For users with 4x-8x 6000 PROs, how is your experience with bigger models lately? (GLM 5.2, Kimi 2.7, DeepSeek V4 Pro)

Hello guys, hoping you're doing fine!

I was wondering, for users with 4x-8x 6000 PROs (so between 384 and 768GB VRAM), how are bigger models working for you?

I have planned to either jump to 4 or 8 from my actual system, and want to see the experiences with these lately.

In theory you can run GLM 5.2 at 4 bits, but not 8 bits right? Same with Kimi 2.7, or DeepSeek V4 Pro. There is a ton of info here https://github.com/local-inference-lab/rtx6kpro/blob/master/benchmarks/results.md, but missing some of the latest models.

Is there a way too big agentic or programming performance hit by using less than 8 bits? I ask this mostly, because I have read that 4bit perf hit for agentic or programming is way too high vs 8bit, but for bigger models not sure how it really works here.

Are you running these on vLLM/SGLang or another backend?

Many thanks!