SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving
arXiv:2606.08635v1 Announce Type: new Abstract: Prefill-decode (PD) disaggregation decouples prompt processing from token generation, but it also turns the key-value (KV) cache into a network payload. Existing PD-side KV reduction methods are mostly binary: selected tokens are transmitted at full precision and the rest are not transmitted. This paper argues that binary selection leaves a useful design space unused. SpectrumKV assigns a precision level to each token instead: attention sinks and other high-importance tokens are protected at FP16, medium-importance tokens are sent at INT8, and lo
The increasing scale of LLMs and the demand for efficient serving necessitate continuous innovation in memory and communication optimization techniques.
This development improves the efficiency and scalability of large language model serving, directly impacting the cost and performance of AI applications.
Traditional binary KV cache reduction is replaced by a more nuanced, per-token mixed-precision approach, leading to better resource utilization and potentially lower inference costs.
- · Cloud providers
- · LLM developers
- · AI service providers
- · Data center operators
- · Less efficient LLM serving architectures
Reduced network bandwidth and memory footprint for LLM serving.
Improved throughput and reduced latency for disaggregated LLM inference leading to wider adoption.
Lower operational costs for AI inference could accelerate the deployment of more complex and larger AI models across various industries.
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Read at arXiv cs.LG