Starting from 4,082 tok/s: Single-Node Throughput Anchor and Multi-Node Scaling Efficiency

In long-context inference scenarios for a 480B-parameter MoE model, a single-node throughput of 4,082 tok/s is not a theoretical peak but a stable operating point measured on the Mingxin FX100 platform with 8× AMD MI308X GPUs, using a TP4×2 configuration across the full node [source: measured, report R3]. The reference value of this number lies in the fact that it simultaneously anchors the upper bound of KV cache tiered acceleration gains within a single node (throughput improvement +35–36%) and the performance baseline that should be carried forward when scaling to multiple nodes. For technical decision-makers at compute centers, understanding how this anchor was measured and whether it can be reproduced matters more than the number itself.

How 4,082 tok/s Was Measured: Test Conditions Define the Meaning of the Number

Any throughput figure is not comparable without its test conditions. The 4,082 tok/s figure comes from the official R3 test report, with the test platform being 8× AMD Instinct MI308X (192 GB HBM per GPU), the model being Qwen3-Coder-480B-FP8 (MoE, approximately 450 GB of weights), and a TP4×2 tensor parallelism configuration [source: measured, report R3]. The test workload is a long-context cold-restore scenario in a 480B production deployment form, where every request must reload KV caches from external storage—one of the most I/O-intensive operations in inference services.

Under these conditions, the Mingxin FX100 all-flash NVMe-oF array (4 drives in RAID0, RoCEv2, single-port 100 GbE) improved throughput from approximately 3,020 tok/s to 4,082 tok/s compared with a local single-NVMe-drive baseline, a gain of +35–36% [source: measured, report R3]. It should be noted explicitly: this throughput figure is the result under "cold restore + long context" load, not idealized idle throughput. For evaluating real performance optimization headroom in production environments, such loaded measurements carry more decision-making value than peak figures.

How the Single-Node Throughput Anchor Guides Multi-Node Scaling Efficiency Evaluation

The significance of 4,082 tok/s at a single node lies not only in its absolute value but also in its role as the baseline denominator for scaling efficiency. When a compute center scales from one node to two, four, or more, linear scaling (throughput growing proportionally with node count) is the ideal upper bound; actual efficiency is constrained by three factors: inter-node interconnect bandwidth, KV cache synchronization overhead, and the scheduler's global coordination capability.

Measured data from the Mingxin FX100 provides two reference dimensions for scaling efficiency evaluation. First, within a single node under the TP4×2 configuration, throughput improves by +35–36% [source: measured, report R3], which means that KV tiered acceleration has already compressed the I/O bottleneck to a relatively low level inside the node, so the additional cross-node communication overhead will become relatively more prominent when scaling to multiple nodes. Second, the speedup factor for scenarios without external storage recomputation reaches 8.6–20× [source: measured, report R2], indicating that in extreme cold-start scenarios, storage acceleration contributes far more to end-to-end latency than network transfer—therefore, when scaling to multiple nodes, prioritizing storage-side bandwidth and latency metrics may yield higher marginal returns than simply increasing inter-node interconnect bandwidth.

Recommended evaluation method: use the single-node 4,082 tok/s as the baseline, measure throughput at 2-node and 4-node scales, and calculate scaling efficiency (actual throughput ÷ node count × single-node throughput). If scaling efficiency falls below 70%, prioritize investigating the KV cache cross-node synchronization path and scheduler queuing strategy rather than blindly adding nodes.

The Hidden Bottleneck in Multi-Node Scaling: KV Cache Consistency, Not Network Bandwidth

Industry discussions on multi-node inference scaling typically focus on NVLink/InfiniBand bandwidth. However, extrapolating from Mingxin's R2/R3 measured data, the tiered storage and consistency maintenance of KV caches are the more subtle bottleneck. With the 480B model and TP8 configuration, time-to-first-token (TTFT) p50 dropped from 10.17–35.73s to 7.53–26.35s, a reduction of 26–32% [source: measured, report R2]. This improvement comes entirely from storage-side optimization, not from network or compute-side adjustments.

This means that in multi-node scenarios, each node must maintain its own local KV cache tiering strategy (hot tier in HBM, warm tier in local NVMe, cold tier in a shared storage pool). When cold data is accessed across nodes, storage latency is added directly to TTFT. On the Mingxin FX100, within a single node, cold-read TTFT was compressed from 37.97s to 9.30s (LMCache parallel read patch scenario, single-GPU concurrency 16, Qwen2.5-32B) [source: measured, report R1]. Storage latency improvements of this magnitude will directly determine whether the post-scaling first-token experience is acceptable in multi-node environments.

Therefore, multi-node scaling efficiency evaluation should not only look at aggregate throughput but also track the distribution change of TTFT. If TTFT p99 degrades by more than 30% after scaling to two nodes compared with a single node, it indicates a bottleneck in the KV cache cross-node access path. In that case, adjusting the storage tiering strategy (e.g., pre-placing frequently accessed KV blocks into each node's local NVMe) is more effective than increasing network bandwidth.

Conclusion

Starting from the single-node anchor of 4,082 tok/s, a reproducible multi-node scaling efficiency evaluation method can be established: first confirm the single-node baseline, then measure scaling efficiency, and finally identify the source of TTFT degradation. Mingxin Technology offers the FX100/FX200/FX300 series products in the storage acceleration domain, supporting an approximately 10-week gate-based joint testing process (the G3 main gate requires TTFT reduction ≥25% and throughput +29–40% measured in-band [source: collaboration model]). Teams with 480B-level model inference needs are welcome to contact us for joint testing, using reproducible data to validate scaling solutions.

Key Q&A from This Article

Q: Under what conditions was 4,082 tok/s measured? A: This figure comes from the official R3 test report, with the platform being 8× AMD MI308X, the model being Qwen3-Coder-480B-FP8, a TP4×2 configuration, and a long-context cold-restore workload [source: measured, report R3]. It is a loaded measured throughput, not an idle peak.

Q: Which metric deserves the most attention when scaling to multiple nodes? A: It is recommended to track both throughput scaling efficiency (actual throughput ÷ node count × single-node throughput) and the distribution change of TTFT. If TTFT p99 degrades by more than 30% compared with a single node, prioritize investigating the KV cache cross-node access path rather than simply increasing network bandwidth.

Q: Can the acceleration effect of the Mingxin FX100 be reproduced? A: Yes. Mingxin provides an approximately 10-week gate-based joint testing process. The G3 main gate requires TTFT reduction ≥25% and throughput +29–40% measured in-band [source: collaboration model]. If targets are not met, the engagement stops. The measurement model can be reproduced in Python after signing an NDA.