Qwen3.8 27B
on 3× NVIDIA RTX 3090 · llama.cpp · 262,144 ctx
Use cases
codingagenticlong-context
Summary
User reports running Qwen3.8-27B at 44-46 t/s at the start of context and 28-30 t/s as context fills, on three RTX 3090 GPUs.
Setup is self-compiled llama.cpp with Q8_0 GGUF, 262144 context, tensor split 14,24,24, and ngram-mod plus draft-mtp speculative decoding. The 3090s are power-limited to 300W.
The user notes the machine is a primary desktop, so 2-3 GB of VRAM is reserved for the OS and applications, leaving roughly 62GB usable and 10-12 GB spare at full context.