You can get all the Qwen 3.x models up to ~1 million tokens using YaRN with llama.cpp.[0]
Personally I am using `--no-context-shift` and feeding in context back in on failure at the harness level.
I have 2x1080ti + 1xTitanV that have a full 262,144 tokens context on 262,144 tokens with `-sm tensor` at 62.04 t/s which isn't so bad.
But I also have a 1x3090 running unsloth/Qwen3.6-27B-MTP-GGUF:UD-Q4_K_XL at 41.89 t/s but with only 130k context, but if you have a modular programming style both work pretty well.
Personally I am using `--no-context-shift` and feeding in context back in on failure at the harness level.
I have 2x1080ti + 1xTitanV that have a full 262,144 tokens context on 262,144 tokens with `-sm tensor` at 62.04 t/s which isn't so bad.
But I also have a 1x3090 running unsloth/Qwen3.6-27B-MTP-GGUF:UD-Q4_K_XL at 41.89 t/s but with only 130k context, but if you have a modular programming style both work pretty well.
But play with YaRN if you really need it.
[0]https://qwen.readthedocs.io/en/v3.0/run_locally/llama.cpp.ht...