aiqbench analysis
Geodesia-KV introduces a novel, training-free policy for KV cache compression that resolves critical measurement errors between resident bits and read bandwidth. By enabling monotonic precision allocation and query-sparse reading, this approach achieves significant VRAM savings on consumer hardware while maintaining model accuracy across major architectures like Qwen and Llama. The method effectively extends context windows to 1M tokens on a single GPU, offering a practical path toward efficient long-context inference without dense cache residency.
Squeezing the Cache, Preserving the Truth: Monotonic Equipotential Allocation with Geodesia-KV
arXiv:2608.23599v1 Announce Type: cross Abstract: Current assessment of KV-cache compression performance confuses resident bits with read bandwidth and is affected by the artifacts of chunked teacher-forcing. We present Geodesia-KV, a family of training-free KV cache policies based on monotonic block-wise precision allocation, exact rate-distortion residuals, and query-sparse reading, enabling proper hardware-ready compression. With proper separation of resident and read bits and causal evaluation, we show that Geodesia-KV significantly outperforms other approaches. Specifically, on WikiText-2 with 16k context, the 5-bit operating point of Geodesia-KV results in lower perplexity at lower bitrate than KIVI-4 on Qwen. In addition, our compressed-Quest version delivers improved perplexity and reduces resident (9.83 vs 16.25 bits/value) and read rates (1.95 vs 2.32 bits/value) over baseline sparse methods on PG-19. As Geodesia-KV is implemented as native GeodesiaKVCacheManager plug-in of vLLM, Geodesia-KV fully removes the need for dense cache residency via monotonic bit demotion. With the full consumer hardware evaluation, Geodesia-KV leads to 1M-token context generation on a single 16 GiB GPU with up to 71.7% peak VRAM savings on all leading architectures (Qwen, Llama, DeepSeek).
Abstract:Current assessment of KV-cache compression performance confuses resident bits with read bandwidth and is affected by the artifacts of chunked teacher-forcing. We present Geodesia-KV, a family of training-free KV cache policies based on monotonic block-wise precision allocation, exact rate-distortion residuals, and query-sparse reading, enabling proper hardware-ready compression. With proper separation of resident and read bits and causal evaluation, we show that Geodesia-KV significantly outperforms other approaches. Specifically, on WikiText-2 with 16k context, the 5-bit operating point of Geodesia-KV results in lower perplexity at lower bitrate than KIVI-4 on Qwen. In addition, our compressed-Quest version delivers improved perplexity and reduces resident (9.83 vs 16.25 bits/value) and read rates (1.95 vs 2.32 bits/value) over baseline sparse methods on PG-19. As Geodesia-KV is implemented as native GeodesiaKVCacheManager plug-in of vLLM, Geodesia-KV fully removes the need for dense cache residency via monotonic bit demotion. With the full consumer hardware evaluation, Geodesia-KV leads to 1M-token context generation on a single 16 GiB GPU with up to 71.7% peak VRAM savings on all leading architectures (Qwen, Llama, DeepSeek).
11 pages, 3 figures, 8 tables. Code and vLLM integration available at this https URL
Hardware Architecture (cs.AR); Computation and Language (cs.CL)
arXiv:2608.23599 [cs.AR]
(or arXiv:2608.23599v1 [cs.AR] for this version)
https://doi.org/10.48550/arXiv.2608.23599 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 283 words
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