AI inference is shifting from cloud to edge, but virtualization overhead rarely gets the attention it deserves. We benchmarked LLM inference under KVM, Firecracker, and bare metal-and found that virtualization-induced latency jitter can hit 20–40%, with GPU passthrough failing to fully fix the mess. Our hunch: the scheduling interrupts of virtualization clash with the compute patterns of LLM inference, amplifying memory-switching delays. We're now tinkering with a "virtualization-aware" scheduling strategy that decouples inference tasks from those interrupts, and we've squeezed out a 15% improvement in tail latency so far. Curious if anyone else working at the edge-AI-virtualization intersection has real-world numbers to share. Full dataset on OSF: https://osf.io/qgxwu

Interesting results. A 20–40% virtualization penalty for LLM inference is significant, especially as AI workloads move toward virtualized edge environments. I'd be particularly interested in seeing how much of that gap remains with CPU pinning, NUMA-aware placement, huge pages, and GPU passthrough properly optimized. The virtualization layer may not be the problem by itself. The configuration around it matters just as much.

谢谢，你这个方向正好切中我们在做的第二轮实验。我们目前的测试在CPU钉住和大页面上确实没有做深入优化，默认用的是KVM的默认配置。你提到的NUMA感知位置是个好方向，我们在多socket服务器上测试时，跨NUMA节点的延迟波动确实比单socket高出不少。关于GPU直通，我们的测试结果和你说的差不多--直通能解决吞吐量问题，但对尾延迟影响有限。我们正在尝试用SR-IOV + 虚拟化感知调度的组合方案，初步效果比纯直通好一些，但还在验证阶段。方便问一下你是在什么硬件环境下测的？我们目前的测试平台是两颗Intel 8375C + 100GbE，想看看不同硬件下这个20-40%的波动区间是不是普遍现象。