AI ENGINEERING
Train large models faster. Make better use of your compute.
Training large models demands substantial compute. Fitting a model into GPU memory is a major challenge—but efficient training also depends on how computation, memory, and communication work together.
FROM ANALYSIS TO IMPLEMENTATION
Different architectures call for different optimizations. We tailor configurations, parallelism strategies, and kernels to your model and cluster.
For example, we combine data, pipeline, sequence, and expert parallelism to fit the workload. For Mixture-of-Experts (MoE) models, we also optimize how tokens are routed and processed across experts, developing custom kernels where they make a meaningful difference.
Reinforcement learning (RL) workflows can combine training, inference, multiple models, and execution environments such as sandboxes. Each component has different resource needs, and performance depends on how work and data move between them.
We tune resource allocation, communication, and scheduling across the workflow to balance load. This helps reduce idle time when one stage waits for another, keeping CPUs and GPUs productive.
GPU memory bandwidth, CPU overhead, and communication all shape training performance. At cluster scale, these factors need to be understood together.
We profile the full training workload, identify bottlenecks, and implement improvements across the model and the systems it runs on.
LET'S WORK TOGETHER
Our researchers and engineers bring broad, hands-on experience in AI systems optimization, including distributed workloads spanning thousands of GPUs.
We work with your team from performance analysis through implementation, with improvements measured against your actual workloads.
Projects are scoped and quoted individually.