Qwen3.6 quants expose context tradeoffs

This is the kind of benchmark local LLM users actually need: not leaderboard theater, but memory-quality tradeoffs that decide whether a model fits your workload.

• –NVFP4(A4) may matter for batched serving because it can stay in 4-bit longer, while NVFP4A16 variants carry a larger footprint
• –The Cyan BF16-INT4 jump shows how mixed precision can quietly erase context headroom for marginal KLD gains
• –Qwen3.6-27B’s 262K-token context makes quant choice unusually consequential because every extra GB spent on weights is a GB not spent on KV cache
• –Early community results should be treated as directional, but they are useful for deciding which GGUF/NVFP build to download first