Tensor Parallelism vs. Pipeline Parallelism: AI Model Parallelization on Multiple GPUs

Parallelizing AI Models on Multiple GPUs: Tensor Parallelism vs. Pipeline Parallelism

Modern AI models are growing ever larger, often exceeding the memory (VRAM) of a single GPU. To train or run these models, parallelization across multiple GPUs is essential. Two main strategies have emerged: Tensor Parallelism (TP) and Pipeline Parallelism (PP). Both distribute the computation, but in fundamentally different ways, with distinct impacts on latency, throughput, and hardware requirements. This article explains how each works, the overheads involved, and the optimal use cases for each approach.

Pipeline Parallelism (PP): Layer-Based Division

In Pipeline Parallelism, the model is split vertically: the layers of the neural network are distributed across different GPUs. For example, GPU 0 handles the first 10 layers, GPU 1 the next 10, and so on. Data flows through the GPUs like an assembly line. GPU 0 computes its layers and passes the activations to GPU 1. This introduces an inherent problem: the pipeline bubble. Since each GPU must wait for the previous one to finish, idle time occurs. In a simple configuration, GPUs often sit idle waiting for data, reducing overall efficiency. However, this drawback is mitigated when processing batches (multiple requests simultaneously). When many requests arrive in sequence, the pipeline can be filled: while GPU 1 is still processing request A, GPU 0 is already working on request B. This masks the idle time and significantly boosts throughput. PP is therefore well-suited for high batch throughput scenarios where single-request latency is less critical.

Tensor Parallelism (TP): Matrix-Based Division

In contrast, Tensor Parallelism splits the model horizontally by distributing the individual matrix operations within each layer across multiple GPUs. Each GPU holds only a portion (shard) of the weights of every layer. To compute the final result of a layer, all GPUs must collaborate: they perform partial calculations and then combine them via a synchronization operation called all-reduce. This requires constant communication between GPUs – at virtually every layer. The resulting communication overhead is the critical factor for TP performance. The speed of this communication heavily depends on the interconnect technology used. NVLink, a dedicated high-speed link between GPUs, enables data transfers in a few microseconds, making the overhead negligible. When using the slower PCIe bus, however, data transfer becomes the bottleneck. PCIe bandwidth is significantly lower than GPU internal memory bandwidth. The time spent waiting for data exchange can consume a large fraction of the total computation time – often between 25% and 50% in extreme cases. This can negate the benefits of parallelization. A typical all-reduce over PCIe can take several milliseconds, while the actual computation takes only microseconds. Without NVLink, TP is often inefficient.

Comparison and Overhead

Conclusion

The choice between Tensor Parallelism and Pipeline Parallelism depends heavily on the available hardware and performance goals. If GPUs are connected via NVLink and single-request latency must be minimized, TP is the superior choice. It enables fine-grained parallelization and low latency, but requires a very fast interconnect. If only PCIe connections are available, or the model is distributed across multiple servers, PP is the more robust and efficient option. The pipeline bubble is mitigated by batching, and communication overhead remains low. In practice, both techniques are often combined (e.g., TP within a node using NVLink and PP between nodes over network) to leverage the advantages of both. Understanding these differences is crucial for efficient utilization of multi-GPU systems in AI.