AI & computing optimization
More performance from every compute resource
4colors treats AI infrastructure as one scheduling and allocation problem. Its algorithms assign GPUs, memory and workloads for throughput and cost.
Built on the scheduling engine behind new best-known schedules on a public benchmark.
A faster model needs more than fast hardware
An inference server can be busy all day and still keep users waiting. Batching requests may improve accelerator utilization, yet delay a short interactive query behind longer work.
Moving a job to another device may free compute but increase data-transfer time or hit a memory limit.
The algorithms surrounding a model can be as important to service performance as the model's raw execution speed.
Seven jobs on four nodes of eight GPUs; a job cannot span nodes.
Idle GPUs8
Jobs waiting1
Illustrative example, not customer data.
The scheduling decisions inside a computing system
GPU and workload scheduling
Assign tasks to accelerators with different capacity, memory and availability. Balance job priorities, queue times, utilization and the cost of switching or transferring work. Long training runs and short jobs share the cluster without starving each other.
LLM training and inference infrastructure
Optimize admission, batching and placement for AI workloads with different latency and throughput requirements. Keep accelerators busy while each request still meets its service target, as traffic rises and falls through the day.
Data-center operations
Coordinate compute capacity, maintenance windows and energy-intensive workloads within power and cooling limits. Connect workload scheduling to operational efficiency without treating every data center as the same problem.
How 4colors fits
From the solver engine to ready-made planning apps, pick the level that suits your team.
4colors Optimizer
The solver engine behind every 4colors product. A portfolio of search methods for hard planning decisions in routing, loading, scheduling and allocation, from Python or the API.
Optimizer for Scheduling
Advanced scheduling algorithms for the software you build to manage jobs, machines, people and resources. Due dates, setups and shared resources sit in one model.
Planning Applications
Takt, Roster and Stride bring optimization to production, shift and project planning. When a rush order lands or someone calls in sick, each re-plans within your rules.
Custom Optimization
Original models and algorithms built around your own resources, rules and objectives. We work with your team from the first prototype to a planner that runs in your systems.
Scheduling compute at scale
Explore more use cases- Industry article
Computing Infrastructure: Scheduling the Invisible Work
The quality of a digital service can depend on decisions made before the application responds.
Read more - Insight
Why Data Center Scheduling Is an Optimization Problem
A computing cluster is full of local choices whose effects are felt elsewhere in the queue.
Read the insight - Use case
Scheduling the Work Behind an AI Response
The user asks one question. The server is deciding what to do with thousands of others.
Explore the use case - Insight
The Cost of a Slow Decision
The value of a better optimization answer can vanish if it arrives after the decision deadline.
Read the insight - Insight
Feasible, Optimal and Good Enough: What Does a Solver Result Mean?
A feasible schedule obeys the rules; whether it is the schedule people want to use is another question.
Read the insight - Guide
Integrating Optimization into Existing Software
Calling an optimization solver from an application is easy to describe as one API request and much harder to make reliable in production.
Read the guide
Find the expensive decision behind the workload
Discuss a computing optimization problemExplore Custom Optimization