Qwen3.8 27B

User benchmarks Qwen3.8-27B on dual RTX 3090s, comparing llama.cpp against vLLM int8. llama.cpp with Q8_K_XL GGUF and q8_0 KV cache reaches 44.22 tok/s decode on narrative prompts, roughly half the 84.56 tok/s of the vLLM int8 w8a16 container with fp8 KV cache and MTP. Setup uses two RTX 3090s with a 0.55/0.45 split and ngl=99 for llama.cpp; vLLM uses custom all-reduce with P2P granted. Prefill at 10K tokens is 1710.16 tok/s for llama.cpp versus 1891.57 tok/s for vLLM. TTFT is about 5x slower on llama.cpp (337 ms vs 69 ms narrative). The 90K prefill run for llama.cpp was stopped early. User considers adding more system RAM for LMCache instead.