arXiv Machine Learning
GroupKV: Hierarchical KV Cache Management for Long-Context Diffusion LLM Inference
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arXiv AI
Sep 3
HeadWiseKV: Budgeted Per-Head Cache Residency for Hybrid Long-Context Language Models
HeadWiseKV is a training‑free framework that compresses the residual global key–value caches of hybrid long‑context language models by assigning each physical KV head a static, multilevel history window. It formulates cache allocation as a restricted operational rate–distortion problem and uses the SeqCalib algorithm to generate per‑head residency policies that account for interactions across layers. In evaluations on four hybrid models, HeadWiseKV preserves near‑full‑KV quality while reducing peak device memory usage by 8.59% at a 112K context length and extending the largest verified context from 114K to 161K.
Hugging Face Trending Papers
Sep 2
HeadWiseKV: Budgeted Per-Head Cache Residency for Hybrid Long-Context Language Models
Long-context inference retains a growing key--value (KV) cache during decoding, which consumes substantial GPU memory and can reduce generation throughput. This bottleneck remains in hybrid language m...
arXiv AI
Jun 9
End-to-End Context Compression at Scale
arXiv:2606. 09659v1 Announce Type: cross Abstract: Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length.
Hugging Face Trending Papers
Jun 8
End-to-End Context Compression at Scale
Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degrade model quality substantially or require considerable time and compute to compress a single long prompt.
arXiv Machine Learning
3d ago
Physically Partitioned KVCache Format for CPU--GPU Load Balancing in MoE Inference
arXiv:2609.14507v1 Announce Type: cross Abstract: Single-GPU long-context inference with Mixture-of-Experts (MoE) models requires spilling the key-value cache (KVCache) to CPU memory. The spilled KV...
arXiv AI
Jun 16
PolyKV: Heterogeneous Retention and Allocation for KV Cache Compression
arXiv:2606. 15157v1 Announce Type: cross Abstract: KV cache compression is essential for reducing the memory cost of long-context large language model inference.