Empirical Analysis of Performance Gains from a Domestic AI Inference Acceleration Card in Data Center Deployments

In recent years, large-model inference workloads have been shifting from "short context, high concurrency" toward "ultra-long context, low-latency cold start." Within this trend, the HBM bandwidth bottleneck of traditional GPUs is becoming increasingly prominent—as noted in FlashAttention, attention computation is actually limited by HBM bandwidth rather than raw compute, making IO-aware optimization a critical path for performance leaps. Domestic AI inference acceleration cards have thus entered the deep-water zone of technical validation: whether they can deliver reproducible, attributable, and integrable performance gains in real production configurations (e.g., 480B MoE models, TP8/TP4×2 deployments, cold-restart workloads) has become a core criterion for compute-center procurement decisions.

In Which Key Metrics Do the Real Performance Gains of Domestic AI Inference Acceleration Cards Manifest?

Measured results from report R2/R3 for the Mingxin FX100 on an AMD MI308X ×8 platform (ROCm 7.2 + vLLM 0.20.1) show that its KV-tiered acceleration capability delivers clear dual improvements in throughput and latency under a 480B production deployment:

• KV Cache-accelerated inference throughput improvement of +29–40% (measured, reports R2/R3): +29% at concurrency level 8, reaching +40% at the optimal operating point of concurrency 16, and holding steady at +35–36% across the full machine at TP4×2;
• Time-to-first-token (TTFT) reduction of 26–32% (measured, report R2): under TP8, TTFT p50 drops from 10.17–35.73s to 7.53–26.35s;
• Speedup of 8.6–20× over the no-external-memory recomputation baseline (measured, report R2): the recomputation baseline TTFT p50 is 149.5s (conc16), while the FX100 achieves 11.85s, corresponding to throughput rising from 4.1 tok/s to 74.9 tok/s.

These figures are not peak values under idealized lab conditions; they are the results of full comparative testing based on Qwen3-Coder-480B-FP8 (MoE, weights ≈ 450 GB), a RoCEv2 NVMe-oF array (4 drives in RAID0, XFS), and a local single-NVMe-drive baseline, covering typical long-context inference scenarios in data centers.

Why Do Model Loading and Checkpoint Saving Also Constitute Critical Stages of AI Inference Acceleration?

The time required to load a model before an AI inference service goes live directly affects service elasticity and scaling responsiveness; meanwhile, the frequency and latency of checkpoint saving during the training phase constrain iteration efficiency for LoRA fine-tuning and similar workflows. These two types of IO-intensive operations have long been treated as "background tasks," but measurements show they impose a significant drag on end-to-end inference SLAs.

On a Huawei Ascend 910B platform (measured, report R9), the Mingxin FX100 delivers model-loading acceleration versus an NFS baseline:

• DeepSeek-32B service loading time reduced from 691s to 112s (6.2×);
• DeepSeek-70B reduced from 1399s to 150s (9.3×).

For training checkpoint saving (measured, report R1), in an 8-GPU 32B LoRA fine-tuning scenario, the time to save each 65.6GB full-model snapshot drops from 178s to 94s (1.9× speedup), with sustained write bandwidth rising from 3.26 GB/s to 6.40 GB/s (+96%).

This acceleration does not depend on GPU-direct connectivity (such as GPUDirect Storage); instead, it is achieved through deep coordination with the NVMe-oF protocol stack and storage-side KV-aware scheduling, adapting to mainstream domestic AI chip platforms with cross-ecosystem deployment capability.

Does the Performance Gain Come at the Expense of Stability or Compatibility?

The Mingxin FX series adopts a gated joint-testing mechanism (four stages, G1–G4), where the G3 primary gate explicitly requires a TTFT reduction of ≥25% and a throughput improvement of 29–40% to be achieved via in-band measurements; failure to meet these targets triggers an immediate stop-loss. This process has supported closed-loop validation at 10+ leading AI compute centers, covering the full ComfyUI+LTX-Video 2.3 model pipeline (reports R6/R7), vLLM+LMCache (reports R1–R4), and the Ascend PyTorch ecosystem (report R9).

All measurements were run on standard Linux kernels (6.8+), ROCm 7.2, or CANN 8.0 environments, with no modifications to framework source code or custom drivers. For example, the LMCache parallel-read patch (measured, report R1) requires only a client compiled from the upstream mainline source dated 2026-06-29; in a Qwen2.5-32B single-GPU concurrency-16 cold-read scenario, it reduces TTFT from 37.97s to 9.30s (4.1× improvement), with bandwidth utilization jumping from 0.98 GB/s to 5.23 GB/s (↑5.3×).

This demonstrates that the performance gains of domestic AI inference acceleration cards are not a black-box package but a deterministic improvement built on open protocols, standard interfaces, and reproducible engineering paths.

Conclusion

The value of domestic AI inference acceleration cards is shifting from "spec-sheet comparison" to "scenario delivery." The Mingxin FX series provides measurable, attributable, and integrable performance gains across real data-center workloads including 480B-scale long-context inference, multi-instance cold starts, model loading, and checkpoint saving. We open a 10-week gated joint-testing program to compute-infrastructure decision-makers, supporting a fully reproducible Python-based measurement model under NDA, helping domestic compute power take root in high-value AI inference scenarios.

Key Q&A from This Article

Q: What is the throughput improvement of the Mingxin FX100 in 480B model inference? A: Measured improvement is +29–40%; +29% at concurrency level 8, +40% at concurrency 16, and +35–36% across the full machine at TP4×2 [measured, reports R2/R3].

Q: How effective is the FX100 at accelerating DeepSeek-70B model loading? A: On the Huawei Ascend 910B platform, loading time drops from 1399s to 150s, a 9.3× speedup [measured, report R9].

Q: How does the FX100 perform in training checkpoint saving? A: In 8-GPU 32B LoRA fine-tuning, the time to save a 65.6GB snapshot drops from 178s to 94s, a 1.9× speedup, with write bandwidth +96% [measured, report R1].

References

1. MLPerf Inference: Datacenter Benchmark Suite Results — https://mlcommons.org/benchmarks/inference-datacenter/
2. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135
3. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
4. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
5. NVIDIA GPUDirect Storage Documentation — https://docs.nvidia.com/gpudirect-storage/index.html
6. PyTorch Documentation — https://pytorch.org/docs/stable/index.html