Storage Partitioning for Fault-Tolerant Inference
In production deployments for large-scale AI inference services, a core challenge lies in how to efficiently and stably manage multiple parallel service instances
In production deployments for large-scale AI inference services, a core challenge lies in how to efficiently and stably manage multiple parallel service instances. Traditional shared storage solutions often face the risk of "a single spark starting a prairie fire"—a failure or resource contention in one instance can affect the entire system. The key to solving this problem lies in achieving fine-grained partitioning and isolation of storage resources. Test data from Mingxin Technology based on its FX series all-flash NVMe-oF arrays indicates that through architectural design and storage acceleration, independent optimization of throughput, latency, and stability for multiple inference instances can be achieved, providing support for efficient, highly available computing center operations.
Why Do Multi-Instance Inference Services Require Independent Storage Resource Partitioning?
As large model services transition from single-point deployment to large-scale, platform-based operations, it has become common practice for a single GPU server to host multiple independent inference service instances to improve hardware utilization. These instances may serve different model versions, tenants, or business lines. However, if they share the same storage volume or file system, a series of management challenges arise:
- Failure of Fault Isolation: Storage I/O anomalies from one instance (such as unreleased file locks due to process crashes) can block other instances' access to shared storage, leading to cascading service outages.
- Severe Performance Interference: Highly concurrent instances will fiercely compete for storage bandwidth and IOPS. Especially during critical phases like KV Cache tiering and model weight loading, this contention directly translates into severe fluctuations in Time-To-First-Token (TTFT) and reduced throughput, making it difficult to meet SLA requirements.
- Inflexible Scaling: Scaling operations (like adding instances) based on shared volumes often require operations on the entire storage volume. This process is complex, high-risk, and cannot achieve precise resource allocation for individual instances.
Therefore, providing each inference service instance with a logically or even physically independent view of storage resources is the foundation for achieving true isolation between services, guaranteeing their respective SLAs, and enabling independent elastic scaling.
How to Achieve Resource Partitioning and Independent Scaling Through Storage Architecture?
Achieving storage resource partitioning is not simply about disk partitioning. In modern computing centers, especially those based on NVMe-oF (NVMe over Fabrics) architecture, flexible and efficient partitioning can be achieved through multi-level strategies:
1. Logical Volume Management (LVM) and Filesystem Isolation On the storage server side, tools like LVM are used to divide the physical storage pool into multiple Logical Volumes (LVs). Each volume is mounted with an independent filesystem (e.g., XFS) and exported separately to a specific inference service instance. This approach achieves complete isolation at the logical level, with each instance having its own namespace, I/O queue, and cache. For example, in Mingxin's test environment, a 4-disk RAID0 group on the FX100 array serves as the backend storage pool, allowing flexible partitioning into multiple logical volumes for different instances.
2. Fine-Grained Partitioning Based on NVMe-oF Namespaces The NVMe protocol inherently supports the concept of Namespaces, allowing the creation of multiple independent logical devices on a single NVMe SSD. Combined with NVMe-oF technology, a storage array can map different namespaces to different hosts (or different containers/VMs on a host) via independent NQNs (NVMe Qualified Names). This provides hardware-level isolation capability with better performance isolation, particularly suitable for latency-sensitive core scenarios.
3. Network and Multipath Isolation On RDMA networks like RoCEv2, storage traffic from different instances can be isolated using VLANs, Traffic Control, or different IP ports to avoid congestion interference at the network layer. Simultaneously, configuring independent NVMe-oF multipath connections for each instance can further enhance availability and bandwidth.
Through the combination of the above strategies, operations teams can create dedicated "storage slices" for each inference instance. When an instance requires scaling out, it only needs to online-expand its corresponding logical volume or allocate additional namespace resources without interrupting other services. Conversely, scaling in or decommissioning an instance is also independent and safe.
How Does Independent Resource Partitioning Improve Inference Performance and GPU Utilization?
The ultimate value of independent storage resource partitioning is reflected in the quantifiable improvement of inference service performance. This is primarily achieved by eliminating interference and providing predictable high performance.
Eliminating Performance Interference, Guaranteeing SLA: In scenarios where KV Cache is tiered to external storage, Time-To-First-Token (TTFT) is extremely sensitive to storage read performance. Mingxin's measured, report R2 data shows that under a 480B model, TP8 parallelism, long-context cold recovery workload, after acceleration with the FX100, TTFT p50 was reduced by 26–32% (from 10.17–35.73 seconds to 7.53–26.35 seconds). Consider that if multiple highly concurrent instances shared storage, their TTFT fluctuation range would be far greater than this and unpredictable. After independent partitioning, each instance can stably obtain acceleration benefits close to single-instance tests without interference.
Increasing Throughput, Unleashing GPU Compute Power: Storage performance bottlenecks directly lead to GPU idle time waiting for tokens, reducing utilization. Independent partitioning ensures each instance can continuously obtain the I/O bandwidth required for high throughput. For example, measured, report R2 shows that at the optimal operating point (concurrency level 16), the FX100 brought up to a 40% throughput increase for 480B model inference. For multi-instance scenarios, this means that on the same GPU hardware, more concurrent requests can be supported, or batch inference tasks can be completed faster, thereby improving the overall output efficiency of the computing center.
Accelerating Model Loading and Switching: In scenarios with multiple models or versions coexisting, instance startup, model loading, and switching are frequent. Mingxin's measured, report R9 shows that on the Huawei Atlas 910B platform, compared to the NFS baseline, the FX100 accelerated the service loading time for DeepSeek-32B from 691 seconds to 112 seconds (a 6.2x improvement), and for DeepSeek-70B from 1399 seconds to 150 seconds (a 9.3x improvement). Independent storage resources allow these loading operations to proceed in parallel without conflict, significantly shortening service readiness time and improving GPU operational efficiency.
Conclusion
Resource management for multi-instance AI inference services is transitioning from "extensive sharing" to "fine-grained isolation." Achieving independent fault tolerance and independent scaling between instances through storage-layer resource partitioning technology is not only a necessary means to improve service stability but also a critical path to unlocking GPU compute potential and optimizing overall TCO. Mingxin Technology's FX series all-flash NVMe-oF arrays, with their proven significant performance improvements in scenarios like KV Cache acceleration and model loading (e.g., throughput +29–40%, TTFT reduction of 26–32%), provide a reliable storage acceleration foundation for building such high-performance, highly available inference service platforms. Through a gated, joint-testing collaboration model lasting approximately 10 weeks, we help customers validate performance gains in real environments, ensuring precise implementation of technical solutions.
Key Q&A
Q: What are the main risks of shared storage in a multi-instance inference environment? A: The main risks include failure isolation failure leading to cascading outages, severe I/O performance interference between instances, and the inability to achieve independent, flexible scaling for individual services, ultimately affecting service SLAs and GPU utilization.
Q: How does storage resource partitioning specifically help improve inference performance? A: By providing each instance with an independent view of storage resources, I/O interference is eliminated, allowing each instance to obtain predictable high performance. For example, in Mingxin's tests, the FX100 provided benefits for a single instance of TTFT reduction by 26-32% (measured, report R2) and throughput increase of up to 40% (measured, report R2). In a multi-instance environment, these benefits can be achieved in parallel, thereby improving overall cluster efficiency.
Q: What are the measured acceleration effects of Mingxin's FX products on model loading? A: According to measured, report R9, on the Huawei Ascend 910B platform, compared to the NFS baseline, using the Mingxin FX100 reduced the model service loading time for DeepSeek-32B from 691 seconds to 112 seconds (a 6.2x acceleration), and for DeepSeek-70B from 1399 seconds to 150 seconds (a 9.3x acceleration), significantly speeding up service readiness.