Qwen3.5-35B quants split ROCm, Vulkan

This is the kind of benchmark local AI developers actually care about: not abstract model hype, but concrete backend tradeoffs on real AMD hardware.

• –Vulkan starts out clearly ahead on prompt ingestion, then converges toward ROCm as context grows
• –ROCm holds a consistent lead on token generation, which matters more for long interactive sessions
• –Q4_0 and Q4_1 staying on top suggests older, leaner quant formats still dominate when pure speed is the goal
• –The gap between bartowski's IQ4_NL and Unsloth's UD-IQ4_NL shows quant packaging and implementation details can materially affect throughput
• –Flash attention looking universally faster here is a useful practical takeaway for llama.cpp users tuning MI50 setups