AI Inference, MoE Routing, and KV Cache Offloading

Unlock deterministic, microsecond-latency inference at scale with kernel-bypass architecture.

MoE Weight Streaming

Frontier models utilize a Mixture of Experts (MoE) architecture, where only a sparse subset of expert parameters is activated for any given token. Because these models are too massive to reside entirely in VRAM, inactive experts are frequently offloaded to NVMe storage. When a token routing decision triggers a prefetch miss, fetching the required expert across the PCIe interconnect via standard kernel I/O incurs severe latency spikes. These PCIe bottleneck stalls cripple token generation speeds. Bypassing the kernel allows inference engines to stream expert weights directly into GPU memory via user-space DMA, preserving interactive latencies without sacrificing model accuracy.

KV Cache Swapping

For long-lived chat sessions or multi-turn agentic workflows, inference servers must evict the Key-Value (KV) cache of inactive users to disk. When a user returns, recalculating a large context from scratch (the prefill phase) consumes heavy GPU compute and takes significant time. Swapping that cache directly from NVMe back to VRAM via PCIe is exponentially faster. A zero-copy data plane allows inference providers to maximize highly concurrent KV cache offloading without burning CPU cycles, dramatically lowering the cost-per-token by increasing the number of users multiplexed onto a single GPU.