yes, I was attempting 8 WU's at once and was dropping down 2 at a time as a performance/efficiency test.
From what you say then either 1 or possibly 2 WU's would be most efficient.

I'll skip the tests for 6 WU and 4 WU then (8x WU is already completed and makes a baseline), thanks.