Linear-attention hybrids may need finer caching for long prompts and workflows
stochasticchasm · x · 2026-07-28
The post highlights a subtle production issue for linear-attention hybrids: unlike normal attention, they do not naturally preserve full KV caches, so operators may need finer-grained caching for long system prompts, workflows, and other prefix-heavy requests.
It also points to a section on sandbox infrastructure, where pause/resume and forking are called out as especially useful. The author argues that long-tail branching rollouts may become important, and that the internal eval section is revealing because it maps out gaps in the model/platform stack through many in-house benchmarks.
Related event: LLM Inference Infrastructure: Long-Context Cache and Agent Sandboxes(2 posts)→
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