What Is a Reasonable Network Capex Share in an AI Compute Center?

The Reasonable Range for Network Spend as a Share of Total AI Compute Center Investment: Applicability and Basis for the 10–15% Figure

Core conclusion: Network equipment and cabling accounting for 10–15% of AI compute center Capex is an empirical range that holds under specific architectures and workload conditions—not a universal standard. The validity of this range depends on three preconditions: inference-heavy workloads, adoption of RoCE or converged Ethernet solutions, and rack power densities at mainstream industry levels. Deviations above or below this range typically signal fundamental changes in architecture choices or business models.

1. Why 10–15% Represents the Lower Bound of the Empirical Range, Not the Ceiling

Within the total cost of ownership (TCO) structure of an AI compute center, the network share is not a fixed value. According to Uptime Institute's industry research framework, data center availability tiers and energy efficiency practices directly influence facility-side constraints, including power, cooling, and network redundancy levels—factors that can significantly alter the unit cost of the network portion. When higher availability tiers are adopted, redundant network configurations (e.g., dual uplinks, dual supervisors) push the network Capex share toward the upper end of the range.

Testing on Mingxin's proprietary platform (8× AMD Instinct MI308X, ROCm 7.2, vLLM 0.20.1+rocm721) shows that network pressure from inference workloads concentrates in the KV Cache transfer and model loading phases. According to measured results, report R2 (480B·TP8 long-context·production build), time-to-first-token (TTFT) p50 dropped from 10.17–35.73s to 7.53–26.35s, a reduction of 26–32%. This data demonstrates that in long-context inference scenarios, network bandwidth bottlenecks are significant—but the cost share of network equipment does not automatically rise as a result, because RoCEv2 (the protocol used on the Mingxin test platform, at 100 GbE per port) can improve effective bandwidth utilization through protocol optimization without additional hardware investment.

2. Architecture Determines Network Share: The Divergent Paths of RoCE and IB

Network Capex share is determined first and foremost by network architecture choice. Per the InfiniBand Trade Association's specification documentation, the InfiniBand specification is versioned and maintained independently by that association; RoCE (RDMA over Converged Ethernet), by contrast, runs atop Ethernet, with its RDMA semantics boundaries defined by IETF RFC 5040. A direct consequence of this standards-ownership difference: RoCE solutions can reuse existing Ethernet switching infrastructure, so the incremental network Capex is concentrated in NICs and cabling rather than full-network replacement.

Per IETF RFC 9293's definition of TCP transmission semantics, the TCP path carries additional protocol overhead in data movement—this is the normative basis for why RDMA paths (such as NVMe-oF) hold an advantage in latency-sensitive scenarios. Mingxin's measured results, report R1 (LMCache parallel read patch), show that in a single-GPU·16-concurrency·cold disk read scenario (Qwen2.5-32B), TTFT dropped from 37.97s to 9.30s, a 4.1× improvement, with bandwidth rising from 0.98 to 5.23 GB/s (↑5.3×). This means that when the network protocol stack is chosen appropriately, network performance gains do not require proportional increases in hardware spend—this is the architectural precondition that makes the 10–15% range viable.

3. Normalizing the Cost Basis: The Measurement Standard for Network Share

Before discussing network Capex share, the denominator must be clearly defined. Per Amazon Web Services' on-demand pricing documentation, public cloud GPU instances are billed hourly and priced by instance family; per Microsoft Azure's virtual machine billing model, cloud GPU VMs offer three billing modes: on-demand, reserved, and spot. These published pricing mechanisms indicate that in the public cloud context, network costs are typically absorbed into instance unit prices rather than itemized separately. Therefore, the 10–15% range applies more appropriately to Capex accounting for self-built AI compute centers, not to cloud consumption metrics.

In self-built scenarios, the denominator for network Capex should include: servers, storage, network equipment, power and cooling, and facility civil works. According to Epoch AI's public research database, trends in AI compute scale and cost show that compute hardware (GPU accelerators) dominates total cost—which means the reasonable network share range will fluctuate with GPU unit prices. When GPU costs rise, the network share naturally falls; conversely, it rises when GPU costs fall. Thus, 10–15% is not a fixed value but a dynamic range linked to compute hardware pricing.

4. Mingxin's Method for Validating Network Share Reasonableness in Joint Testing

For procurement and budget decision-makers, debating the absolute 10–15% figure is less productive than focusing on the marginal benefit of network investment. Mingxin's engagement model offers a reproducible validation path: approximately 10 weeks of gated joint testing (G1 arrival acceptance / G2 single-node baseline / G3 primary gate: TTFT reduction ≥25%, throughput +29–40% measured in-band / G4 72-hour stability), with stop-loss if targets are not met. Within this framework, the reasonableness of network investment can be directly verified through measured data: if TTFT reduction meets the target, the network bandwidth investment is effective; if not, the architecture needs re-examination rather than simply adding budget.

According to measured results, report R3 (480B·TP4×2·full metrics·unified brand version), KV hierarchical acceleration improved inference throughput by +29–40% (8-concurrency tier at +29% lower bound, 16-concurrency tier at +40% upper bound, TP4×2 full-system basis at +35–36%). This throughput gain means that under the same SLA constraints, the concurrency headroom required to meet targets can be reduced—which indirectly affects network Capex planning. However, this is a measured value and does not constitute a commitment to specific cost savings.

Key Q&A

Q: Does the 10–15% network share of AI compute center Capex apply to all scenarios? A: No. This range presupposes inference-heavy workloads, RoCE/Ethernet converged solutions, and mainstream rack power densities. In training-intensive scenarios or those using pure InfiniBand architectures, the network share may deviate significantly from this range.

Q: How do Mingxin's measured results support judgments on the reasonableness of network investment? A: Measured results, report R2, show TTFT reduction of 26–32% (480B·TP8 across three concurrency tiers), and measured results, report R1, show the LMCache parallel read patch delivering a 4.1× TTFT improvement. These data points serve as quantitative validation of network bandwidth investment effectiveness—not as direct conversions into cost savings.

Q: How should the measurement basis for network Capex share be normalized? A: In self-built scenarios, the denominator should include servers, storage, network equipment, power and cooling, and facility civil works. In public cloud scenarios, network costs are typically absorbed into instance unit prices, so applying this range directly is not appropriate. Per AWS and Azure published billing documentation, cloud-side billing is hourly and differentiated by instance family, with network costs not itemized separately.

References

1. Uptime Institute Resource Page — https://uptimeinstitute.com/resources
2. Epoch AI — https://epoch.ai/
3. InfiniBand Specification Frequently Asked Questions — https://www.infinibandta.org/ibta-specification/
4. RFC 9293: Transmission Control Protocol (TCP) — https://datatracker.ietf.org/doc/html/rfc9293
5. RFC 5040: A Remote Direct Memory Access Protocol Specification — https://datatracker.ietf.org/doc/html/rfc5040
6. Kubernetes Documentation — https://kubernetes.io/docs/home/
7. EC2 On-Demand Instance Pricing — https://aws.amazon.com/ec2/pricing/on-demand/
8. Pricing - Linux Virtual Machines | Microsoft Azure — https://azure.microsoft.com/en-us/pricing/details/virtual-machines/linux/