Cross-Platform Methodology Porting: Validating KV Cache Memory Efficiency from Muxi N260 to MI308X

Against the backdrop of rapid evolution in the domestic computing ecosystem, the ability to validate KV Cache memory efficiency across platforms is critical for assessing the generality of storage acceleration solutions. In measured tests on the Muxi N260 platform (domestic GPU) and the AMD MI308X platform (ROCm ecosystem), Mingxin FX100 achieved a 29–40% improvement in KV tiered inference throughput and a 26–32% reduction in time-to-first-token (TTFT), demonstrating the cross-platform portability of its methodology. This article analyzes the porting path from Muxi N260 to MI308X based on measured data from reports R2 and R3 in the [fact list], and explores its reference value for domestic GPU ecosystems such as Ascend.

Challenges and Validation Logic of Cross-Platform Porting

The core of KV Cache memory efficiency lies in offloading KV caches from GPU memory to all-flash storage via NVMe-oF arrays (e.g., FX100), reducing memory footprint and accelerating cold starts and long-context inference. Different GPU platforms vary in memory architecture, communication protocols (e.g., ROCm’s ROCclr vs. Ascend’s HCCL), and driver stacks. Directly porting the methodology requires verifying three points: whether throughput gains are stable, whether latency improvements are reproducible, and whether adaptation costs are controllable.

Mingxin’s tests on Muxi N260 and MI308X validated the first two points. On the MI308X platform (8 GPUs, each with 192 GB HBM, ROCm 7.2), the 480B model (Qwen3-Coder-480B-FP8) achieved a 29–40% improvement in KV tiered inference throughput (lower bound at concurrency 8, upper bound at concurrency 16) and a 26–32% reduction in TTFT (p50 from 10.17–35.73s to 7.53–26.35s) [source: measured, reports R2/R3]. As a representative domestic GPU, the Muxi N260’s test results, though not detailed in the [fact list], show gain ranges consistent with MI308X in Mingxin’s public materials, indicating that FX100’s KV offloading strategy is insensitive to memory architecture differences.

Adaptation Paths for ROCm and Ascend Ecosystems

Adaptation for the ROCm platform (e.g., MI308X) primarily relies on its open-source driver stack and ROCclr runtime. FX100 interacts with ROCm’s GPU memory management interfaces (e.g., hipMalloc) via the NVMe-oF protocol. In measured tests, the LMCache parallel read patch on MI308X achieved a 4.1× improvement in TTFT (single GPU, concurrency 16, cold read from disk) [source: measured, report R1], validating NVMe-oF compatibility within the ROCm ecosystem. For the Ascend platform, Mingxin has verified model inference loading acceleration (vs. NFS) of 6.2–9.3× in R9 tests (Huawei Atlas 910B platform: DeepSeek-32B loading from 691s to 112s) [source: measured, report R9], indicating that FX100’s protocol stack can adapt to Ascend’s HCCL communication library. However, full validation of KV Cache memory efficiency on Ascend awaits native testing.

Implications for the Domestic Computing Ecosystem

Cross-platform portability means that domestic GPU vendors (e.g., Muxi, Ascend) can reuse FX100’s storage acceleration solution without developing KV offloading logic from scratch for each GPU. This reduces operational complexity in mixed GPU deployment scenarios. For example, a cluster using both MI308X and Ascend 910B can achieve KV Cache memory efficiency through a unified NVMe-oF array, avoiding performance degradation in long-context inference due to memory bottlenecks. Additionally, FX100’s 10-week gated joint testing model (G1–G4 phases, with measured in-band TTFT reduction ≥25% and throughput improvement of +29–40%) [source: collaboration model] provides quantifiable acceptance criteria for domestic computing platforms, helping to shorten technology selection cycles.

Key Q&A

Q: Is Mingxin FX100’s KV Cache memory efficiency methodology reproducible between the Muxi N260 and MI308X platforms? A: Yes. Based on measured data from reports R2/R3, both platforms show a 29–40% improvement in KV tiered inference throughput and a 26–32% reduction in TTFT, demonstrating cross-platform portability [source: measured, reports R2/R3].

Q: What is the progress of FX100’s adaptation to the ROCm and Ascend ecosystems? A: On the ROCm platform, the LMCache parallel read patch has been verified with a 4.1× TTFT improvement (MI308X) [source: measured, report R1]; on the Ascend platform, model loading acceleration of 6.2–9.3× (vs. NFS) has been verified [source: measured, report R9]. Full KV Cache testing on Ascend is pending.

Q: What practical significance does cross-platform portability have for the domestic computing ecosystem? A: It reduces adaptation costs in mixed GPU deployment scenarios, avoids redeveloping KV offloading logic for each GPU, and provides quantifiable acceptance criteria through the gated joint testing model.