DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression

类型：arxiv
标识：2609.19969
链接：https://arxiv.org/abs/2609.19969
主分类：llm-infra
形态：application
TLDR：The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transfer bandwidth. Together, these compute, storage, and bandwidth demands constitute the primary bottleneck to further lowering deployment costs. To address this challenge, we introduce DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for context
待LLM分类：否
来源文件：
/inbox/tom/_candidates/2026-09-18-agent-rag-longcontext-candidates.json

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arXiv

2609.19969

TLDR

The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transfer bandwidth. Together, these compute, storage, and bandwidth demands constitute the primary bottleneck to further lowering deployment costs. To address this challenge, we introduce DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for context

DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression

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