Memory, not the model.

The KV cache on this engine grows about 2.8 MiB per token of context, and the machine's 128 GB is already holding the 50.7 GiB resident trunk plus reserves the speculative-verify path needs

(we found the hard way that squeezing those makes the verifier reject wide batches and decode falls to single-token steps).

With the current reserves the engine admits ~4.4k tokens; that's a configuration ceiling you can raise by giving the cache more of the 128 GB and accepting less headroom elsewhere.

K3 itself supports far longer contexts — but see the prefill caveat above: on this setup long prompts cost minutes per 512 tokens until the scheduling fix lands.