As models grow in size, so do infrastructure demands. Running large language models, computer vision systems, or multi-agent orchestration at scale quickly becomes expensive—especially when deployed in production environments with real-time constraints.
But bigger models don’t have to mean bigger costs. With the right architecture and optimisation strategies, it’s possible to scale capability without scaling operational burden.
At Miniml, we focus on designing AI systems that are efficient, performant, and sustainable to run—without cutting corners on reliability or capability.
The Challenge: Power vs Performance
In many AI projects, infrastructure cost becomes a bottleneck. This shows up as:
- GPU usage that spikes with latency
- Inference times that don’t meet SLAs
- Memory overhead that makes local or edge deployment impractical
- Training pipelines that require prohibitively large compute clusters
When performance gains flatten while costs grow, optimisation becomes the most strategic investment you can make.
Strategies That Work
Here are three proven approaches to reducing the footprint of AI models—without compromising value.
1. Quantisation-Aware Training (QAT)
Quantisation reduces the bit-width of model weights and activations (e.g. from 32-bit to 8-bit), dramatically reducing memory usage and improving inference speed.
Unlike post-training quantisation, QAT introduces these constraints during training, allowing the model to adapt. The result: lightweight models with minimal accuracy degradation.
✅ Benefits:
- Up to 4x reduction in model size
- Faster inference, especially on CPUs or edge devices
- Lower energy and cooling requirements
2. Sparse Attention and Pruning
Large models like transformers suffer from quadratic complexity in their attention layers. By introducing sparse attention mechanisms, it’s possible to retain accuracy while computing fewer attention weights.
Similarly, structured pruning can remove redundant weights or neurons from overparameterised networks—reducing size and latency while maintaining output quality.
✅ Benefits:
- Reduced GPU memory footprint
- Faster training and inference
- Support for long-context modelling with lower compute
3. Architecture Reparameterisation
Sometimes, optimisation means rethinking the architecture itself. Techniques like low-rank adaptation (LoRA), grouped convolutions, or Mixture-of-Experts (MoE) allow for more efficient parameter usage and compute allocation.
Rather than compressing a model post hoc, these designs build efficiency into the foundation—optimising for both performance and scale from the start.
✅ Benefits:
- Modular, extensible models
- Fine-tuned compute allocation across tasks
- Flexibility for both local and distributed deployment
Deploying Efficiently
Optimised models are only valuable if deployed correctly. That means:
- Using asynchronous or batched inference for high-throughput applications
- Leveraging CPU acceleration where possible (especially with quantised models)
- Monitoring throughput, latency, and cost metrics continuously
- Selecting the right deployment environment—cloud, edge, hybrid—for your scale
Our approach ensures that every optimisation works in the context of your stack—not just in theory.
Smarter Models, Smarter Operations
Reducing model size isn’t about cutting capability—it’s about building systems that are lean, fast, and fit for use at scale. Whether you’re deploying LLMs across customer workflows or running real-time inference on sensor data, model optimisation is the key to sustainable AI.
→ Talk to the Miniml team about optimising your models for scale
