AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
It is argued that a KV cache server — disaggregated, CXL-attached memory device(s) with optional near-memory compute—is the correct infrastructure primitive for the next generation of AI data centers.
This work presents InferScale, a GPU-native LLM memory system that replaces repeated prompt prefilling with reusable KV state, and encodes each memory fact together with a small window of preceding conversation context while caching only the target fact's KV.
OasisKV is presented, a memory-centric LLM inference system design that alleviates HBM capacity pressure by decoupling full KV-cache storage from HBM during LLM decoding and observes that future important tokens can be predicted accurately in advance using lookahead tokens drafted by speculative decoding (SD).
DualDecoder is presented, a lightweight serving system for long-context LLM inference that enables efficient sparse KV cache retrieval from host memory that leverages a novel dual-token decoding pipeline that accurately identifies critical KV entries with negligible computational overhead.
FLINT is proposed, a workload-driven HBF substrate for capacity-scalable LLM inference that integrates HBF as a memory-capacity tier alongside HBM while addressing three adoption challenges.
This model reveals one key opportunity: dividing a restore request proportionally between the storage path and the GPU can improve inference performance while still meeting SLOs, and reduces the KV-cache storage stack to a performance model based on per-tier capacity, per-tier and interconnect bandwidth, and GPU arithmetic throughput to identify optimization opportunities for KV-cache management.
Long-context, multi-turn, and agentic LLM workloads increasingly reuse previously processed context, making KV-cache reuse essential for reducing redundant computation. However, this reuse shifts the bottleneck to the memory tier that stores and serves reusable KV states at cluster scale. GPU HBM and host DRAM are too costly to scale to TB-scale shared context capacity, motivating remote tiers built from lower-cost, higher-capacity media. This paper presents HyMCache, a CXL memory rack for multi-turn LLM serving. We build the memory rack using cost-efficient CXL-hybrid memory (CXL-HM), which combines a small amount of in-device DRAM with large SSD-backed capacity behind a CXL interface. By exploiting the read-dominant, predictable, and append-only nature of multi-turn KV-cache access, HyMCache rethinks DRAM management within CXL-HM to efficiently support TB-scale SSD-backed KV reuse. It uses request-level prefix prefetching and opportunistic write buffering to stage latency-critical reads in device DRAM, enabling DRAM-scale KV-cache efficiency at SSD-level cost. We evaluate HyMCache on a real CXL-HM prototype under both single-aggregator and PD-disaggregated serving configurations. Under the same DRAM budget, HyMCache outperforms local LMCache by 3.0x in single-node serving and 1.45x in PD-disaggregated serving. Compared with 1 TB distributed-DRAM Mooncake, HyMCache incurs about 30% lower performance but uses 16x less DRAM.
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