Why Macs lose to DGX Spark at batched inference: bandwidth wins solo, FP4 compute wins crowds
QuixiAI · x · 2026-10-29
A technical thread on local LLM hardware: with one user, decode is memory-bound and the Mac's 1.2TB/s bandwidth shines, but batching amortizes weight reads across users and shifts the bottleneck to compute, where the Spark's Blackwell tensor cores with native FP4 dominate. QuixiAI adds that concurrency matters even for a single user running subagents, recommending lucebox or a quad B70 workstation over a DGX Spark, with only the network adapter as a nit.
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