An infographic on the efficient architectures, powerful compression techniques, and strategic deployment of SLMs for specialized AI applications.
SLMs often use the core Transformer architecture but with crucial modifications like fewer layers, smaller hidden dimensions, and shared parameters (e.g., ALBERT) to reduce size and computation from the ground up.
Instead of one giant network, MoE uses multiple smaller "expert" sub-networks. For any given input, only a few relevant experts are activated, drastically reducing the computational cost per inference while maintaining high capacity.
A newer class of architecture (e.g., Mamba) that processes sequential data linearly. This avoids the quadratic complexity of Transformer attention, enabling much faster inference and handling of extremely long contexts efficiently.
This technique identifies and removes redundant or unimportant neural network connections (weights), much like pruning a tree. This creates a "sparse" model that is smaller and faster without significant accuracy loss.
Reduces the numerical precision of the model's weights (e.g., from 32-bit floating point to 8-bit integers). This shrinks the model's memory footprint and can accelerate computation on compatible hardware.
A larger "teacher" model trains a smaller "student" model. The student learns to mimic the teacher's outputs, effectively transferring the knowledge into a more compact form suitable for deployment.
Running the SLM directly on user devices like smartphones or laptops.
Deploying the model on local hardware near the data source (e.g., IoT gateways, smart cameras).
Running the SLM in the cloud on smaller, more cost-effective virtual machines.