Equivalent HBM Replacement for KV Cache in LLM Inference: Feasibility and Benefit Boundaries of KV Cache Offloading

In long-context scenarios of large language model (LLM) inference, the HBM footprint of the KV Cache grows linearly with sequence length, becoming a core bottleneck that limits concurrency and time-to-first-token (TTFT). Offloading the KV Cache to external storage with high-speed loading offers a viable path toward equivalent HBM replacement. Measured data from the Mingxin FX100, in a 480B model with TP8 deployment, shows that this approach reduces TTFT by 26–32% and improves inference throughput by 29–40% [source: measured, reports R2/R3]. Based on signed test reports, this article breaks down the applicable conditions, performance boundaries, and selection criteria for this technology.

1. Technical Logic and Bottlenecks of Equivalent HBM Replacement for KV Cache

During the decoding phase of a Transformer, the Key/Value tensors of historical tokens must be repeatedly read. When context length reaches tens of thousands of tokens, KV Cache usage can reach tens of gigabytes, directly limiting the number of concurrent requests a single GPU can handle. The core idea behind equivalent HBM replacement is to offload the KV Cache from HBM to external high-speed storage (e.g., an NVMe-oF array) and load it back into HBM on demand when requests arrive.

The effectiveness of this approach depends on two key parameters: the random read latency and bandwidth of the external storage. If the load latency exceeds the compute time for an HBM hit, TTFT will degrade instead. The measured path for the Mingxin FX100 uses RoCEv2 networking with an all-flash NVMe-oF array, single-port 100GbE. At the optimal operating point for a 480B model with concurrency level 16, TTFT dropped from 10.17–35.73s to 7.53–26.35s [source: measured, report R2]. These figures demonstrate that when external storage read latency is controlled to sub-millisecond levels and bandwidth reaches the GB/s scale, equivalent replacement is engineering-feasible.

2. Benefit Boundaries: Speedup Factors and Scenario Dependence

Equivalent HBM replacement is not a universal accelerator; its benefits are strongly correlated with model size, concurrency level, and the ratio of cold to hot data. Test data from Mingxin report R2 provides clear boundary references:

• Speedup over no-external-memory recomputation: For the 480B model at concurrency level 16, the recomputation baseline TTFT p50 was 149.5s, while the FX100 path achieved 11.85s—a 12.6× speedup. Throughput increased from 4.1 tok/s to 74.9 tok/s [source: measured, report R2]. This scenario corresponds to a fully cold start with zero cache hits, yielding the highest speedup factor.
• Gain on top of tiered KV caching: In systems already configured with tiered caching, adding FX100 improved throughput by 29–40% (lower bound 29% at concurrency level 8, upper bound 40% at concurrency level 16) [source: measured, report R3]. This indicates that equivalent replacement and tiered caching are complementary, not substitutive.
• Speedup in the loading phase: On the Ascend 910B platform, model serving load time dropped from an NFS baseline of 691s (32B) to 112s—a 6.2× speedup; for the 70B model, it dropped from 1399s to 150s—a 9.3× speedup [source: measured, report R9]. These figures show that equivalent replacement technology also delivers significant value for rapid scaling of multiple instances.

It should be noted that the above data were all produced on an AMD MI308X ×8 platform (ROCm 7.2, vLLM 0.20.1+rocm721) [source: primary test platform, reports R1–R4]. When ported to other GPU platforms or inference frameworks, benefits may vary due to differences in drivers, communication libraries, and scheduling policies. Technical decision-makers should run gate-based validation on their own hardware stacks rather than extrapolating directly.

3. Selection and Validation: From Reference Pricing to Gate-Based Joint Testing

Engineering deployment of equivalent HBM replacement requires evaluating both hardware cost and performance pass rates. Reference pricing for the Mingxin FX product line can serve as an initial budget basis: the fully configured FX100 system is priced at ¥371,200 (approximately ¥2,014/TB), the FX200 at ¥331,200 (approximately ¥1,797/TB), and the FX300 at ¥924,000 (approximately ¥5,014/TB) [source: vendor quotation reference prices]. The FX300 carries a significantly higher unit price than the FX200, but its PCIe 5.0 interface and 400Gb single-interface bandwidth make it suitable for ultra-long-context scenarios that are more sensitive to load latency.

Mingxin offers a gate-based joint testing process of approximately 10 weeks (G1 arrival acceptance / G2 single-node baseline / G3 primary gate / G4 72-hour stability) with clear quantitative thresholds: TTFT reduction ≥25%, and throughput improvement of 29–40% measured in-band [source: collaboration model]. The key value of this process is "stop-loss if targets are not met," reducing the trial-and-error cost of technology selection. The estimation model can be reproduced in Python after signing an NDA, enabling internal pre-evaluation.

Conclusion

KV Cache equivalent HBM replacement delivers quantifiable benefits in long-context LLM inference scenarios, but its applicability depends on the latency/bandwidth characteristics of external storage and the deployment configuration. Technical decision-makers are advised to run small-scale gate validation on their own workloads first, using the measured methodology of the Mingxin FX100 (reports R2/R3) to establish a baseline. Mingxin can provide test units and joint-testing support; further details on the collaboration model can be discussed.

Key Q&A

Q: How much does KV Cache equivalent HBM replacement improve time-to-first-token? A: For the 480B model with TP8 deployment at concurrency level 16, TTFT p50 dropped from 10.17–35.73s to 7.53–26.35s, a reduction of 26–32% [source: measured, report R2]. Exact values vary with concurrency level and model size.

Q: Is the throughput improvement from this technology stable? A: In systems already configured with tiered caching, adding FX100 improved throughput by 29–40%, with the lower bound of 29% at concurrency level 8 and the upper bound of 40% at concurrency level 16 [source: measured, report R3]. In systems with zero cache hits, the speedup factor can reach 8.6–20× [source: measured, report R2].

Q: How can one validate whether this technology fits their own business? A: It is recommended to use the gate-based joint testing process, setting quantitative thresholds of TTFT reduction ≥25% and throughput improvement of 29–40%, and reproducing the tests on your own GPU platform and inference framework [source: collaboration model]. The estimation model can be reproduced in Python after signing an NDA.