You have to apply a lot of careful architecture and TDD to your approach. Eliminate technical risk by tackling hard things early and wrapping them up in a simple, easy to use interface.
I find I can get some projects done 2-3 times faster than if I wrote them by hand. It can also save about 5-10x time on mundane or broadly scoped projects by helping me consolidate and try out ideas very quickly.
Setup-wise, I switch between vLLM using nvidia/Gemma-4-31B-IT-NVFP4 and llama.cpp using unsloth/gemma-4-31B-it-qat-GGUF with MTP. I throttle the GPU power usage to 400W.
My current llama.cpp setup gets token generation rates between 60-150 t/s depending on MTP draft acceptance rates. Prefill is between 1500-4000 t/s depending on context length/depth.
If I get some time to circle back, I'll be sure to incorporate these into some new tests and address them.
I want to set up a qemu-system emulator based testing approach so I can incorporate things like AppArmor and SELinux into end to end tests that include different environment configurations.
Part of that will be setting up software defined networking so I can have things like DNS and wireguard VPN servers in a box and then test and evaluate the wg-wrap behavior at the packet level.
I find it useful.
This side project highlights a similar approach to how I scope and tackle projects at work now:
https://git.theodohertyfamily.com/wg-wrap.git/tree/README.md
https://git.theodohertyfamily.com/wg-wrap.git/tree/CASE_STUD...
You have to apply a lot of careful architecture and TDD to your approach. Eliminate technical risk by tackling hard things early and wrapping them up in a simple, easy to use interface.
I find I can get some projects done 2-3 times faster than if I wrote them by hand. It can also save about 5-10x time on mundane or broadly scoped projects by helping me consolidate and try out ideas very quickly.
Setup-wise, I switch between vLLM using nvidia/Gemma-4-31B-IT-NVFP4 and llama.cpp using unsloth/gemma-4-31B-it-qat-GGUF with MTP. I throttle the GPU power usage to 400W.
My current llama.cpp setup gets token generation rates between 60-150 t/s depending on MTP draft acceptance rates. Prefill is between 1500-4000 t/s depending on context length/depth.