Post
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We have covered Tensor Parallelism for slicing matrices and Pipeline Parallelism for stacking layers. But what if your model isn't just deep or wide—it's a sprawling Mixture-of-Experts (MoE) architecture like Mixtral or DeepSeek, with trillions of parameters that are mostly idle per token?
Replicating those experts wastes VRAM. Slicing them with TP wastes bandwidth. The solution is Expert Parallelism (EP), which distributes the experts themselves across GPUs and routes tokens to wherever their "chosen" expert lives.
The hardware catch? It is not matrix splitting or pipeline bubbles—it's the "Router's Dilemma." You must shuffle massive volumes of tokens across the cluster using All-to-All communication, and any imbalance can leave expensive GPUs idle.
My latest guide dives into the mechanics of EP and why the interconnect becomes the ultimate bottleneck.
In this breakdown, we explore:
The Token Routing Lifecycle
A four-step hardware flow: Local routing to pick experts, Dispatch (All-to-All shuffle), Expert computation on the "home" GPU, and Combine (another All-to-All to return results).
The All-to-All Primitive
Unlike the ring-based syncs in TP, All-to-All creates a dense mesh of personalized data transfers. We compare it to All-Reduce and show why uneven token distribution (load imbalance) causes network congestion and compute skew.
Load Balancing: The Hardware Nightmare
If one expert gets 90% of the tokens, its GPU bottlenecks while others stall. We discuss mitigation strategies like token dropping and auxiliary losses to keep utilization high.
The article includes a raw PyTorch implementation of an EP layer using torch.distributed.all_to_all_single to reveal exactly how the data shuffles and where the stalls happen.
Read the full hardware-centric guide here:
https://flozi.net/en/guides/ai/scaling/expert_parallel
Replicating those experts wastes VRAM. Slicing them with TP wastes bandwidth. The solution is Expert Parallelism (EP), which distributes the experts themselves across GPUs and routes tokens to wherever their "chosen" expert lives.
The hardware catch? It is not matrix splitting or pipeline bubbles—it's the "Router's Dilemma." You must shuffle massive volumes of tokens across the cluster using All-to-All communication, and any imbalance can leave expensive GPUs idle.
My latest guide dives into the mechanics of EP and why the interconnect becomes the ultimate bottleneck.
In this breakdown, we explore:
The Token Routing Lifecycle
A four-step hardware flow: Local routing to pick experts, Dispatch (All-to-All shuffle), Expert computation on the "home" GPU, and Combine (another All-to-All to return results).
The All-to-All Primitive
Unlike the ring-based syncs in TP, All-to-All creates a dense mesh of personalized data transfers. We compare it to All-Reduce and show why uneven token distribution (load imbalance) causes network congestion and compute skew.
Load Balancing: The Hardware Nightmare
If one expert gets 90% of the tokens, its GPU bottlenecks while others stall. We discuss mitigation strategies like token dropping and auxiliary losses to keep utilization high.
The article includes a raw PyTorch implementation of an EP layer using torch.distributed.all_to_all_single to reveal exactly how the data shuffles and where the stalls happen.
Read the full hardware-centric guide here:
https://flozi.net/en/guides/ai/scaling/expert_parallel