llama.cpp debate spotlights context rot

The post is mostly right: in local inference, long-context coherence is often more meaningful than headline parameter count, and the best stack is the one that stays reliable on the hardware you actually own. The missing piece is rigorous benchmarking; without token-position tests on consumer GPUs, we’re mostly arguing from anecdotes and workload-specific experience.

• –`llama.cpp` shows the issue is a full-stack problem: quantized weights are only one part of the story, while cache policy and context handling decide whether long chats stay coherent.
• –KV-cache research treats the cache as a first-class bottleneck; long-context inference becomes memory-bound fast, and key/value compression is now a real research area.
• –Quantization methods are not interchangeable: GGUF is a container/format, EXL2 is a mixed-bit quantization scheme, and AWQ is weight-only quantization, so comparisons need the same runtime, cache settings, and context length.
• –What’s missing is a standard consumer-GPU benchmark for coherence decay over token position, not just perplexity or general-purpose leaderboards.
• –The practical takeaway is boring but important: a smaller model that stays reliable at 32k is often a better local tool than a larger model that starts drifting halfway through the job.