arXiv:2605.20868v1 Announce Type: new Abstract: KV cache quantization reduces the memory cost of long-context LLM inference, but introduces approximation error that is typically validated only empirically. Existing systems rely on average-case robustness, with no mechanism to detect or recover from failures at runtime. We present a tiered KV cache architecture that enables runtime-certified attention: INT8 keys and INT4 values are stored in GPU memory, while FP16 originals are retained in system RAM for deterministic fallback. A two-term error decomposition yields per-head, per-step bounds on
The increasing demand for long-context LLMs and the memory limitations of current hardware necessitate innovative solutions for KV cache management, while ensuring reliability.
This development allows for more reliable and efficient deployment of large language models in resource-constrained environments, directly impacting their commercial viability and wider adoption.
LLM inference can now leverage aggressive quantization techniques with a guaranteed runtime error bound, moving beyond purely empirical validation and enabling deterministic fallback.
- · AI model developers
- · Cloud providers
- · Edge AI hardware manufacturers
- · Companies with inefficient LLM deployment strategies
Reduced memory footprint and improved performance for long-context LLMs.
Accelerated development and deployment of more complex, reliable AI applications.
Increased competition in the AI inference market as more efficient solutions become available.
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Read at arXiv cs.LG