TL;DR: Local LLM users are seeking methods to maintain high prefill speeds when processing context windows up to 230k tokens.
Summary: Developers running models like Qwen on AMD GPUs are experiencing significant performance drops in prompt processing, with speeds falling from 850 t/s to 350 t/s as the context size scales up to 160k. The discussion highlights tradeoffs between different execution backends like Vulkan and HIP on Linux. While HIP offers approximately 10% faster prompt processing, it suffers from worse token generation performance and higher memory consumption.
Why it matters: Maintaining high prefill and prompt processing speeds is critical for building efficient, long-context agentic workflows on local consumer hardware. AI developers should monitor backend optimizations and context caching techniques to mitigate latency scaling in multi-turn agent runs.
Source: r/localllama