Efficiency Before Expansion: A Practical Checklist for Unlocking the Potential of Existing GPU Clusters
In the current wave of computing power infrastructure development, a growing consensus is clear: before investing heavily in new GPU procurement, prioritizing the potential of existing GPU clusters is a more cost-effective and technically rational choice. Simply stacking hardware cannot solve the problem of computing power "idling" caused by storage I/O bottlenecks, inefficient software scheduling, and poorly designed data flows. This article will focus on three core aspects—data flow, software stack, and hardware co-design—to provide technical decision-makers with a practical checklist for unlocking the efficiency of existing GPU clusters.
How to Break Through Data Flow Bottlenecks and Unleash True GPU Computing Power?
The high computing power of GPUs requires continuous, high-speed data "feeding." In large model inference and training scenarios, data flow bottlenecks often become the biggest constraint limiting GPU utilization. This is mainly reflected in two aspects: model weight loading and the access of intermediate states (such as KV Cache).
Traditional storage solutions based on Network File Systems (NFS) or local low-speed NVMe drives can be surprisingly time-consuming when handling the loading of models that are hundreds of GBs in size. According to the measured, report R9, on the Huawei Atlas 910B platform, the time to load the DeepSeek-70B model was reduced from the NFS baseline of 1399 seconds to 150 seconds after using a high-speed NVMe-oF array, achieving an acceleration ratio of 9.3x. This means the wait time for service deployment or restart is reduced from over 23 minutes to 2.5 minutes, significantly improving cluster agility and service availability.
More critical is the management of KV Cache during inference. For long-context tasks, the KV Cache volume is huge, far exceeding GPU memory capacity, requiring frequent swapping with external storage. Measured, report R2 data shows that under a demanding workload with a 480B parameter model and TP8 tensor parallelism, using a dedicated high-speed storage acceleration solution can reduce First Token Latency (TTFT) by 26% to 32% and improve inference throughput by 29% to 40%. Compared to the extreme case without external storage recomputation, the acceleration effect is even more significant: TTFT decreased from 149.5 seconds to 11.85 seconds, an acceleration ratio of 12.6x; throughput increased from 4.1 tok/s to 74.9 tok/s, an improvement of over 18x. These data indicate that optimizing storage data flow is one of the most effective means to directly unlock GPU computing power constrained by I/O and improve overall cluster throughput.
How to Maximize Hardware Value Through Software Stack Optimization?
Hardware potential needs to be fully unleashed through the software stack. Software-level optimization mainly revolves around scheduling strategies, caching mechanisms, and computational graph optimization.
First, there's Batching and Scheduling Strategies. Technologies like dynamic batching and continuous batching can effectively increase GPU SM (Streaming Multiprocessor) occupancy. The key is to dynamically adjust batch sizes based on actual load characteristics (such as request latency requirements, input length distribution) to find the optimal balance between throughput and latency. The aforementioned measured throughput improvement of 29%-40% was achieved precisely at the optimal concurrency level, validating the synergistic value of software scheduling and hardware performance.
Second, there's the design and application of efficient caching systems. For example, for KV Cache in large model inference, a hierarchical caching strategy can be adopted to keep hot data in high-speed storage layers. Measured, report R1 shows that by applying the LMCache parallel read patch, in a single-card, 16-concurrency cold read scenario, TTFT improved from 37.97 seconds to 9.30 seconds, an acceleration ratio of 4.1x, while read bandwidth increased from 0.98 GB/s to 5.23 GB/s. This proves that software-layer cache algorithm optimization can directly translate into significant performance gains.
Finally, there's framework and operator-level optimization. Using deeply optimized inference engines (such as vLLM, TensorRT-LLM) and performing operator fusion and kernel tuning for specific hardware platforms (such as AMD ROCm or NVIDIA CUDA) can further reduce kernel launch overhead and memory access latency, improving computational efficiency.
How to Achieve Co-design of Storage and Computing Hardware?
The final stage of efficiency unlocking involves examining the synergy between computing and storage from a system-level perspective. This is not just about selecting high-performance hardware, but also about architectural matching and integration.
First, matching bandwidth and latency. GPU computing cards already have memory bandwidth as high as TB/s levels (e.g., AMD MI308X's 192GB HBM bandwidth is approximately 3.5TB/s). To feed data to such computing units, the effective bandwidth of the external storage system must match it to avoid becoming a bottleneck. PCIe 5.0, 6.0 interfaces and 200Gb, 400Gb networks are the physical foundation supporting high-bandwidth demands. Simultaneously, low latency is crucial for key metrics like TTFT, requiring storage devices to have extremely high IOPS processing capability (e.g., millions of IOPS) and optimized protocol stacks.
Second, planning capacity and hierarchy. Based on workload characteristics, plan multi-level storage hierarchies including GPU memory, local SSD, all-flash arrays, and object storage. Place frequently accessed model weights and active KV Cache in the storage layer with the lowest latency and highest bandwidth (e.g., all-flash NVMe-oF arrays), while storing archived checkpoints and historical data in lower-cost capacity tiers. In the measured, report R1, the training checkpoint save speed improved by 1.9x (from 178 seconds to 94 seconds), precisely demonstrating the value of high-speed storage layers in write-intensive tasks.
Third, standardization and maintainability. Adopting standardized hardware form factors like U.2, E1.S and standard network protocols like NVMe-oF and RoCE is conducive to building decoupled, scalable, and easily maintainable heterogeneous computing pools, avoiding vendor lock-in and laying the foundation for future hardware iteration and expansion.
Conclusion Improving the efficiency of existing GPU clusters is a systematic project that requires full-stack examination and optimization from data flow and software stack to hardware co-design. Among these, solving storage I/O bottlenecks is often the entry point with the highest return on investment, capable of directly unleashing constrained GPU computing power. Mingxin Technology's all-flash NVMe-oF accelerated storage solution has been validated in multiple rounds of joint testing with mainstream GPU platforms for its practical value in reducing inference latency and improving training efficiency. We offer a gated joint testing cooperation model lasting approximately 10 weeks, helping customers quantitatively evaluate efficiency improvement potential through reproducible tests and providing solid data support for technical decisions.
Key Q&A
Q: What is the specific acceleration effect of storage optimization on large model inference? A: According to measured, report R2/R3, in long-context inference scenarios with 480B parameter models, dedicated storage acceleration solutions can reduce First Token Latency (TTFT) by 26%-32% and improve inference throughput by 29%-40%. Compared to the recomputation baseline without external storage acceleration, the TTFT acceleration ratio can reach 12.6x.
Q: Besides storage, what other software-level optimization methods are there? A: Core methods include: implementing dynamic/continuous batching to optimize GPU utilization; applying hierarchical caching strategies (e.g., KV Cache optimization), with measured, report R1 showing it can improve TTFT in cold read scenarios by 4.1x; and adopting deeply optimized inference frameworks with operator-level tuning to improve computational efficiency.
Q: When planning a computing cluster, how should storage and computing co-design be considered? A: Three key points need focus: bandwidth and latency must match GPU computing capability to avoid I/O bottlenecks; design multi-level storage hierarchies, placing hot data in high-performance tiers; and adopt standardized hardware interfaces and network protocols (e.g., NVMe-oF) to build a decoupled, scalable architecture ensuring long-term maintainability and upgrade flexibility.