AI Workloads. Routed Globally.
From Compute to Tokens, Workloads and AI Applications.
Tell us what needs to be computed — not what infrastructure you think you need.
MACOMX matches AI workloads with suitable compute, models and execution infrastructure based on cost, performance, location, availability and compliance requirements.
- Lower Cost for Business.
- More Workloads for Capacity.
- Better Efficiency for AI Infrastructure.
Workloads we route
Each type asks something different of the infrastructure underneath it.
AI Agents
Multi-step tasks that call models and tools repeatedly. Sensitive to latency and to the cost of each call.
LLM Inference
Serving model responses at volume. Priced per token and bound by latency and throughput.
Coding AI
Code generation and review. Long contexts and demand that arrives in bursts.
Video & Multimodal
Generating or understanding image, video and audio. Heavy on memory and bandwidth.
RAG & Embedding
Retrieval, embedding and vector search over private data. Where the data sits often decides the route.
Batch AI
Large offline jobs where the completion time is flexible and cost matters most.
Fine-Tuning
Adapting a base model to your own data. Needs short, well-connected blocks of GPU time.
Training
Pre-training and large-scale training. Long-running, interconnect-bound GPU clusters.
One Workload. Multiple Execution Options.
MACOMX profiles, compares and routes each workload to a suitable execution path.
- Profile
The workload is described: model, volume, latency, data location and compliance constraints.
- Compare
Execution options across compute, models and regions are compared on cost, performance and availability.
- Route
The workload is assigned to the execution path that meets its requirements.
- Execute
The workload runs, and delivery is checked against the agreed terms.
Submit a Workload
Describe the outcome you need. Models, hardware and regions can be worked out together.