Inference is where the magic happens. During inference, the model is not learning new things, but using it’s already-learnt knowledge (ganined during training) to predict the most likely words to follow your prompt step-by-step.
Generating responses needs significant computing resources, especially for complex tasks or long inputs. To make inference faster and more efficient, techniques like parallelism are used. Parallelism involves splitting the workload across multiple GPUs to speed up computations.
Three key parallelism techniques: Pipeline parallelism, Tensor parallelism, and Sequence parallelism. Each technique divides the work differently to optimize performance.
1. Pipeline Parallelism
Imagine constructing a skyscraper where each specialized crew handles only specific floors. This is pipeline parallelism for LLM inference. Instead of forcing a single GPU to process all transformer layers sequentially, the model’s depth is partitioned across multiple devices: early layers run on GPU A, middle layers on GPU B, and so on.
As token sequence flows through this computational assembly line, each device processes its assigned layers before passing intermediate results downstream. This dramatically reduces per-device memory demands, making trillion-parameter models feasible on modest hardware clusters.
But this his carries a subtle tax: pipeline bubbles. When the first token batch completes GPU A’s layers but GPU B is still busy, idle cycles emerge. Like workers pausing between relay baton handoffs. Sophisticated inference engines mitigate this through micro-batching, processing multiple sets of tokens at the same time to keep all GPUs working efficiently.
2. Tensor Parallelism
When individual transformer layers grow too wide, to fit within a GPU’s memory or compute limits, tensor parallelism takes the stage. Here massive matrix operations are fractured within a single layer across devices.
Picture a jigsaw puzzle divided among specialists: GPU 1 computes the first quarter of neuron activations, GPU 2 the next, and so forth. These shards must later recombine via all-reduce operations (a collective communication pattern where all devices contribute to a unified result), introducing synchronization overhead.
This technique shines for models with extreme layer widths but demands high-bandwidth interconnects like NVLink to avoid drowning in communication costs. NVLink is a high-bandwidth, direct GPU-to-GPU interconnect technology developed by NVIDIA that enables fast data exchange between GPUs. It’s essential for tensor parallelism in AI workloads because it allows efficient communication when matrix operations are split across multiple GPUs, preventing performance bottlenecks.
Unlike pipeline parallelism’s sequential handoffs, tensor parallelism is intensely communication-bound—each forward pass requires multiple data exchanges between devices. Yet when paired with pipeline parallelism (e.g., 4-way tensor splits within 8-way pipeline stages), it unlocks the ability to distribute even the bulkiest layers across dozens of GPUs without duplicating weights, turning computational bottlenecks into parallelized throughput.
3. Sequence Parallelism
LLMs often process sequences of data, such as long pieces of text. Sequence parallelism focuses on splitting these sequences into smaller chunks and processing them in parallel across multiple devices.
For instance, a long input text can be divided into smaller segments (like sentences or paragraphs), and each segment is processed on a different GPU. since language models rely on context (e.g., earlier words in a sentence), the processing must account for dependencies between segments. Techniques are used to ensure that the context is preserved across the splits.
This is particularly helpful for long inputs, as it reduces the time needed to process the entire sequence by handling parts of it simultaneously.
Combining Techniques for Greater Efficiency
In practice, these parallelism techniques are often combined to maximize performance:
Pipeline parallelism can be used alongside tensor parallelism to split both the model’s layers and its large tensors across devices. Sequence parallelism can be integrated to handle long inputs more efficiently while the model is already distributed using the other techniques.
By combining these approaches, LLM inference becomes faster and more scalable, enabling the use of larger models and longer inputs without overwhelming compute.
Why These Techniques Matter
Parallelism is crucial for making LLMs practical and efficient, especially as models grow in size and complexity. Without these techniques, running inference on large models would be slow and resource-intensive, limiting their usability. By distributing the workload across multiple devices, parallelism allows LLMs to: 1) Handle larger models that wouldn’t fit on a single device. 2) Process inputs faster by overlapping computations. 3) Scale to longer sequences without a proportional increase in computation time.