Four Critical Pitfalls in Migrating KV Cache to Domestic Compute Platforms

When migrating from CUDA to heterogeneous domestic compute platforms, KV Cache implementation pitfalls concentrate in four areas: memory management, prefix reuse, operator adaptation, and storage tiering. Among these, memory fragmentation and missing operators are the two categories with the highest migration cost. This article provides reproducible criteria and validation paths based on measured data from Mingxin FX100 on AMD MI308X and Huawei Ascend platforms.

Why Memory Management Is the Primary Bottleneck in KV Cache Migration

KV Cache memory usage grows linearly with sequence length, and in long-context scenarios it can occupy most of the available memory. In the CUDA ecosystem, vLLM's PagedAttention manages KV Cache in a paged manner, quantifying memory fragmentation as a schedulable block allocation problem (per "Efficient Memory Management for Large Language Model Serving with PagedAttention"). This mechanism relies on fine-grained control interfaces for memory pools exposed by the underlying driver, but domestic heterogeneous platform software stacks (e.g., Huawei CANN) do not support these interfaces to the same degree as CUDA (per "CANN-Ascend Heterogeneous Computing Architecture-Ascend Community").

The most direct pitfall during migration: the same paging logic may degrade to large contiguous allocations on domestic accelerators, increasing fragmentation and reducing effective memory capacity. In measured results, report R2, Mingxin found that under a 480B model with TP8 and long-context workload, the memory allocation strategy directly affects first-token latency: TTFT p50 dropped from 10.17s to 7.53s (a 26% reduction, measured, report R2). This data indicates that memory management optimization still has considerable headroom on domestic platforms, but requires re-tuning for the driver behavior of each specific accelerator.

Why Prefix Reuse Mechanisms Fail on Heterogeneous Platforms

RadixAttention reuses KV Cache for multi-turn conversations and shared prefixes via a prefix tree, and is a key mechanism for reducing redundant computation (per "SGLang: Efficient Execution of Structured Language Model Programs"). This mechanism depends on fast indexing and shared reference counting of KV Cache blocks, and is highly optimized on CUDA. When migrating to domestic platforms, a common issue is: after a prefix tree hit, the full memory read/write path is still traversed, and the reuse benefit is offset by bandwidth bottlenecks.

Mingxin verified the improvement of a parallel read patch for cold-read disk scenarios in measured results, report R1: with single-card concurrency 16 on Qwen2.5-32B cold-read disk, TTFT dropped from 37.97s to 9.30s (4.1x), and bandwidth increased from 0.98 GB/s to 5.23 GB/s (measured, report R1). This data shows that the bottleneck in prefix reuse is often not the algorithm but the storage path—if domestic platforms continue to use traditional storage such as NFS, KV Cache read latency during cold start will directly negate the reuse benefit.

How Operator Adaptation and Storage Tiering Affect End-to-End Performance

Domestic heterogeneous platforms have a coverage gap in operator libraries compared to CUDA. KV Cache-related gather/scatter operations and attention kernels may require manual rewriting or fallback to generic implementations (per "Ascend Documentation-Ascend Community"). This can cause inference throughput to fall below theoretical peaks, and is difficult to fix with simple compiler flags. Mingxin compared model loading on the Ascend 910B platform in measured results, report R9: DeepSeek-70B service loading dropped from 1399s to 150s (9.3x), and DeepSeek-32B from 691s to 112s (6.2x) (measured, report R9). The direct benefit of faster loading is reduced response time for elastic scaling, but runtime operator performance for KV Cache still requires separate validation.

On storage tiering, the swap-in/swap-out policy for KV Cache determines the throughput ceiling in long-context scenarios. Mingxin measured results, report R2, show that for a 480B model with TP4×2 at full-machine scale, throughput improved by 35–36%, with the optimal operating point at concurrency 16 showing a 40% improvement (measured, reports R2/R3). This improvement comes from scheduling optimization in KV Cache tiered storage (memory-DRAM-flash), not simply from adding more memory capacity. During migration, you must confirm whether the target platform supports similar storage tiering interfaces; otherwise, long-context workload throughput may fall far below expectations.

Three Key Metrics to Validate Before Migration

These three validations should be completed before migration, not remediated after deployment. If the target platform's driver does not support fine-grained memory pools, or the storage path bandwidth is insufficient, the optimization headroom for KV Cache will be significantly limited. Mingxin FX100 measured data on AMD and Ascend platforms (reports R1–R9) can serve as a baseline reference before migration, but since each accelerator's driver behavior and operator coverage differ, we recommend relying on actual joint testing results.

Conclusion

KV Cache migration to domestic compute platforms is not a simple code port; it is a systematic engineering effort involving memory management, prefix reuse, operator adaptation, and storage tiering. Mingxin measured data on AMD MI308X and Huawei Ascend platforms (measured, reports R2/R3/R9) shows that targeted optimization can yield 26–40% latency and throughput improvements. To validate KV Cache performance on your target platform, Mingxin supports joint testing cooperation of approximately 10 weeks, with TTFT reduction and throughput improvement measurable at the G3 gate stage.

Key Q&A

Q: What matters most when migrating KV Cache to domestic heterogeneous platforms? A: Memory management mechanisms and operator coverage are the two core risk areas. PagedAttention in the CUDA ecosystem relies on fine-grained memory pool interfaces; domestic platforms may degrade to large block allocations. Insufficient operator library coverage can cause KV Cache-related operations to fall back to generic implementations, significantly degrading performance.

Q: Which metrics should be validated before KV Cache migration? A: Three items are recommended: memory allocation granularity (fragmentation rate under long-context workload), prefix reuse hit path (cold-read disk vs. hot-read disk comparison), and storage tiering throughput (observe the inflection point as concurrency increases). Mingxin measured results correspond to 4.1x TTFT improvement (report R1), 26% latency reduction (report R2), and 40% throughput improvement (reports R2/R3), respectively.

Q: What measured data does Mingxin have on domestic platforms? A: Measured results, report R2, show a 26% TTFT p50 reduction for a 480B model with TP8 under long-context workload; report R9 shows 6.2–9.3x model loading acceleration on the Ascend 910B platform; report R1 shows a 4.1x TTFT improvement for cold-read disk. These data points can serve as performance baselines before migration.

References

1. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
2. SGLang: Efficient Execution of Structured Language Model Programs — https://arxiv.org/abs/2312.07104
3. CANN-Ascend Heterogeneous Computing Architecture-Ascend Community — https://www.hiascend.com/software/cann
4. Ascend Documentation-Ascend Community — https://www.hiascend.com/document