arXiv (All)
open access
DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
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-transf
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
- Reshaping Rollout Workloads for Asynchronous RL Post-Training on Heterogeneous Accelerators#1132100
- Reshaping Rollout Workloads for Asynchronous RL Post-Training on Heterogeneous Accelerators#1122264
- PulseInfer: I/O-Centric Sparse KV Cache Offloading for Efficient Long-Context LLM Decoding#1128820
- SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving#267624
- Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs#673488
Record · ID 1033751
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