Energy Consumption Assessment and Optimization Paths for Data Center-Scale KV Cache Deployment

The deployment location and access path of KV Cache are becoming a non-negligible variable in data center energy bills. Based on measured data from Mingxin FX100 under long-context workloads with a 480B model (measured, reports R2/R3), this article presents a methodological framework for KV Cache energy assessment and optimization directions. The core conclusion is that migrating KV Cache from local GPU memory/local disks to a shared storage pool, while increasing network and storage layer energy consumption, can significantly reduce time-to-first-token (TTFT ↓26–32%) and improve throughput (+29–40%). This enables a reduction in required GPU concurrency headroom under the same SLA constraints, yielding net energy savings at the system level. This conclusion presupposes that the storage pool itself has sufficient bandwidth and low latency characteristics, as elaborated below.

Why KV Cache Becomes an Energy Variable in Data Centers

In LLM inference services, KV Cache size grows linearly with context length. According to the analysis in "Efficient Memory Management for Large Language Model Serving with PagedAttention" (SOSP '23), KV Cache memory usage is one of the primary sources of memory pressure in inference services, and its paging management mechanism was designed specifically to address memory fragmentation and utilization issues. When context lengths reach tens of thousands of tokens, a single request's KV Cache can range from hundreds of MB to several GB, placing sustained pressure on both memory capacity and memory access bandwidth.

In traditional deployment configurations, KV Cache resides in GPU local memory, which means:

• Memory capacity limits the number of concurrently processable requests; excess requests must queue;
• In long-context scenarios, insufficient memory causes requests to be evicted, requiring model or KV Cache reloading from external storage, resulting in significant recomputation.

Both scenarios cause GPU idle waiting or redundant computation, and GPUs are among the highest energy-consuming devices per unit in data centers. According to the NVIDIA DGX SuperPOD reference architecture documentation (NVIDIA Docs), tiered compute, storage, and network design is fundamental methodology in large-scale GPU clusters, where storage layer performance directly determines compute layer utilization. When the storage layer cannot keep pace with GPU consumption rates, GPUs idle-spin waiting—an invisible source of energy waste.

Measured Data: Where the Energy Benefits of External KV Cache Storage Come From

Test data from Mingxin FX100 on an 8× AMD MI308X platform (measured, report R2) quantifies the effect of migrating KV Cache from local disks to NVMe-oF shared storage. The test model is Qwen3-Coder-480B-FP8 (MoE, weights approximately 450GB), under long-context cold-recovery workloads, comparing baseline against the FX100 all-flash array.

The energy implications of these numbers require decomposition. A TTFT reduction of 26–32% means: under the same SLA constraint (e.g., first-token latency not exceeding 10 seconds), the concurrency headroom the system must reserve can be correspondingly reduced. For example, if the baseline requires 16 concurrent requests to meet p50 targets, FX100 may achieve compliance at 12 concurrent requests—the GPU card-hours saved are direct energy savings. Throughput improvements of 8.6–20× mean the number of requests servable per GPU increases substantially, significantly amortizing GPU energy cost per token.

It must be emphasized that these benefits do not come without cost. NVMe-oF arrays consume power, RoCEv2 network switches consume power, and sustained storage media read/write also draws electricity. However, the key point is: storage layer power consumption is orders of magnitude lower than GPU power consumption. According to the architecture analysis in "Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving" (arXiv:2407.00079), one of the core motivations for KVCache-centric disaggregated compute-storage design is to keep expensive GPU resources focused on computation while shifting storage burdens to relatively inexpensive storage nodes. The energy logic of this design trade-off is: rather than letting GPUs idle-spin waiting for recomputation, it is better to let the storage layer absorb additional read/write pressure.

Energy Assessment Methodology: Three Ledgers That Must Be Separated

For data center operators, evaluating the energy impact of KV Cache deployment options requires separating three ledgers for individual accounting, then making a combined judgment.

Ledger One: GPU Utilization. This is the largest energy item. GPU idle power consumption is approximately 30–50% of full load, and the difference between full load and idle is the recoverable space that KV Cache optimization can reclaim. The assessment method is: under a fixed SLA, measure GPU average utilization, queue waiting time, and recomputation count for both baseline and optimized solutions, converting to equivalent GPU card-hours. In Mingxin's measured R2 data, the no-external-recompute baseline (conc16) had a TTFT p50 of 149.5s, while FX100 achieved 11.85s (measured, R2)—meaning the baseline solution had GPUs spending substantial time waiting for recomputation to complete, all of which translates to idle-spin energy consumption.

Ledger Two: Storage and Network. NVMe-oF array and switch power consumption is relatively controllable, but must be accounted based on actual bandwidth utilization rather than rated power. Measured R1 data shows that after the LMCache parallel read patch, single-GPU cold-read disk bandwidth improved from 0.98 GB/s to 5.23 GB/s (↑5.3×, measured, R1). Higher bandwidth utilization means the same storage hardware can serve more GPUs, reducing energy cost per unit of bandwidth.

Ledger Three: Operations and Cooling. Increased storage layer power consumption translates to additional cooling burden, but the cooling reduction from decreased GPU idle-spinning typically far exceeds this. According to Kubernetes official documentation (Kubernetes Docs), resource scheduling and storage volume attachment mechanisms in inference clusters allow operators to finely control storage resource allocation policies, providing orchestration-level leverage for energy optimization.

Optimization Path: Three Steps from Measurement to Deployment

Based on the methodology above, data center-scale KV Cache energy optimization can proceed along the following path:

Step One: Establish a Baseline. Under the current deployment configuration, measure GPU utilization, TTFT distribution, and recomputation frequency for typical workloads, and calculate GPU energy cost per token. The key here is distinguishing "compute energy" from "waiting energy"—the latter being the primary optimization target.

Step Two: Tiered Migration. Migrate KV Cache from GPU local memory to the NVMe-oF shared storage pool, prioritizing long-context and cold-start scenarios. Measured R2 data shows that under 480B model long-context cold-recovery workloads, throughput improvement ranges from +29% (concurrency 8) to +40% (concurrency 16), with a full-system TP4×2 figure of +35–36% (measured, R3)—these figures can serve as inputs for capacity planning.

Step Three: Derive Concurrency from SLA. Using the TTFT reduction (26–32%, measured, R2) as the basis, recalculate the minimum concurrency required to meet SLA targets and release surplus GPU resources. This step requires confirming the SLA percentile requirement (p50 or p95) with the business side, as different percentiles correspond to different concurrency headrooms.

It should be clearly noted that the applicable boundary of the above optimization path is: storage pool bandwidth must be sufficient (recommended no less than the peak KV Cache read rate of a single GPU), network latency must be low (RoCEv2 or better), and the workload itself must be dominated by long-context, high-concurrency scenarios. For short-context, low-concurrency scenarios, the benefits of external KV Cache may be marginal, or may even slightly degrade due to network overhead.

Conclusion

KV Cache energy optimization is not simply about "saving power"—it is about reallocating the energy budget among GPU, storage, and network so that the most expensive compute resources run as close to full utilization as possible. Mingxin FX100's measured data on the 480B model (measured, reports R2/R3) provides quantitative evidence for this allocation. For teams wishing to validate benefits under their own workloads, Mingxin offers an approximately 10-week joint testing collaboration model, allowing TTFT reduction and throughput improvement to be measured on actual business workloads, with the option to stop if targets are not met. The energy ledger ultimately must be calculated on your own workload; others' numbers serve only as reference.

Key Q&A

Q: Can external KV Cache storage genuinely save energy? A: Yes, but only if the storage pool has sufficient bandwidth. Mingxin's measured R2 data shows TTFT reduction of 26–32% and throughput improvement of 29–40% under 480B model long-context workloads. This means the GPU concurrency headroom required under the same SLA decreases, GPU idle-spin energy is significantly reduced, and the net effect is lower system-level energy consumption.

Q: Which metrics should be examined when evaluating KV Cache energy optimization? A: Focus on three ledgers: GPU utilization (including proportion of waiting and recomputation time), storage and network bandwidth utilization, and cooling burden changes. Do not look only at storage layer power consumption—the cost of GPU idle-spinning far exceeds the additional electricity consumed by the storage layer.

Q: Does this optimization apply to all scenarios? A: No. For short-context, low-concurrency, or latency-insensitive scenarios, the benefits of external KV Cache are limited. Baseline measurements should be taken on your own workload before optimization. Mingxin offers approximately 10 weeks of joint testing to verify whether TTFT reduction and throughput improvement fall within the measured band on actual workloads.

References

1. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
2. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
3. NVIDIA DGX SuperPOD - NVIDIA Docs — https://docs.nvidia.com/dgx-superpod/
4. Kubernetes Documentation — https://kubernetes.io/docs/home/