Discrete-event simulation benchmark for LLM request cancellation handling, measuring wasted compute, KV residency overhead, and survivor latency recovery across polling and push-based strategies.
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Updated
Jul 25, 2026 - Python
Discrete-event simulation benchmark for LLM request cancellation handling, measuring wasted compute, KV residency overhead, and survivor latency recovery across polling and push-based strategies.
Exploratory AI infrastructure project modeling semantic KV-cache orchestration, memory tiering, and HBM/CXL movement tradeoffs for long-context LLM inference.
Discrete-event simulation benchmark for graceful degradation strategies in LLM serving: output capping, CoT disabling, model downgrade, and selective drop under progressive pressure.
Discrete-event simulation benchmark for decode preemption policies in LLM serving: when to interrupt a running decode to admit an urgent prefill, and whether checkpoint or recompute is cheaper.
KV-cache warming strategies for LLM serving: top-k adaptive, reserve-headroom, replay, and progressive warming under cold-start after scale-up.
Monte Carlo simulation benchmark for MoE expert activation memory variability and admission control under non-deterministic routing — including routing skew amplification, top_k sweep, and policy comparison across uniform, skewed, and very_skewed distributions
Simulation benchmark for LLM serving observability design: which minimum set of signals detects all major failure modes before SLO violation, and what does each signal miss?
Discrete-event simulation for LLM serving capacity planning: how many GPUs for a p99 TTFT SLO under ShareGPT traffic, batching fragmentation, and autoscaling lag — with cliff analysis and three levels of planning rigor.
Analytical benchmark for sliding window attention KV cache management: quality vs window size tradeoffs, SWA vs eviction comparison, prefix sharing interaction, and operational window recommendations across four attention distributions
Simulation benchmark for canary deployment strategies in LLM model updates, measuring safety, rollback behavior, bad exposure, and rollout speed across good, bad, and marginal candidate scenarios
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