AI Inference Storage Acceleration: Selection and Configuration Optimization Guide

Core Findings

The key to selecting AI inference storage acceleration technology lies in identifying the memory access patterns of inference workloads: in long-context scenarios, KV Cache read latency directly determines time-to-first-token (TTFT), while throughput bottlenecks often stem not from compute power but from HBM bandwidth and storage data paths. According to Mingxin's measured results, report R2, under a 480B model long-context cold-restore workload, tiered KV acceleration improved inference throughput by 29–40% and reduced TTFT by 26–32%. When selecting a solution, prioritize validating the actual benefit of the storage solution on the KV Cache read path, rather than merely comparing peak bandwidth.

Why KV Cache Is the Primary Target for Inference Storage Optimization

The memory access bottleneck in large-model inference is fundamentally a data movement problem. According to the analysis in FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness, attention computation is limited by HBM bandwidth rather than compute power, and the gains from IO-aware optimization far exceed those from compute optimization. This conclusion holds equally in KV Cache scenarios: during long-context inference, KV Cache size can reach hundreds of GB, far exceeding the memory capacity of a single GPU, necessitating external or tiered storage.

According to Efficient Memory Management for Large Language Model Serving with PagedAttention, paged management of KV Cache addresses GPU memory fragmentation but does not eliminate external storage access latency. When KV Cache must be read from the storage system, storage latency is directly added to TTFT. Mingxin's measured results, report R2, show that under a 480B·TP8 workload with three concurrency levels, TTFT p50 dropped from 10.17–35.73s to 7.53–26.35s, a reduction of 26–32%—this is the direct benefit of storage acceleration.

Technology Selection: Three Key Evaluation Dimensions

1. Storage Protocol and Data Path

According to NVIDIA GPUDirect Storage documentation, GPU-direct storage bypasses the CPU bounce buffer, shortening the data path. When selecting a solution, verify whether it supports RDMA (e.g., RoCEv2) and GPU-direct access. Mingxin's FX100 all-flash NVMe-oF array uses a RoCEv2 single-port 100GbE configuration, achieving the benefits described above in R2 testing. If the solution only supports traditional TCP/IP paths, latency will increase significantly.

2. Verifiability of Acceleration Benefits

Selection should not rely solely on vendor-claimed bandwidth numbers; require reproducible measured test protocols. Mingxin's collaboration model offers an approximately 10-week gate-based joint testing process (G1 arrival acceptance / G2 single-node baseline / G3 main gate: TTFT reduction ≥25%, throughput +29–40% measured in-band / G4 72-hour stability), with stop-loss if targets are not met. This verification approach is more reliable than a single benchmark number—according to the MLPerf Inference Datacenter Benchmark Suite definition, inference performance must be compared under unified conditions; otherwise, numbers are not comparable.

3. Compatibility with Existing Software Stacks

The storage acceleration solution must integrate deeply with inference frameworks (e.g., vLLM) and cache management libraries (e.g., LMCache). Mingxin's measured results, report R1, show that the LMCache parallel-read patch combined with FX100, under a single-GPU, concurrency-16, cold-read scenario, reduced TTFT from 37.97s to 9.30s and increased bandwidth from 0.98 to 5.23 GB/s (↑5.3×). If the solution cannot adapt to mainstream frameworks' cache management interfaces, the actual benefit will be significantly diminished.

Configuration Optimization: Deriving Configuration Points from Measured Data

Configuration optimization note: storage acceleration is not effective in all scenarios. According to the analysis in Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, a KVCache-centric disaggregated compute-storage architecture suits prefix cache reuse and cross-node KV pooling, but if the workload's KV reuse rate is low, acceleration benefits will narrow. It is recommended to run small-scale validation with your own workload before deployment.

Selection Checklist: Five Essential Questions

1. Is the TTFT reduction quantifiable? Require measured data matching your model size and concurrency levels, not just peak bandwidth figures.
2. Does the data path support RDMA and GPU-direct access? Per NVIDIA GPUDirect Storage documentation, this is a prerequisite for bypassing the CPU bottleneck.
3. How mature is the integration with vLLM/LMCache? Reference the 4.1× TTFT improvement from the LMCache parallel-read patch in R1 measured results.
4. Is there a gate-based acceptance mechanism? A joint testing protocol with stop-loss on failure is more reliable than post-hoc benchmarks.
5. Does it cover training scenarios? Per R1 measured results, 8-GPU 32B LoRA training checkpoint saving dropped from 178s to 94s (1.9×); if training and inference share storage, validate both.

Conclusion

The essence of AI inference storage acceleration selection is verifying whether "storage latency truly enters the inference critical path." It is recommended to use TTFT reduction ≥25% and throughput improvement of 29–40% as quantitative acceptance thresholds, and require reproducibility under your own workload. Mingxin offers all-flash NVMe-oF array products such as the FX100, supporting gate-based joint testing and Python-reproducible measurement models. Teams with long-context inference or training acceleration needs are welcome to contact us for hands-on testing.

Key Q&A from This Article

Q: Which metric should be the primary focus when selecting AI inference storage acceleration? A: TTFT (time-to-first-token) reduction and throughput improvement, not peak bandwidth. Mingxin's measured results, report R2, show that under a 480B long-context workload, tiered KV acceleration achieves TTFT reduction of 26–32% and throughput improvement of 29–40%.

Q: How can the real benefit of a storage acceleration solution be verified? A: Require gate-based joint testing, set quantitative acceptance thresholds of TTFT reduction ≥25% and throughput +29–40%, and ensure reproducibility under your own model and concurrency levels. Reference Mingxin's approximately 10-week G1–G4 joint testing protocol.

Q: Is storage acceleration effective for all inference workloads? A: No. Long-context scenarios with high KV Cache reuse rates see significant benefits; short-context or low KV-reuse workloads see limited gains. According to the Mooncake analysis, disaggregated compute-storage architectures depend on prefix cache reuse—run small-scale validation with your own workload before selection.

References

1. MLPerf Inference: Datacenter Benchmark Suite Results — https://mlcommons.org/benchmarks/inference-datacenter/
2. NVIDIA GPUDirect Storage Documentation — https://docs.nvidia.com/gpudirect-storage/index.html
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