The model card went up before the weights, and unlike most pre-release cards it publishes the full head configuration. So the KV cache cost is computable right now.

Published layout is 64 layers, arranged as 16 x (3 x Gated DeltaNet -> 1 x Gated Attention). Gated Attention uses 4 KV heads at head dimension 256. Only those 16 full-attention layers pay for cache, so the per-token cost is 16 x 4 x 256 x 2 (K+V) x 2 bytes, which is 65,536 bytes, or exactly 64 KiB per token. At 262,144 native context that comes to exactly 16 GiB of KV, before weights. The 48 DeltaNet layers hold a fixed recurrent state instead, which I estimate at roughly 72 MiB and which does not grow with context.

At Q4\_K\_M that leaves you with roughly this. On 16 GB you fit the weights and essentially nothing else. On 24 GB you get around 100K context at f16 KV, or around 180K at q8. On 32 GB you can hold the full 262K at q8. On 80 GB you can run FP8 weights with full context and still have headroom to batch.

I ran the same math against Qwen3.6-27B, which I have benched. At 90K context with f16 KV and Q4\_K\_M weights, predicted allocation was 16,105 MiB of weights plus 5,625 MiB of KV, so 21,730 MiB total. Measured was 22,118 MiB. At q8 KV the 180K and 200K runs both came in about 1.5 GB over prediction, which is consistent with each other and with context checkpoints plus compute buffers. I do not have a clean explanation for why the f16 run's overhead was so much lower than the q8 runs', so treat the numbers above as approximate at the margins.

The part I found interesting is that 3.6 was also 64 KiB per token. The hybrid DeltaNet layout is the memory-wall fix and it does work, roughly a 4x cut against full attention, but it landed in 3.5 and has not moved since. Two generations of gains have all gone into quality rather than into cache cost.

One other thing worth flagging: the card recommends 224k video tokens for hour-scale video, which is 13.7 GiB of KV for the video alone, before your prompt.

If anyone pulls the weights and has config.json open, I would like to know what the vision tower adds on top. That is the one number I cannot get from the card.

Qwen3.8-27B KV cache works out to 64 KiB/token, so full 262K context needs 16 GiB on top of the weights