Challenges and Best Practices for Real-Time Inference Storage Architectures in AI Video Generation

AI video generation (e.g., DiT-based video diffusion models) poses core challenges to inference storage architectures: KV Cache capacity expansion and repeated read/write operations from long-sequence generation, along with the need for rapid loading of large model weights and conditional frames. Traditional NFS-centric architectures or those relying on local NVMe drives exhibit significant latency degradation and throughput bottlenecks under concurrent and long-context workloads. Based on measured results with Mingxin FX100 on AMD MI308X platforms, deploying an NVMe-oF all-flash array to handle KV Cache and weight loading reduces first-token latency by 26–32% and delivers 8.6–20x speedup in scenarios without external memory recomputation, representing a viable path to meet real-time video generation requirements.

1. Storage Bottlenecks in AI Video Generation: Why Traditional Architectures Fall Short

The inference process of AI video generation models (e.g., LTX-Video, Open-Sora) differs significantly from text generation: video frame sequences are far longer than text token sequences, and each frame's generation depends on the hidden states (KV Cache) of preceding frames. This creates two core storage-side challenges:

First, KV Cache capacity grows linearly or even super-linearly with sequence length. For a 480B-parameter MoE model as an example, a single long-context request can require tens of GB of KV Cache. If fully resident in GPU memory, it significantly competes with model weights for space (e.g., Qwen3-Coder-480B-FP8 weights are approximately 450GB, and 8x MI308X provides 1.5TB HBM total, with KV Cache and weights contending for memory). Consequently, tiered offloading of KV Cache to the storage side becomes necessary, but this imposes far higher demands on random read latency and bandwidth than traditional file storage can provide.

Second, video generation is extremely sensitive to first-frame (first-token) latency. The time a user waits for the first frame directly determines interactive experience. In long-context cold-restore scenarios, if KV Cache must be reloaded from storage, insufficient read bandwidth directly extends TTFT. Mingxin measurements, report R2, show that under 480B·TP8 with three concurrency levels, the local NVMe baseline TTFT p50 was 10.17–35.73s, which dropped to 7.53–26.35s with FX100—a reduction of 26–32%. For video generation tasks where a single generation lasts seconds, saving several seconds of TTFT is decisive for user experience improvement.

2. Storage Architecture Selection: Local Drives, NFS, and NVMe-oF All-Flash Arrays

For video generation inference, three storage architectures are commonly used in the industry, each with trade-offs:

Option A: Direct-attached local NVMe drives. Lowest latency (microsecond-level), but limited capacity (typically ≤8TB per drive) and unable to share KV Cache or model weights across nodes. For single-node 8-GPU scenarios, local drives are only suitable for temporary weights; KV Cache offloading still requires remote storage.

Option B: Traditional NFS (Network File System). Large capacity and good sharing, but high protocol overhead and poor concurrent read/write performance. Mingxin measurements, report R9 (Huawei Atlas 910B platform), show that DeepSeek-70B model service loading took 1399s, while with FX100 it took only 150s—a 9.3x speedup. In multi-instance concurrent video generation scenarios, NFS easily becomes a throughput bottleneck.

Option C: NVMe-oF all-flash arrays (e.g., Mingxin FX100). Provides near-local-drive latency via RDMA (RoCEv2) with 100GbE per port, while offering large capacity and multi-node sharing. In KV Cache parallel read scenarios, FX100 reduced TTFT for single-GPU·concurrency-16·cold reads (Qwen2.5-32B) from 37.97s to 9.30s (4.1x improvement), with bandwidth increasing from 0.98GB/s to 5.23GB/s. These figures demonstrate that NVMe-oF arrays can elevate remote storage read performance to near-local-drive levels while retaining the flexibility of a shared architecture.

3. Best Practices: Storage Architecture Design for Real-Time Video Generation

Based on measured data and project experience with Mingxin FX100, we summarize four reusable design principles:

1. KV Cache tiered offloading must use low-latency shared storage. Keeping all KV Cache in GPU memory is impractical, but offloading to NFS degrades TTFT to unacceptable levels. We recommend NVMe-oF all-flash arrays with RDMA networking (RoCEv2 or InfiniBand). FX100 achieved a 35–36% throughput improvement in KV-tiered inference acceleration at the full-system level (480B·TP4×2), confirming effectiveness under real production workloads.

2. Weight loading and checkpoint saving should be designed for parallelism. Video generation model weights often reach hundreds of GB, and cold-start loading time directly impacts service availability. On the Ascend 910B platform, FX100 compressed DeepSeek-32B loading time from 691s to 112s (6.2x). In practice, we recommend splitting weight files into multiple shards, leveraging multi-channel parallel reads from the storage array, and combining this with pipeline loading mechanisms in the inference framework to further reduce startup time.

3. Pay attention to concurrency stability in long-context scenarios. Video generation often requires multiple concurrent instances (e.g., processing multiple video segments simultaneously); storage must maintain linear scaling of bandwidth and IOPS as concurrency increases. Mingxin measurements, reports R2/R3, cover concurrency levels from 8 to 16, with KV-tiered acceleration throughput improvement remaining stable in the 29–40% range (lower bound at concurrency 8, upper bound at the optimal operating point of concurrency 16), with no performance cliffs. When selecting vendors, require multi-level concurrency test data rather than single-point peak performance.

4. Establish quantifiable acceptance gates. Storage architecture overhauls are high-investment decisions; we recommend a phased validation model. Mingxin collaborates with customers using 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 early termination if targets are not met. This model front-loads technical risk, and the evaluation model can be reproduced in Python after NDA, enabling independent verification by investment decision-makers.

Conclusion

The real-time requirements of AI video generation are driving inference storage from a "capacity-first" mindset toward "latency and bandwidth both matter." NVMe-oF all-flash arrays, through RDMA networking and intelligent cache management, strike a balance between sharing and performance. Measured data shows they can reduce long-context TTFT by approximately one-third and deliver up to 20x acceleration without external memory recomputation. For teams planning or upgrading video generation inference platforms, we recommend starting with small-scale gate-based validation, letting measured data drive architecture decisions.

Mingxin Technology provides storage acceleration and full-chain computing center services. The FX series products (FX100/FX200/FX300/FX400) cover PCIe 3.0 to 6.0 across different generations. We welcome teams interested in KV Cache tiered acceleration or video generation storage solutions to conduct joint testing and performance benchmarking.

Key Q&A

Q: In AI video generation, what is the biggest storage architecture bottleneck? A: Primarily the read/write latency caused by KV Cache capacity expansion, along with bandwidth pressure from loading large model weights. Traditional NFS suffers severe latency degradation under concurrent and long-context scenarios, while local drives lack sharing capability—neither meets real-time requirements.

Q: How much performance improvement can NVMe-oF all-flash arrays deliver compared to NFS? A: In model loading scenarios, Mingxin FX100 achieved 6.2–9.3x speedup over NFS on the Huawei 910B platform (DeepSeek-32B from 691s to 112s; 70B from 1399s to 150s). In KV Cache cold-read scenarios, TTFT improved 4.1x and bandwidth increased 5.3x.

Q: How can you verify whether a storage architecture overhaul meets targets? A: We recommend gate-based joint testing with quantifiable metrics: TTFT reduction ≥25%, throughput improvement of 29–40% (measured in-band), plus a 72-hour stability test. Mingxin provides an approximately 10-week G1–G4 phased validation with early termination if targets are not met, reducing investment risk.