SpectrumKV: Mixed-Precision Key-Value Cache Transfer for Distributed LLM Serving
Researchers propose SpectrumKV, a technique that assigns different precision levels to individual tokens in key-value caches during distributed language model inference, rather than using binary keep/discard decisions. The method protects high-importance tokens at full precision (FP16) while compressing less critical tokens to INT8 or INT4, with model-dependent tolerance determined by lightweight deployment-time testing. This approach significantly reduces network transfer overhead while maintaining model quality and retrieval accuracy across multiple language models.
SpectrumKV addresses a challenge in prefill-decode disaggregated LLM serving, where key-value caches must be transmitted over the network between processing stages. Rather than existing binary approaches that either transmit tokens at full precision or not at all, SpectrumKV treats KV cache transfer as a precision-allocation problem. The system assigns FP16 precision to attention sinks and high-importance tokens, INT8 to medium-importance tokens, and INT4 to low-importance tokens when the model can tolerate it. A key insight is that INT4 tolerance varies by model: Qwen2.5-7B fails catastrophically under INT4 quantization while Mistral-7B and Gemma-2-9B remain stable. SpectrumKV uses a lightweight three-trial probe at deployment time to determine each model's tolerance level. Experimental results show substantial improvements over prior work (PDTrim), with perplexity changes of +1.97%, -0.06%, and -0.44% versus +25.85%, +22.07%, and +35.63% respectively across three models at 50% normalized KV budget. On needle-in-haystack retrieval tasks, SpectrumKV achieves 52.6% accuracy on Qwen at aggressive compression versus 26.3% for PDTrim, with end-to-end timing improvements of 50-62% in time-to-first-token.
What's missing
The paper does not discuss computational overhead of the deployment-time probing procedure itself, nor does it compare against other recent mixed-precision or adaptive quantization approaches beyond PDTrim. The generalization of the three-tier policy to other model architectures and sizes beyond the tested 7B-9B range remains unclear.
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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