Tutti is a GPU-direct SSD-backed KV cache that removes CPU bottlenecks via object abstraction, GPU io_uring, and slack scheduling, delivering near-DRAM performance at 2x higher request rate and 27% lower cost than prior GDS-based systems.
Attentionstore: Cost-effective atten- tion reuse across multi-turn conversations in large lan- guage model serving.arXiv preprint arXiv:2403.19708, 52:20–38, 2024
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Hive is a multi-agent infrastructure with a logits cache for reducing cross-path redundancy in sampling and agent-aware scheduling for better compute and KV-cache allocation, shown to deliver 1.11x-1.76x speedups and 33%-51% lower hotspot miss rates.
TokenDance scales multi-agent LLM serving to 2.7x more concurrent agents by collective KV cache reuse and block-sparse diff encoding that achieves 11-17x compression.
Workload-aware optimizations for LLM serving in AML and fraud detection yield substantial gains in throughput, latency, and GPU utilization on synthetic compliance prompts.
citing papers explorer
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Tutti: Making SSD-Backed KV Cache Practical for Long-Context LLM Serving
Tutti is a GPU-direct SSD-backed KV cache that removes CPU bottlenecks via object abstraction, GPU io_uring, and slack scheduling, delivering near-DRAM performance at 2x higher request rate and 27% lower cost than prior GDS-based systems.
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Hive: A Multi-Agent Infrastructure for Algorithm- and Task-Level Scaling
Hive is a multi-agent infrastructure with a logits cache for reducing cross-path redundancy in sampling and agent-aware scheduling for better compute and KV-cache allocation, shown to deliver 1.11x-1.76x speedups and 33%-51% lower hotspot miss rates.
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TokenDance: Scaling Multi-Agent LLM Serving via Collective KV Cache Sharing
TokenDance scales multi-agent LLM serving to 2.7x more concurrent agents by collective KV cache reuse and block-sparse diff encoding that achieves 11-17x compression.
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Rethinking LLMOps for Fraud and AML: Building a Compliance-Grade LLM Serving Stack
Workload-aware optimizations for LLM serving in AML and fraud detection yield substantial gains in throughput, latency, and GPU utilization on synthetic compliance prompts.
- Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live