Defend your GPU count when finance expects the MoE bill to track active FLOPs.

**MoE serving cost** has two axes that finance often confuses. Total parameters set how much memory you need. Active parameters set how fast each token is computed. When DeepSeek-V3 says 37B of 671B parameters activate per token, that 37B is compute, not storage. Routing decides per token, per layer which experts run. Any token can pick any expert, so every expert must already be loaded in GPU memory. You cannot stream them in at request time without destroying latency. That memory floor is what fixes the GPU count. You need enough VRAM across the fleet to hold all 671B parameters plus KV cache plus activation buffers. Once the fleet exists, the 37B active-FLOPs figure tells you how many tokens per second each GPU can produce. That is where MoE earns its keep: dense-671B quality at the throughput of a much smaller model. The honest pitch to finance: this looks like a big model on the hosting bill and a small model on the per-token cost. Quality per dollar, not hardware count alone, is the right metric.

**Fleet size for an MoE is a memory question. Per-token cost is a FLOPs question. Conflating them is the finance bug here.** The MoE router picks experts per token, per layer, dynamically. There is no static subset you can host: any future token might route to any expert. That means every parameter sits in VRAM all the time. For DeepSeek-V3 at 671B parameters in fp8, the weights alone are roughly 670 GB, plus KV cache that scales with concurrent users and context length, plus activation and routing buffers. That sets the GPU count floor before a single request lands. Once the fleet exists, the 37B active figure does pay off. Per-token compute is a sparse 37B forward, not a dense 671B forward, so each GPU can serve far more tokens per second than it could if you were hosting a true 671B dense model on the same hardware. Throughput per dollar is where the win shows up. There are also secondary costs: expert-parallel all to all dispatch between GPUs, capacity headroom to absorb routing skew without dropping tokens, and KV cache memory that grows with concurrency. The right finance metric is quality per dollar or cost per token at target quality, not GPU count compared to active-parameter count.