The MoE variants are more cache efficient. Here from Qwen3.6 35B A3B MoE with 256k (262144) context at full F16 (so no cache quality loss):

  llama_kv_cache: size = 5120.00 MiB (262144 cells,  10 layers,  4/1 seqs), K (f16): 2560.00 MiB, V (f16): 2560.00 MiB

The MXFP4-quantized variant from Unsloth just fits my 5090 with 32GB VRAM at 256k context.

Meanwhile here's for Qwen 3.6 27B:

  llama_kv_cache: size = 3072.00 MiB ( 49152 cells,  16 layers,  4/1 seqs), K (f16): 1536.00 MiB, V (f16): 1536.00 MiB

So 16 tokens per MiB for the 27B model vs about 51 tokens per MiB for the 35B MoE model.

I went for the Q5 UD variant for 27B so could just fit 48k context, though it seems if I went for the Q4 UD variant I could get 64k context.

That said I haven't tried the Qwen3.6 35B MoE to figure out if it can effectively use the full 256k context, that varies from model to model depending on the model training.