Recent practical evaluations of large-scale Mixture-of-Experts architectures reveal critical insights into distributed training, memory optimization, and multimodal routing behavior. As models expand beyond single-modality text processing, efficiently scaling and fine-tuning MoE systems on constrained hardware remains a significant engineering hurdle. A recent deployment analysis of Qwen3-Omni-30B-A3B, a 35.26-billion-parameter multimodal model, demonstrates how architectural incompatibilities and memory bottlenecks can be systematically resolved. Unlike dense Transformer models that activate all parameters per token, MoE networks route inputs through a learned dispatcher that selects only a subset of specialized subnetworks. This conditional computation drastically reduces per-token overhead while enabling massive parameter scaling. However, this data-dependent routing fundamentally conflicts with conventional parameter-sharding frameworks like DeepSpeed ZeRO-3 and PyTorch FSDP. During forward passes, these frameworks cannot predict which expert weights will be required, forcing full weight gatherings that negate memory savings and trigger out-of-memory failures across standard configurations. To enable fine-tuning on a four-GPU A100 80GB setup, researchers abandoned parameter sharding in favor of keeping complete MoE layers intact per device. This approach was stabilized through a three-stage memory reduction pipeline: 4-bit NF4 quantization of frozen base weights via QLoRA, tiled attention allocation using Flash Attention 2 to prevent quadratic memory spikes, and a custom output-layer hook that slices hidden states before full vocabulary projection. Together, these optimizations reduced peak per-device memory to 15.6 GB, allowing stable training across sequences exceeding 28,000 tokens. Routing analysis across the model’s 48 layers uncovered a clear architectural transition in multimodal processing. In the first 28 layers, expert activation patterns remained highly modality-specific, consistently grouping audio, video, and text tokens into separate computational pathways. Beyond layer 28, routing entropy decayed as the dispatcher shifted toward semantic differentiation. Audio and video streams maintained stable expert assignments throughout the network, while text tokens exhibited high routing volatility, dynamically reallocating to different experts as contextual reasoning deepened. Because the routing tables were already highly converged before task-specific adaptation, freezing the dispatcher during fine-tuning proved optimal, eliminating auxiliary balancing losses and preserving the model’s learned specialization. These findings carry direct implications for the broader MoE ecosystem. While Expert Parallelism remains the theoretically ideal distributed strategy, current framework implementations lack seamless pretrained checkpoint compatibility, delaying its widespread adoption for fine-tuning workloads. Future advancements must prioritize router stability monitoring, adaptive token selection for long multimodal sequences, and evaluation protocols that isolate structural formatting failures from genuine reasoning deficits. As MoE architectures become standard for next-generation AI systems, resolving these distributed training and routing optimization challenges will be essential for unlocking their full parameter-scale potential.