Negative-Result
Nvidia's router dealt the cards evenly. That was the whole problem.
Nvidia PAIR split 20 requests 10/10 across an RTX 3090 and a 3060 for 1.07x, and my own router managed 0.52x. Plus ten LoRA seeds that all landed at or below the base model, and the llama.cpp pin moving to v0.4.0.
A 177B Model on a 3060: The 32 GB Number Nobody Measured
Qwen3.8-Flash-Next on an RTX 3090 and a 3060. Stock, the 3090 matches the video's 3060. One flag buys 34 to 40 percent. A real 32 GB box: 8.7 tok/s, not 22.
Nvidia PAIR Bought Me 7 Percent. My Own Router Cost Me Half.
Nvidia's new home-network AI router split 20 requests across an RTX 3090 and a 3060 for 1.07x over the 3090, then dropped half a 27B queue. Mine: 0.52x.
LoRA Skill Compilation Is a Double-Headed Coin Flip
Ten LoRA seeds on identical data spread 3.62 points, against 4.65 for prompt compilation. Not one beat the no-adapter base. Measured on an RTX 3090.
I got the result I wanted. Then I paid $4.97 to run it nine more times.
A frontier model wrote a skill that made our local 27B 10.6 points better. Nine more compilation runs showed the number was noise. Plus the LoRA substrate nobody has measured, MoE routing traced, and 277 GB in a file with no name.
I Got the AI Result I Wanted. Then I Ran It Nine More Times
A frontier model read my logs and wrote a skill that beat my local 27B baseline by 10.6 points. Nine more compilation runs showed the number was fake.