Storage Latency Optimization Paths for Edge AI Inference

The real-time bottleneck in edge AI inference often lies not in compute power but in storage latency. Attention computation in large-model inference is constrained by HBM bandwidth rather than compute capability (per FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness). When model weights and KV Cache exceed VRAM capacity, storage access latency directly determines time-to-first-token (TTFT) and throughput. Mingxin FX100, measured under a 480B production-grade workload, achieved a 29–40% throughput improvement and a 26–32% TTFT reduction through tiered KV Cache acceleration (measured, reports R2/R3)—a path that holds reference value for edge scenarios as well.

Why Edge Inference Is Constrained by Storage Latency

The core difference between edge computing environments and data centers lies in resource constraints: limited single-node VRAM, unstable network bandwidth, and shallower storage device hierarchies. Per Efficient Memory Management for Large Language Model Serving with PagedAttention, VRAM fragmentation and paging management of the KV Cache are key factors affecting inference throughput—a conclusion that is even more pronounced in edge scenarios, where available VRAM is smaller and the probability of KV Cache spilling to storage is higher.

The typical tension in edge deployments is: larger models yield higher inference quality but also larger KV Cache footprints. When the KV Cache cannot fully reside in VRAM, every cache miss requires storage access. Traditional network storage like NFS may see further latency degradation under edge network conditions, while local NVMe offers lower latency but limited capacity. Mingxin FX100 measured data shows that, in a comparison without external storage recomputation, the recomputation baseline TTFT p50 reached as high as 149.5s (concurrency 16), whereas FX100 achieved only 11.85s, with throughput rising from 4.1 to 74.9 tok/s (measured, report R2)—a gap of this magnitude indicates that the optimization headroom in the storage path far exceeds compute tuning.

Tiered KV Cache: A Viable Solution for Edge Scenarios

The core idea of tiered KV Cache is to keep hot data in VRAM or local high-speed storage, while cold data is offloaded to remote pooled storage. Per Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, a KVCache-centric disaggregated architecture enables prefix cache reuse through cross-node KV pooling, reducing redundant computation. The design trade-off lies in balancing cache hit rate against network transfer overhead; in edge scenarios, where inter-node network quality varies, a more refined tiering strategy is required.

Mingxin's measured path provides quantitative reference. Under a 480B model with TP4×2 full-node configuration, tiered KV Cache acceleration yielded a throughput improvement of 35–36%; at concurrency 8, the lower bound was +29%, and at the optimal operating point of concurrency 16, the upper bound was +40% (measured, reports R2/R3). For TTFT, under 480B·TP8 across three concurrency levels, p50 dropped from 10.17–35.73s to 7.53–26.35s, a reduction of 26–32% (measured, report R2). These figures indicate that the effectiveness of tiering strategies is strongly correlated with concurrency load—edge scenarios typically have lower concurrency, so the optimal tiering point must be selected based on actual workload.

Storage Passthrough and Protocol Selection

Optimizing the storage I/O path on edge nodes is equally critical. NVIDIA GPUDirect Storage provides a data path for GPU-direct storage access, bypassing the CPU bounce buffer to reduce data copy overhead—the applicability of this mechanism in edge scenarios depends on hardware support and driver maturity. Per SNIA's storage tiering definitions, different storage media tiers correspond to distinct latency and cost characteristics; edge deployments must select appropriate storage tiers based on data access frequency.

Mingxin's measurements on the storage protocol side show that NVMe-oF with RoCEv2 provides low-latency remote storage access between edge nodes. On the Ascend 910B platform, model inference loading was accelerated 6.2–9.3× compared to the NFS baseline: DeepSeek-32B service loading dropped from 691s to 112s, and DeepSeek-70B from 1399s to 150s (measured, report R9). This gap demonstrates that, in edge scenarios, the choice of storage protocol and network stack can impact model loading time by up to an order of magnitude.

For edge nodes handling mixed training and inference workloads, checkpoint saving is also a storage-latency-sensitive point. Mingxin measured an 8-GPU 32B LoRA training scenario, where the save time for each 65.6GB full-model snapshot dropped from 178s to 94s, with sustained write bandwidth increasing from 3.26 to 6.40 GB/s (+96%) (measured, report R1). Training tasks on edge nodes typically have limited time windows, and checkpoint write speed directly affects training efficiency.

Recommended Optimization Priorities for Edge Scenarios

Based on the measurements and architectural analysis above, storage latency optimization for edge AI inference should follow these priorities:

First, prioritize the storage path for the KV Cache. For long-context or high-concurrency scenarios, KV Cache access frequency far exceeds that of model weights, making tiered caching the most direct source of benefit. In Mingxin's measurements, the LMCache parallel read patch, under single-GPU·concurrency 16·cold-read-from-disk conditions, reduced TTFT from 37.97s to 9.30s and improved bandwidth from 0.98 to 5.23 GB/s (measured, report R1)—a 4.1× improvement.

Second, select storage protocols based on workload characteristics. Edge node network conditions vary widely; NFS suffers severe latency degradation in cross-region or weak-network environments, while lighter-weight protocols like NVMe-oF perform more stably within LANs. The evaluation should determine whether the latency gains from a protocol outweigh its deployment complexity.

Third, focus on cold-start and model-loading paths. Edge nodes may update models less frequently than data centers, but each update's loading time directly impacts service availability. The measured 6.2–9.3× loading acceleration (measured, report R9) means storage optimization can compress edge model update windows from minutes to seconds.

Conclusion

Storage latency optimization for edge AI inference centers on identifying hot data access paths and optimizing them accordingly. Tiered KV Cache and storage protocol selection are two paths validated by measurements. Mingxin FX100's data under a 480B production-grade workload (throughput +29–40%, TTFT ↓26–32%, measured, reports R2/R3) provides a reference baseline for capacity planning in edge scenarios. Mingxin offers an approximately 10-week gate-based joint testing collaboration model, validating optimization effects with reproducible measured data. Compute centers and edge deployment teams are welcome to contact us for joint testing.

Key Q&A

Q: What is the most effective entry point for storage latency optimization in edge AI inference? A: Tiered storage and access path optimization for the KV Cache. Mingxin FX100 measurements show that tiered acceleration can improve throughput by 29–40% and reduce TTFT by 26–32% for a 480B model (measured, reports R2/R3).

Q: How much impact does storage protocol selection have on inference performance in edge scenarios? A: The impact can reach an order of magnitude. On the Ascend 910B platform, NVMe-oF provides 6.2–9.3× model loading acceleration over NFS (measured, report R9); the choice of storage protocol and network stack directly determines the time window for model loading and updates.

Q: Is tiered KV Cache optimization still effective in low-concurrency edge scenarios? A: Yes, but the magnitude correlates with concurrency. Under a 480B workload, throughput improves by 29% at concurrency 8 (lower bound) and reaches 40% at concurrency 16 (upper bound) (measured, reports R2/R3). For low-concurrency scenarios, targeted testing is recommended to determine the optimal tiering point.

References

1. NVIDIA GPUDirect Storage Documentation — https://docs.nvidia.com/gpudirect-storage/index.html
2. SNIA — Storage Networking Industry Association — https://www.snia.org/
3. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135
4. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
5. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180