arXiv (All)
open access
TierKV: Long-Context On-Device LLMs via Predictive Multi-Tier KV Caching
Large language models (LLMs) are moving onto mobile devices for increasingly diverse workloads over text, images, video, and audio. These applications often require long contexts, making the Key-Value (KV) cache a dominant memory bottleneck because it grows linearly with sequence length and is acces
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training#267627
- GroupKV: Hierarchical KV Cache Management for Long-Context Diffusion LLM Inference#1011307
- Just Talk Once: Communication-Efficient Split Federated LLM Fine-Tuning on Edge Devices#821496
- FreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inference#346538
- Preserving Provenance in Shared KV Caches for LLM Serving#1161006
Record · ID 1035140
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive