As artificial intelligence (AI) continues to advance, the demand for more powerful and flexible computing resources grows. One of the most promising advancements in this space is the implementation of composable GPUs, which allow for the dynamic allocation of GPU resources to best match the requirements of different AI models, ranging from small to massive, and support a variety of tasks, including training and inference workloads. In this write-up, we will explore how composable GPUs cater to the dynamic needs of 8 billion (8B), 70 billion (70B), and 400 billion (400B) parameter models, unlocking new levels of GPU efficiency, scalability, management, and performance optimization.
Understanding Composable GPUs
Composable GPUs are part of a greater technology called composable infrastructure, where computing, storage, and networking resources are disaggregated and then dynamically allocated based on workload demands. For GPUs, this means that instead of having fixed GPU resources dedicated to specific tasks, they are pooled and assigned to servers as needed. This flexibility is particularly valuable in AI and machine learning, where the computational and memory requirements can vary significantly between models.
Matching GPU Resources to ModelSize
AI models come in various sizes, with the number of parameters being a key indicator of their complexity and memory requirements. ComposableGPUs can help optimize the AI infrastructure for different model sizes being deployed:
8B Parameter Models
Small models, such as those with 8 billion parameters, are typically used for tasks that require less computational power and memory.These models can run efficiently on a single high-end GPU or a small cluster ofGPUs. The key benefit of using composable GPUs for these models is the ability to allocate just enough resources to meet their needs without over-provisioning.
For instance, an 8B model might only require 40-80 GB of GPU memory. With composable GPUs, a system can allocate the exact amount of GPU memory needed, optimizing resource utilization and reducing power and costs.
70B Parameter Models
Medium-sized models with 70 billion parameters require significantly more memory and computational power. These models might need between 350-700 GB of GPU memory. In a traditional setup, this would mean using multiple GPUs in parallel, each contributing a portion of the required memory.
Composable GPUs shine in this scenario by enabling seamless scaling. The system can dynamically pool the memory from multiple GPUs to create a single unified memory space that matches the model's requirements.This ensures that the 70B model has sufficient resources without the complexity of managing multiple discrete low GPU density servers.
400B Parameter Models
Large-scale models with 400 billion parameters represent the cutting edge of AI research and development. These models require massive amounts of memory, often exceeding 2-4 TB. Such models traditionally run on large GPU clusters, which can be challenging to manage and optimize.
With composable GPUs, the system can aggregate the memory and computational power of dozens of GPUs to a single server, creating a massive, unified GPU pool. This approach simplifies the deployment and scaling of these enormous models, reducing CapEx and OpEx and ensuring that they have the necessary memory footprint and computational power for optimal performance.
Benefits of Composable GPUs
- Resource Efficiency: By dynamically allocating GPU resources based on the specific needs of each model, composableGPUs minimize waste and maximize utilization. This leads to lower power, cost savings, and better performance.
- Scalability: Composable GPUs make it easy to scale resources up or down, accommodating models of any size. This flexibility is crucial for AI research, where model sizes and requirements can change rapidly.
- Simplified Management: Managing a composableGPU infrastructure is more straightforward than dealing with fixed discrete GPU setups. The dynamic allocation of resources reduces the complexity associated with scaling and resource provisioning.
- Performance Optimization: With the ability to match resources precisely to the model's needs, composable GPUs ensure optimal performance, reducing the risk of bottlenecks and underutilization.
GPU Memory vs. Model Size
The formula to estimate the memory a model takes is:
[#n params] x [precision]/ 8 (to convert bits to bytes) + [25% for overhead]
For example, an 8B model trained at FP16 would take:
8B x 16/8 x 1.25 =20 GB including overheads.
# of GPU vs. Model Size
GPU Cost vs. Model Size
Conclusion
The rise of composable GPUs represents a significant leap forward in AI computing. By providing the flexibility to allocate GPU resources dynamically, this approach caters to the diverse needs of AI models, from small 8B parameter models to massive 400B parameter models. As AI continues to evolve, the ability to efficiently manage and scale GPU resources will be crucial in unlocking the full potential of AI applications. Composable GPUs are poised to play a key role in this exciting future.
