Research Hardware & Infra·arXiv cs.LG·16h ago

TempoKV optimizes GPU memory staging for long-context LLM serving

TempoKV addresses a critical bottleneck in LLM inference: managing key-value cache memory when serving long-context models. As reusable KV caches exceed GPU capacity, systems must spill to SSD storage, but naive staging strategies either waste fast-tier space or incur retrieval latency. TempoKV decouples cache tracking from resource commitment, staging data only when runtime estimates predict imminent retrieval need. This timing-aware approach could meaningfully reduce serving latency and improve throughput for production LLM deployments handling variable request patterns, particularly relevant as context windows grow and batch serving becomes more complex.

Modelwire context

Explainer

TempoKV's core insight is decoupling cache metadata tracking from actual memory allocation, using runtime latency predictions to stage data only when needed. This is distinct from static cache policies; the system learns when retrieval will happen and pulls data preemptively, not reactively.

This complements CacheRepair (also from late September) in a specific way: CacheRepair solves the quality problem when KV caches are fused across chunks, while TempoKV solves the capacity and latency problem when caches exceed GPU memory. Together they address different failure modes in the same serving pipeline. TempoKV also connects to the broader infrastructure focus visible in Sol-H3 and E3J from the same week, where the field is shifting from raw model capability to deployable inference paths. The common thread is removing bottlenecks that prevent production adoption of larger or longer-context models.

If TempoKV's latency gains hold on production workloads with variable batch sizes and context lengths (not just synthetic benchmarks), and if a major serving framework like vLLM or SGLang integrates the staging logic within six months, that signals the technique is moving from research to infrastructure. If adoption stalls despite positive numbers, it likely means the engineering overhead of runtime prediction outweighs the gains in practice.

Coverage we drew on

CacheRepair: Learning to Repair Cross-Chunk Context in RAG for KV Cache Fusion · arXiv cs.LG

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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “TempoKV: Timely Staging of LLM KV Caches for Memory-Semantic Flash”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.