What is Model Sharding?

What Is Model Sharding?

Model Sharding is the practice of dividing a large AI model into smaller segments and spreading those segments across multiple GPUs, servers, or devices. This technique makes it possible to train and run models that are far too large to fit into the memory of any single machine.

To understand why this matters, consider that modern large language models like GPT-4 or Google's Gemini contain hundreds of billions of parameters, the numerical values that define the model's knowledge and behaviour. Storing and processing all those parameters requires more memory than even the most powerful single GPU can provide. Model Sharding solves this problem by splitting the work across a team of machines working together.

Think of it like moving a grand piano. One person cannot carry it alone, but four people can each lift a section and carry it together. Model Sharding applies the same principle to AI models that are too large for one machine to handle.

How Model Sharding Works

There are several approaches to sharding a model, each suited to different situations:

• Tensor Parallelism: Individual layers or operations within the model are split across multiple GPUs. Each GPU processes a portion of the calculations for each layer, and the results are combined. This is effective for very large individual layers.

• Pipeline Parallelism: The model is split sequentially, with different stages of the model assigned to different GPUs. Data flows through each stage like a production line. While one batch of data is being processed at stage two, the next batch can begin processing at stage one.

• Data Parallelism: Complete copies of the model are placed on multiple GPUs, with each GPU processing different batches of data simultaneously. The results are then combined. This is technically replication rather than sharding but is often used in combination with true sharding techniques.

• Expert Parallelism: Used in Mixture of Experts models, where different specialised sub-models, called experts, are placed on different GPUs. Only the relevant experts are activated for each input, reducing the actual computation needed.

In practice, large-scale AI deployments often combine multiple sharding strategies. A model might use tensor parallelism within a single server with multiple GPUs and pipeline parallelism across servers.

Why Model Sharding Matters for Business

For business leaders in Southeast Asia, Model Sharding has practical implications even if you are not building models from scratch:

• Access to larger, more capable models: The most powerful AI models require sharding to run. Without this technique, businesses would be limited to smaller, less capable models. Sharding enables your infrastructure to support the latest foundation models.

• Cost management: Running a very large model on a single enormous server is typically more expensive than distributing it across several smaller, more commonly available machines. Sharding enables more cost-effective hardware configurations.

• Custom model deployment: If your organisation fine-tunes large language models on proprietary data, sharding may be necessary to deploy these customised models efficiently. This is increasingly relevant for businesses building AI-powered products and services specific to ASEAN markets.

• Scaling flexibility: Sharding allows you to scale your AI infrastructure incrementally by adding more machines rather than replacing existing ones with larger, more expensive hardware.

Model Sharding in Practice

Several popular frameworks and tools make Model Sharding accessible without requiring deep expertise in distributed systems:

• DeepSpeed (Microsoft): An open-source library that automatically handles model sharding for training and inference, with sophisticated memory optimisation techniques.
• Megatron-LM (NVIDIA): Designed for training very large transformer models with efficient tensor and pipeline parallelism.
• FSDP (PyTorch): Fully Sharded Data Parallelism built into PyTorch, making sharding accessible within the most popular AI framework.
• vLLM: A high-performance inference engine that supports automatic model sharding for serving large language models.

These tools handle the complex coordination between machines, allowing data science teams to focus on model development rather than distributed systems engineering.

Practical Considerations for ASEAN Businesses

For organisations in Southeast Asia considering large model deployments:

1. Evaluate whether sharding is necessary for your use case. Many effective AI applications can run on models that fit on a single GPU. Only consider sharding when working with models that genuinely exceed single-device memory limits.
2. Use managed services when possible. Cloud providers offer pre-configured instances and services that handle sharding automatically, reducing the engineering burden on your team.
3. Optimise network connectivity between sharded instances. Model Sharding generates significant communication between machines, so high-bandwidth, low-latency connections between nodes are critical for performance.
4. Consider model compression first. Techniques like quantisation can reduce model size by 50-75% and may eliminate the need for sharding altogether, which is simpler and cheaper.
5. Factor in the total cost of running sharded models, including network costs between instances, management overhead, and the engineering time required to maintain a distributed setup.

Model Sharding is a powerful technique that enables businesses to leverage the most advanced AI models available. However, it adds infrastructure complexity, so the decision to use it should be driven by genuine business requirements rather than a desire to use the largest possible model.