SpectrumKV: Mixed-Precision Key-Value Cache Transfer for Distributed LLM Serving
Researchers have proposed SpectrumKV, a system that assigns per-token precision levels (FP16, INT8, or INT4) to key-value cache data transferred between prefill and decode servers in disaggregated large language model serving. Unlike existing binary token-selection methods, SpectrumKV treats KV transfer as a precision-allocation problem, using a lightweight deployment-time probe to determine each model's tolerance for aggressive quantization. The approach reduces time-to-first-token by 50–62% at a 50% KV budget while substantially outperforming the baseline PDTrim method on perplexity and retrieval benchmarks.
Prefill-decode (PD) disaggregation is a serving architecture that separates prompt processing from token generation across different hardware, but it creates a bottleneck by requiring key-value (KV) cache data to be transmitted over the network. Current approaches use binary selection—tokens are either sent at full precision or dropped entirely—leaving a middle ground unexplored. SpectrumKV fills this gap by assigning one of three precision tiers to each token: FP16 for attention sinks and high-importance tokens, INT8 for medium-importance tokens, and INT4 for low-importance tokens when the model can tolerate it. A critical finding is that INT4 tolerance is highly model-dependent: Qwen2.5-7B catastrophically degrades under INT4 KV quantization, while Mistral-7B and Gemma-2-9B remain stable. To handle this, SpectrumKV runs three aggressive Needle-in-a-Haystack (NIAH) trials at deployment time to probe each model and select either a three-tier or two-tier policy accordingly. At a 50% normalized KV budget on WikiText-2, SpectrumKV changes perplexity by at most +1.97% across tested models, compared to increases of +22–36% for PDTrim, and achieves 100% NIAH retrieval accuracy by a budget of b=0.5 for all three models. End-to-end GPU timing shows 50–62% reductions in time-to-first-token at b=0.5, suggesting that precision allocation is a more effective design axis than token pruning alone for PD KV transfer.
What's missing
The study evaluates only three models (Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3, Gemma-2-9B-it) and does not assess generalization to larger models or different architectures. Real-world network conditions and heterogeneous hardware configurations are not evaluated, and the paper does not address potential overhead from dynamically routing tokens to different precision pipelines at scale.
What different sources said
- arXiv cs.LGCenter
SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving
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