arXiv:2606.28831v1 Announce Type: new Abstract: Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.g., Top-$p$ nucleus sampling) offer superior accuracy by dynamically fluctuating memory budgets, yet modern inference engines (e.g., vLLM) demand rigid, static memory patterns to leverage CUDA Graphs and PagedAttention. We resolve this ``Static-Dynamic'' mismatch with HARD-KV, a unified framework that that bridges dynamic selection with rigid system constraints. HARD-KV introduces a Cascade Cache hierarchy, managing the token lifecycle across dense, s
The proliferation of long-context LLMs and the increasing demand for efficient inference necessitate advanced memory management solutions.
This research addresses a critical bottleneck in LLM performance, directly impacting the scalability and cost-efficiency of large language models.
New techniques like HARD-KV enable more flexible and efficient memory allocation for LLMs, potentially leading to faster and more cost-effective AI inference for increasingly complex tasks.
- · AI model developers
- · Cloud computing providers
- · AI-powered applications
- · GPU manufacturers
- · Inefficient inference solutions
- · Organizations relying on previous generation LLM infrastructure
Improved performance and reduced operational costs for large language model deployment.
Accelerated development and adoption of more sophisticated and larger-context AI applications.
Increased accessibility and democratization of advanced AI capabilities due to lower computational barriers.
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Read at arXiv cs.LG