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For large language model (LLM) inference, prefill/decode disaggregation (P/D) is deployed in production to isolate phases with different resource demands. Attention/feed-forward network disaggregation (AFD) splits decode again, but adds communication, placement constraints, and another provisioned pool. We ask whether role-specific placement repays this cost as models and accelerator roadmaps change. Infrastructure is committed before future models and hardware can be benchmarked. We present SiliconForge, a calibrated model that varies the serving decomposition and hardware assignment independently. We compare P/D and P/D+AFD across four model directions, four accelerator classes anchored to current and announced parts, and two fabrics. We co-optimize each design using the same rules for pipeline overlap and the same power budget. Our main comparisons show no robust deployment-level AFD advantage in service-level objective (SLO) goodput per kW. Most nominal estimates favor P/D. The largest AFD lead comes from fitting one more prefill replica, not from a more efficient decode pool. AFD has a nominal lead in the KV-heavy (key-value) model direction when the co-located expert pool spans several small scale-up domains. Larger domains or a fast fabric for both designs weaken that lead. We therefore find that prefill provisioning, decode memory bandwidth, links, and pool granularity decide deployments more than the split alone. A fixed hardware assignment can lose its advantage as models, fabrics, hardware assumptions, and objectives change. Re-optimizing mappings and pool sizes on the same hardware cuts early commitment's median normalized goodput loss from 36% to 9%. We recommend adjustable mappings and pool sizes to let boundary and hardware choices follow model demands.