Model & Training Configuration
Architecture configuration, hyperparameter selection, model partitioning, Mixture-of-Experts configuration and parallelism strategy.
AI & Foundation Models
Optimize the model and the infrastructure together.
Foundation-model training is not one optimization problem. It is a system of coupled optimization decisions — architecture, compute, scheduling and communication — that constrain one another.
The challenge
Why this decision gets combinatorially hard.
Architecture choices determine memory and communication needs; GPU allocation constrains what schedules are feasible; training schedules and gradient-communication paths interact with both. Tuning each in isolation wastes scarce, expensive compute and lengthens time-to-train. These are combinatorial decisions hiding inside a pipeline that looks like pure machine learning.
What we optimize
Concrete decisions, mapped to products.
Compute Resource Optimization
GPU and accelerator allocation, memory allocation, workload and model-shard placement, heterogeneous compute assignment.
Training & Experiment Scheduling
Pretraining, fine-tuning, evaluations, hyperparameter trials, checkpoints and scarce-cluster scheduling.
Distributed Training Optimization
Communication paths, GPU-to-GPU traffic, gradient communication and expert routing across distributed compute networks.
Composed solution
TrainIQ — jointly optimized AI infrastructure.
TrainIQ is QuGradient's Quantum AI solution for jointly optimizing foundation-model architecture, training configuration, compute resources, scheduling, placement and distributed communication — as one coupled system rather than four disconnected tools.
Powered by DesignIQ + ResourceIQ + ScheduleIQ + RouteIQ
Product composition
Composed from QuGradient products.
Highlighted products are the ones this industry composes most often. A specific engagement may use one, several or all four.
Potential business outcomes
The KPIs we optimize toward.
Training cost
Time-to-train
Cluster utilization
Throughput
Effective compute
Outcomes are benchmarked on your own data against your current approach. We don't promise unsupported percentages.
How we engage
Assessment → Proof-of-Value → Integration.
Optimization Assessment
Map the decision, objectives, constraints, data readiness and baseline to candidate products.
Proof-of-Value
Benchmark QuGradient against your current solver, process or model — on your own data.
Deploy & Integrate
Integrate the successful capability into your applications, data platforms and compute.
Bring us the problem.
Share the decision, your objectives and constraints, and a representative slice of the data. We'll map it to the right products and tell you whether a benchmark or proof-of-value makes sense.
Discuss your AI infrastructure problem →