Workload Estimator
Estimate GPU costs for AI inference, batch processing, fine-tuning, and training using workload requirements and available infrastructure. Jungle Grid matches the workload to suitable capacity rather than making you manually select a GPU provider first.
Cost estimator
Pick the workload type, model size, and optimization target. Get a live estimate from available nodes with no sign-in required.
01 — Workload type
02 — Model size
03 — Optimise for
Job pricing
You submit a workload — Jungle Grid matches it to live capacity and charges for compute actually used. No reserved instances. No idle billing.
Workload cost drivers
These estimates help teams compare workload economics before execution without pretending every model or runtime has the same infrastructure profile.
Inference cost depends on model size, request volume, runtime, memory requirements, and the infrastructure selected for execution. Short requests and smaller models generally consume less compute than long-running or high-memory inference.
Batch processing cost is shaped by dataset size, per-item execution time, parallelism, and total runtime. Larger datasets and longer runs generally increase the compute consumed by the workload.
Fine-tuning estimates account for model size, training method, dataset, memory requirements, and expected duration. Adapter-based methods and full fine-tuning can have substantially different compute profiles.
Full training workloads vary widely by model scale, hardware requirements, number of steps, and duration. The estimator provides directional planning information rather than a guaranteed final price.
How the estimate works
Jungle Grid is an AI workload execution layer that routes and runs inference, batch, fine-tuning, training, and other AI workloads across available compute infrastructure.
You supply workload requirements, Jungle Grid identifies suitable supported capacity, evaluates viable routes, and returns an estimate. Actual compute remains usage-based and depends on the final execution.
Routing economics
The platform scores cost, latency, reliability, queue depth, and thermal state on every dispatch. You do not manually shop for hardware each time.
Jungle Grid compares live provider-backed capacity instead of forcing one fixed cloud path, so the estimate can reflect healthy infrastructure that fits the request.
The platform scores cost, latency, reliability, queue depth, and thermal state before placement, so a cheap but unhealthy node does not win the route.
You are billed for actual compute used, not reserved guesswork. Short inference requests stay short on the invoice too.
Platform reliability
Fewer than 100 completed jobs in the last 30 days — metrics not yet statistically meaningful.
Estimator FAQ
Cost depends on workload type, model size, runtime, memory requirements, and the infrastructure matched for execution. Use the estimator for a workload-level range.
Jungle Grid evaluates the workload requirements, identifies suitable capacity, estimates runtime, and applies the relevant usage-based infrastructure pricing.
Yes. Select inference, choose the model-size range and optimization target, then request an estimate without dispatching the workload.
Yes. The estimator supports directional estimates for fine-tuning and training as well as inference and batch processing.
No. The public workload estimator can be used without signing in.
No. It is a planning estimate. Actual usage depends on the final workload, runtime, available capacity, and execution behavior.
Jungle Grid evaluates suitable supported capacity and routes the workload without requiring you to select a provider first.
Dispatch with context