fused_experts() is called 64 times per decode step (once per MoE layer).
Each call allocated 3 intermediate tensors via torch.empty = 192 mallocs.
For decode (M=1, topk=8), all 64 calls use identical shapes.
Fix: module-level _moe_intermediate_cache dict that reuses tensors when
shapes match. First layer call allocates, subsequent 63 calls reuse.
Saves 189 CUDA mallocs per decode step = 74,655 mallocs/second at 395 TPS.
Design follows CCCL's dispatch_reduce.cuh pattern: pre-allocate temp_storage
once via alias_temporaries, reuse across kernel invocations.
No functional change — tensors are .empty() (uninitialized), overwritten
before use by ixformer kernels.
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