AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes ActKV, the first KV cache compression framework tailored for agentic LLM inference, and establishes a compression criterion that values KV entries by their contribution to action generation and prioritizes action quality.
The token-level analysis reveals pronounced structural locality in DLM inference, and proposes a window-based token pruning and caching method for inference that achieves up to $99\times inference speedup while largely preserving generation performance.
This work is the first to recast expert activation prediction as sequence modeling, enabling accurate multi-step, multi-layer forecasts that provide a long and reliable window for downstream decisions, and derives general runtime principles encompassing compute-transparent expert placement and synchronization-free orch...
Under W4A4 quantization and 30%--70% sparsity, UnionSparse outperforms FlashLLM and SpInfer by 2.30x and 1.43x, and CUTLASS and cuBLAS Tensor Core by 1.56x and 3.46x, respectively.
This work designs MD-SpMM, an N:M sparse CUDA kernel that reorganizes sparse GEMM into micro-dense, Tensor-Core-aligned dataflow and uses inference-aware adaptive parallelism to sustain utilization and performs layer-wise sparsity search under explicit end-to-end latency budgets.
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