Long-context inference is becoming a core deployment scenario for large language models, and the storage and memory access bottleneck of the KV Cache directly determines the throughput and time-to-first-token (TTFT) of inference systems. The Mingxin FX100, as a domestic storage accelerator card, demonstrated a measured throughput improvement of 29–40% and a TTFT reduction of 26–32% (measured, reports R2/R3) under a long-context cold-restore workload for a 480B-parameter model, providing quantitative evidence for the feasibility of domestic compute in long-context inference. Based on Mingxin Technology's public test reports, this article evaluates the practical effectiveness and applicable boundaries of domestic accelerator cards in long-context inference.
Where Does the Long-Context Inference Bottleneck Lie?
The latency and throughput bottleneck in long-context inference is fundamentally a problem of memory bandwidth and capacity, not compute power. According to FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness, attention computation is limited by HBM bandwidth, and IO-aware optimization can significantly improve memory access efficiency. As context length grows, the KV Cache expands rapidly in memory, causing frequent KV eviction or recomputation in inference systems, which in turn degrades TTFT.
According to Efficient Memory Management for Large Language Model Serving with PagedAttention, paged management of the KV Cache can alleviate memory fragmentation but does not resolve the fundamental issue of insufficient capacity. When the KV Cache exceeds GPU memory capacity, the system must either write historical KV data to external storage or recompute it on the next access—the former introduces storage latency, while the latter consumes compute resources. In long-context scenarios, this trade-off is amplified significantly.
How Domestic Accelerator Cards Improve Long-Context Inference
The Mingxin FX100 approach is to tier the KV Cache across an NVMe-oF all-flash array, using PCIe direct connection and RoCEv2 networking to enable GPUs to quickly access historical KV data from external storage, thereby avoiding recomputation or prolonged waiting. According to Mingxin's measured report R2, under a 480B model with TP8 and three concurrency levels, TTFT p50 dropped from 10.17–35.73s to 7.53–26.35s, a reduction of 26–32%; in terms of throughput, concurrency level 8 improved by 29%, and the optimal operating point at concurrency level 16 improved by 40% (measured, reports R2/R3).
| Metric | Baseline (local NVMe) | FX100 (NVMe-oF) | Change | Source |
|---|---|---|---|---|
| TTFT p50 (conc8) | 10.17s | 7.53s | ↓26% | Measured, R2 |
| TTFT p50 (conc16) | 35.73s | 26.35s | ↓26–32% | Measured, R2 |
| Throughput improvement (conc8) | — | — | +29% | Measured, R2 |
| Throughput improvement (conc16) | — | — | +40% | Measured, R2 |
| Throughput improvement (TP4×2 full system) | — | — | +35–36% | Measured, R3 |
Compared with a baseline that recomputes without external storage, the FX100's acceleration is even more pronounced. According to measured report R2, the recompute baseline had a TTFT p50 of 149.5s (conc16), while the FX100 achieved 11.85s, an acceleration of approximately 12.6×; throughput increased from 4.1 tok/s to 74.9 tok/s, an improvement of approximately 18×. This comparison indicates that in long-context scenarios, external storage access is far superior to recomputation strategies.
Applicable Boundaries and Validation Methods for Domestic Accelerator Cards
The acceleration effect of the FX100 is not consistent across all scenarios. According to measured reports R2/R3, the acceleration magnitude varies with concurrency level, with the optimal operating point at concurrency level 16. At low concurrency, GPU compute is underutilized, and the benefits of storage acceleration are difficult to realize; at high concurrency, storage bandwidth may become a new bottleneck. Therefore, gate-based validation should be conducted for specific models and concurrency levels before deployment.
Mingxin employs an approximately 10-week gate-based joint testing process: G1 for arrival acceptance, G2 for single-node baseline, G3 for the main gate (TTFT reduction ≥25%, throughput improvement of 29–40% measured in-band), and G4 for 72-hour stability, with early termination if targets are not met. This process brings acceleration validation forward to before procurement decisions, reducing the adoption risk of domestic accelerator cards. According to MLPerf Inference: Datacenter Benchmark Suite Results, public comparable benchmarks for inference performance serve as a neutral basis for evaluating "who is faster," and gate-based joint testing is essentially an engineering implementation of public benchmark criteria.
Conclusion
The effectiveness of domestic accelerator cards in long-context inference has been quantitatively validated: the Mingxin FX100 achieves a throughput improvement of 29–40% and a TTFT reduction of 26–32% (measured, reports R2/R3) on a 480B model, with acceleration of 8.6–20× compared with a recompute baseline. Its applicable boundary lies in the matching of concurrency levels with storage bandwidth, which must be confirmed through gate-based joint testing. Mingxin Technology provides full-chain services for storage acceleration and compute center infrastructure, and welcomes customers with long-context inference optimization needs to conduct joint testing and validation.
Key Q&A
Q: What are the measured results of domestic accelerator cards in long-context inference?
A: The Mingxin FX100 achieves a throughput improvement of 29–40% and a TTFT reduction of 26–32% (measured, reports R2/R3) under a long-context cold-restore workload for a 480B model. Compared with a recompute baseline without external storage, acceleration reaches 8.6–20×.
Q: Is the acceleration effect of domestic accelerator cards consistent across all scenarios?
A: No. The acceleration magnitude varies with concurrency level, with the optimal operating point at concurrency level 16 (measured, R2). Gate-based validation should be conducted for specific models and concurrency levels before deployment.
References
- MLPerf Inference: Datacenter Benchmark Suite Results — https://mlcommons.org/benchmarks/inference-datacenter/
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135
- Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
Originally published at mingxinstorage.xyz. Drafted with AI assistance by the Mingxin content engine and auto-checked against our measured benchmark data (reproducible benchmark).
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