Proposes the Intelligent Computing Architecture (ICA) as a six-layer framework with dual probabilistic-deterministic planes and three Amdahl-style heuristics to unify design of LLM-based systems.
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Mooncake: A kvcache-centric disaggregated architecture for llm serving
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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.
GreenCache dynamically manages LLM KV cache resources to reduce carbon emissions by 15.1% on average (up to 25.3%) while meeting latency constraints for over 90% of requests on real traces.
OmniPilot combines conformal quantile regression with OOD detection to rank LLM serving configurations on mixed GPUs, reporting 6.2% MAPE throughput prediction and 95% top-1 accuracy on 460 benchmark runs while abstaining on unsupported cases.
Effective LLM inference cost per million output tokens varies 2.5-36x with offered request rate due to utilization, addressed by a concurrency-aware measurement methodology and open-source vLLM tool validated across model types.
SpectrumKV applies per-token mixed-precision KV cache transfer (FP16/INT8/INT4) with a model-specific probe for INT4 tolerance, achieving better perplexity and retrieval than PDTrim at equivalent budgets on Qwen2.5-7B, Mistral-7B, and Gemma-2-9B.
Vortex provides a programmable frontend and backend for sparse attention in LLM serving, delivering up to 3.46x throughput over full attention while preserving accuracy.
MORI improves throughput 20-71% and TTFT 18-43% over baselines by ranking programs on a continuous idleness spectrum and shifting the GPU-CPU boundary to match capacity in agentic LLM serving.
Prefill-only adaptation of LLMs yields 1.9x higher throughput for 512 adapters on Llama 3.1 70B with near-parity performance on RL tasks and recoverable loss on SFT.
Salca is a new ASIC accelerator that achieves 3.82× speedup and 74.19× energy efficiency over A100 for long-context attention via dual-compression dynamic sparse attention and pipelined hardware.
MemExplorer optimizes heterogeneous memory systems for agentic LLM inference on NPUs and reports up to 2.3x higher energy efficiency than baselines under fixed power budgets.
Tree Training serializes tree trajectories via DFS and uses redundancy-free partitioning to compute weighted per-token losses exactly once per token, achieving up to 6.2x training speedup on dense and MoE models.
TokenCake introduces agent-aware temporal and spatial schedulers for KV cache management in LLM multi-agent serving, claiming over 47% lower end-to-end latency and up to 16.9% better GPU memory utilization than vLLM on representative benchmarks.
Sandwich delivers 2.01x average end-to-end speedup and up to 3.4x latency reduction for CPU LLM serving via phase-wise hot-switching, TopoTree hardware abstraction, and fast-start dynamic kernel generation.
BatchLLM achieves 1.3x-10.8x higher throughput than vLLM and SGLang for batched LLM inference with prefix sharing via global prefix identification, decoding-first reordering, and memory-centric token batching.
RetrievalAttention approximates full attention in long-context LLMs by retrieving relevant KV vectors from CPU-based ANNS indexes with an attention-aware algorithm, achieving near-full accuracy while accessing only 1-3% of the data.
Omni-Flow introduces a three-layer abstraction (Control Flow, Data Flow, Compute Flow) for unified orchestration and KV cache sharing in multimodal inference pipelines.
Organizes the heterogeneous LLM prefill-decode design space along four axes and extracts three boundary decisions with guidance on precision, KV representation, and ownership.
SCD replaces raw KV cache transmission with compact semantic codes via reuse and patching to achieve up to 2.65x TTFT speedup while staying within 5% F1 of oracle quality.
Measurement study finds LLM serving systems sacrifice 60-93% throughput to meet human-centric TTFT/TPOT SLOs unnecessary for programmatic long-horizon tasks.
A unified KV cache system with architecture-specific sizing, six-tier memory from GPU to filesystems, and Bayesian prediction delivers 7.4x higher batch sizes, 70-84% hit rates, and projected 1.7-2.9x throughput gains.
JoyAI-LLM Flash delivers a 48B MoE LLM with 2.7B active parameters per token via FiberPO RL and dense multi-token prediction, released with checkpoints on Hugging Face.
The Workload-Router-Pool architecture is a 3D framework for LLM inference optimization that synthesizes prior vLLM work into a 3x3 interaction matrix and proposes 21 research directions at the intersections.
HFX jointly designs scheduling and scaling for multi-SLO LLM serving, achieving up to 4.44x higher SLO attainment, 65.82% lower latency, and 49.81% lower cost than prior systems on multi-task workloads.
citing papers explorer
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Salca: A Sparsity-Aware Hardware Accelerator for Efficient Long-Context Attention Decoding
Salca is a new ASIC accelerator that achieves 3.82× speedup and 74.19× energy efficiency over A100 for long-context attention via dual-compression dynamic sparse attention and pipelined hardware.
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MemExplorer: Navigating the Heterogeneous Memory Design Space for Agentic Inference NPUs
MemExplorer optimizes heterogeneous memory systems for agentic LLM inference on NPUs and reports up to 2.3x higher energy efficiency than baselines under fixed power budgets.
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The Workload-Router-Pool Architecture for LLM Inference Optimization: A Vision Paper from the vLLM Semantic Router Project
The Workload-Router-Pool architecture is a 3D framework for LLM inference optimization that synthesizes prior vLLM work into a 3x3 interaction matrix and proposes 21 research directions at the intersections.