Over the course of my career in AI infrastructure and solution architecture, I've reviewed a lot of vendor proposals. Proposals for GPU clusters, cloud AI platforms, hybrid infrastructure architectures, MLOps platforms. The vendors vary. The configurations vary. The pricing varies considerably. The mistakes are remarkably consistent.
Here are the six failures I see most often, and what to look for instead.
Benchmark theatre
Almost every AI infrastructure proposal leads with benchmark results. FLOPS, memory bandwidth, training throughput on standard models. The numbers are impressive. They're also almost entirely useless for your evaluation unless they were generated on a workload that closely resembles yours.
Standard benchmark suites are useful for comparing hardware within a category. They're not useful for predicting how hardware will perform on your specific model architecture, your specific data pipeline, and your specific inference serving pattern. Ask vendors to run their performance demonstration on your workload, with your data, in your configuration. Vendors with genuinely competitive hardware will accommodate this. Vendors who defer to standard benchmarks are usually concerned about what workload-specific testing would reveal.
Missing the networking story
GPU compute proposals routinely underspecify the networking architecture required to actually realise the stated performance. Distributed training across multiple nodes is network-sensitive. The interconnect between GPU nodes can be the binding constraint on training throughput regardless of how good the individual GPUs are.
A proposal that specifies GPU hardware without an equally detailed networking architecture is an incomplete proposal. Ask specifically what interconnect architecture is assumed in their performance claims, and what happens to training throughput when you scale from eight to sixty-four GPUs on that network fabric.
Optimistic power assumptions
Vendor power specifications typically reflect thermal design power, the theoretical maximum under sustained load. Real-world AI training workloads often drive hardware close to TDP for extended periods. Proposals that use vendor TDP numbers for power budgeting are frequently optimistic by fifteen to twenty-five percent.
Ask the vendor for customer case studies with real-world power consumption data at your expected utilisation level. If they don't have this, that tells you something.
Ignoring storage architecture
AI training workloads are storage-intensive. Large models require large datasets, and loading training data fast enough to keep GPUs properly fed is a genuine architectural challenge that many proposals treat as an afterthought. Storage architecture belongs in the proposal at the same level of detail as the compute configuration. If it isn't there, ask for it.
No failure mode analysis
Every proposal shows you what happens when everything works. Almost none of them show you what happens when something fails. In a GPU cluster, hardware failures are operational realities, not exceptional events. DRAM errors, GPU failures, networking faults, and storage failures will happen at scale. A proposal without a clear failure mode analysis and recovery architecture hasn't been thought seriously about production operations.
Year-one economics only
Proposals are almost universally structured around year-one acquisition costs. Software licensing costs in year two, when introductory pricing expires. Maintenance contract costs that escalate after year one. Hardware refresh costs when the generation cycle turns. A proposal that doesn't model three-year economics isn't giving you the information you need to make a real decision. Ask for it explicitly.
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