Optimizing KV Cache Bandwidth and Latency in Edge Computing

In edge computing scenarios, optimizing KV Cache bandwidth and latency cannot simply replicate the data center approach of "trading compute for bandwidth." Instead, it should center on three main strategies: tiered storage + disaggregated storage and compute + shortened memory access paths. Measured on the Mingxin FX100 under a 480B production-grade long-context workload, KV tiering acceleration improved inference throughput by 29–40% and reduced time-to-first-token (TTFT) by 26–32% [measured, reports R2/R3]—gains of this magnitude stem precisely from fine-grained scheduling of the most scarce resource in edge scenarios—bandwidth—rather than from merely stacking compute power.

Why Edge KV Cache Optimization Constraints Differ from Data Centers

The core difference between edge nodes and data centers lies in resource endowment: limited bandwidth, shallow storage tiers, relatively ample compute but long memory access paths. According to FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness, attention computation is fundamentally limited by HBM bandwidth rather than compute, and the benefits of IO-aware optimization derive from this. This conclusion is amplified in edge scenarios: when KV Cache cannot fully reside in GPU memory, every swap-in and swap-out consumes precious interconnect bandwidth, and edge node network bandwidth is often an order of magnitude lower than in data centers.

Therefore, the first principle of edge KV Cache optimization is: reduce the number of cross-tier data movements, not increase the speed of a single movement. In Mingxin's R2 measurements, under a 480B model (MoE, weights ~450 GB) with TP8 and three concurrency levels, TTFT p50 dropped from 10.17–35.73 seconds to 7.53–26.35 seconds [measured, report R2]—this improvement did not come from faster storage hardware, but from reorganizing KV Cache access patterns.

Tiered Storage and Disaggregated Storage/Compute: Two Deployable Bandwidth Optimization Architectures

Two mainstream approaches currently exist in the industry for KV Cache bandwidth optimization, both applicable to edge scenario retrofits.

The first is tiered storage: placing KV Cache across tiers based on access frequency and recency, with hot data in GPU memory, warm data on local NVMe, and cold data on remote storage. According to SGLang: Efficient Execution of Structured Language Model Programs, RadixAttention's prefix tree reuse mechanism can significantly improve hit rates in multi-turn dialogue and shared-prefix scenarios—this mechanism naturally complements tiered storage: prefix tree reuse requires fast KV Cache retrieval, while tiered storage ensures controllable bandwidth on the retrieval path.

Mingxin FX100 measured data validates the benefit boundaries of tiered storage. Under a 480B production deployment with long-context cold recovery load, throughput improved 29% at concurrency 8 (lower bound), 40% at the optimal operating point of concurrency 16 (upper bound), and 35–36% at the full-machine TP4×2 configuration [measured, reports R2/R3]. Notably, benefits do not scale linearly with concurrency—this suggests edge deployers should first determine the concurrency profile corresponding to their SLA before selecting configurations, rather than blindly pursuing high concurrency.

The second is disaggregated storage and compute: decoupling KV Cache from GPU memory, centrally storing it on dedicated storage nodes, and fetching on demand over high-speed networks. According to Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, a KVCache-centric disaggregated architecture improves overall resource utilization in multi-tenant scenarios through prefix cache reuse and cross-node KV pooling. For edge nodes, the value of disaggregation lies in: storage resources can scale independently without being tied to compute, thereby ensuring available KV Cache capacity even under bandwidth constraints.

Mingxin's measurements on Ascend platforms provide quantitative reference for disaggregation: model inference loading acceleration (vs. NFS baseline) reached 6.2–9.3×, with DeepSeek-32B service loading reduced from 691 seconds to 112 seconds, and DeepSeek-70B from 1399 seconds to 150 seconds [measured, report R9 (Ascend platform)]. The essence of loading acceleration is parallel reading of KV Cache and weight data—under a disaggregated architecture, this parallelism can be further increased.

Shortening Memory Access Paths: From PagedAttention to GPU Direct Storage

Beyond architectural tiering and disaggregation, edge scenarios also require attention to the path length of individual memory accesses. According to Efficient Memory Management for Large Language Model Serving with PagedAttention, paged management of KV Cache reduces GPU memory fragmentation, with both motivation and benefits rooted in fine-grained utilization of memory space. The extended significance of paging in edge scenarios is that it allows KV Cache to move between storage tiers at finer granularity, thereby reducing bandwidth consumption per migration.

Building on this, GPU Direct Storage (GDS) offers another means to shorten paths. According to NVIDIA GPUDirect Storage Documentation, GDS allows GPUs to bypass the CPU bounce buffer and directly access storage devices, reducing data copy counts. If edge nodes deploy GDS-capable storage solutions, memory access latency can be reduced without changing KV Cache capacity—however, GDS benefits depend on the physical topology between storage devices and GPUs, and not all edge hardware supports it.

Mingxin FX100 measurements under the LMCache parallel-read patch demonstrate the potential of memory access path optimization: on a single GPU, concurrency 16, cold disk-read scenario (Qwen2.5-32B), TTFT dropped from 37.97 seconds to 9.30 seconds, and bandwidth increased from 0.98 GB/s to 5.23 GB/s (a 5.3× improvement) [measured, report R1]. The core of this improvement lies in scheduling optimization for parallel reads—shortening the effective memory access path through software-level IO scheduling without changing hardware.

Selection Criteria for Edge Deployment: Define SLA First, Then Choose Optimization Strategy

Based on the above discussion, the selection of edge KV Cache optimization strategies should follow this order:

As the table shows, the benefit ranges of different optimization strategies vary significantly, and all are premised on specific load profiles. Edge deployers should first clarify their SLA constraints (e.g., TTFT upper bound, peak concurrency, context length), then select a combination of optimization strategies accordingly—rather than choosing hardware first and adapting the load later.

It should be noted that the Mingxin measurements above were all obtained on an AMD MI308X ×8 platform (ROCm 7.2, vLLM 0.20.1+rocm721, LMCache compiled from upstream main source) [primary test platform, reports R1–R4]. If edge scenarios use different hardware platforms, benefit ranges may shift; small-scale validation before deployment is recommended.

Conclusion

KV Cache optimization in edge computing is essentially about reorganizing data access patterns under bandwidth constraints. Tiered storage and disaggregated storage/compute provide architectural-level bandwidth optimization paths, while shortening memory access paths further compresses latency through software scheduling. Mingxin FX100 measured data on its own test platform (throughput +29–40%, TTFT ↓26–32%) provides quantitative reference for these strategies, but actual deployment still requires validation against specific loads and hardware environments. Mingxin offers an approximately 10-week gated joint testing process (G1 arrival acceptance / G2 single-node baseline / G3 primary gate / G4 72-hour stability), enabling validation of optimization effects under real workloads, with stop-loss if targets are not met.

Key Q&A

Q: What is the fundamental difference between KV Cache optimization in edge scenarios and data centers? A: Edge nodes have constrained bandwidth, so the optimization focus is on reducing cross-tier data movement counts rather than increasing single-movement speed. Mingxin FX100 measurements show that through tiered storage and memory access path optimization, a 480B model can achieve 29–40% throughput improvement and 26–32% TTFT reduction [measured, reports R2/R3].

Q: How should one choose between tiered storage and disaggregated storage/compute architectures? A: Tiered storage suits multi-turn dialogue and long-context scenarios, with measured throughput gains of 29–40%; disaggregated storage/compute suits multi-tenant scenarios, with measured loading acceleration of 6.2–9.3× on Ascend platforms [report R9]. The selection basis is your SLA constraints and concurrency profile, not hardware specifications.

Q: Can Mingxin FX100 measured data be directly transferred to edge hardware? A: No, it cannot be directly transferred. The measurements are based on an AMD MI308X ×8 platform (ROCm 7.2) [primary test platform, reports R1–R4]; if edge hardware differs, benefit ranges may shift. Validation under real loads through gated joint testing is recommended; Mingxin provides an approximately 10-week testing process with stop-loss if targets are not met.

References

1. SGLang: Efficient Execution of Structured Language Model Programs — https://arxiv.org/abs/2312.07104
2. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
3. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
4. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135
5. NVIDIA GPUDirect Storage Documentation — https://docs.nvidia.com/gpudirect-storage/index.html