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TechnoLynx helped a client reduce AI inference costs by modelling how common AI operations perform across different GPU architectures, so they could predict performance, choose the most cost-effective GPU for each task, and reduce running costs without sacrificing performance.
As the client's AI models grew more complex, inference costs became a critical problem. They were running multiple models across a wide range of GPU architectures, and needed a reliable way to predict inference performance on different GPU topologies, so they could reduce running costs while keeping performance high.
Inference costs became a critical issue.
The client sought a way to reduce these costs by optimising their use of GPUs.
A wide range of GPU architectures to optimise.
The client was running multiple models across many GPU architectures, each with different strengths and weaknesses.
Some hardware features didn’t boost their AI tasks.
They wanted to understand which GPU features were essential for their work and which were unnecessary.
They needed a way to predict inference performance.
They wanted to predict the inference performance of various models on different GPU topologies to reduce running costs without sacrificing performance.
From identifying critical inference operations to a predictive model and GPU measurement tooling
Defined the optimisation goal: reduce inference costs by understanding how GPU topologies impact model performance across the client’s hardware set.
Focused on core AI operations (e.g., convolutions) and recreated them to study behaviour at a low level for reliable modelling.
Built a predictive performance model using Python + OpenCL that accounts for parameters like clock speed, memory bandwidth, compute units, and parallel processing.
Developed a tool to measure characteristics of any OpenCL-capable GPU and benchmark task-specific performance with detailed feedback.
Delivered reports and workshops so the client’s team could make informed GPU decisions and apply the model in future projects.
We built a performance modelling framework that predicts inference behaviour across GPU architectures and clarifies which hardware characteristics matter for specific AI workloads, supported by measurement tooling for OpenCL-capable GPUs.
A model that forecasts how different GPU topologies will perform for specific AI workloads, helping select the most cost-effective GPU per task.
Recreated core AI operations (e.g., convolutions) and modelled their execution behaviour to understand performance drivers across hardware.
A tool that measures characteristics of OpenCL-capable GPUs and benchmarks task performance using factors like clock speed, memory usage, and bandwidth.
The final result was a detailed performance model and two concrete tools: a topology-level inference performance predictor and an OpenCL-capable GPU characteriser. These gave the client a reliable, repeatable way to reason about GPU selection before committing to hardware or cloud spend. The primary value, as the client’s team acknowledged, was the shift in how they thought about GPU cost — moving from intuition and spec-sheet comparison to profiling-led decision-making grounded in measured operation-level behaviour. Reports and workshops transferred that point of view to the development team directly, not just the deliverable.
Predicted AI model inference performance across a wide range of GPU architectures
Built OpenCL-based tooling to measure GPU characteristics and task-specific performance
Improved decision-making on GPU selection by separating essential vs. unnecessary hardware features
Reduced the amount of time and money spent on AI inference and allowed resources to be reallocated to other areas of the business
Delivered reports and workshops so the client’s development team gained a deeper understanding of how their GPUs worked and could better utilise them in future projects
Our services feature expertise in classical computer vision, human-supervised system design for legal compliance, video pipeline optimisation with tools like FFmpeg, custom adaptable models, and explainable AI for ethical transparency.
We are leaders in generative AI, offering optimised inference for faster deployments, ethical AI systems with bias mitigation, intelligent automation for adaptive workflows, and advanced simulation and prototyping capabilities.
We deliver immersive XR solutions with cross-platform development (Unity 6), GPU performance optimisation, and expertise in NVIDIA Omniverse and CloudXR. We also use reinforcement learning for intelligent XR environments.
Let’s discuss how performance modelling and GPU optimisation can help you run faster, spend less, and scale reliably across real-world workloads.
