OCTOPUS: Optimized KV Cache for Transformers via Octahedral Parametrization Under optimal Squared error quantization
arXiv:2605.21226v1 Announce Type: new Abstract: The key-value (KV) cache dominates memory bandwidth and footprint in long-context autoregressive inference. Recent rotation-preconditioned codecs (TurboQuant, PolarQuant) show that a structured random rotation followed by a per-coordinate scalar quantizer matched to an analytically tractable marginal is a near-optimal recipe for KV compression. OCTOPUS advances this paradigm through joint quantization of rotated coordinate triplets. Each triplet's direction is mapped to a square via an octahedral parameterization, and the two resulting coordinate
The proliferation of increasingly complex AI models like large language models (LLMs) is pushing the limits of current hardware, particularly regarding memory bandwidth and footprint for KV caches in autoregressive inference.
This development proposes a significant advancement in AI model efficiency and scalability, directly impacting the deployment and computational cost of powerful AI systems.
OCTOPUS offers a potentially much more efficient way to manage memory in large transformer models, enabling larger contexts or more economical inference for existing models.
- · AI model developers
- · Cloud providers
- · AI hardware manufacturers
- · End-users of AI applications
- · Less efficient AI acceleration methods
- · Hardware manufacturers reliant on existing memory architectures without innovati
Reduced operational costs and increased capacity for AI inference lead to broader adoption and deployment of advanced AI applications.
The ability to run larger context windows more efficiently could lead to the development of new AI capabilities and applications previously constrained by memory.
This efficiency gain could accelerate the AI race, placing greater pressure on compute infrastructure and potentially exacerbating the compute-supply-chain and energy-bottleneck longer term.
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Read at arXiv cs.LG