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Lilac helps teams source dedicated GPU capacity when reserved hardware is a better fit than the pay-per-token serverless inference network.

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What Lilac offers

Capacity sourced through Lilac’s network of GPU providers:
  • Dedicated GPU virtual machines for reserved workloads.
  • Bare-metal GPU nodes where full hardware control is required.
  • Private multi-node clusters for distributed training and larger deployments.
Lilac coordinates sourcing across this network. Underlying machines and facilities are operated by the providers Lilac works with.

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Who it is for

Teams whose workloads require:
  • Reserved, non-shared capacity.
  • A specific GPU architecture or interconnect.
  • Multi-node topology.
  • A particular region.
  • A defined start date and term.
For workloads that do not require reserved hardware, see serverless inference.

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How requesting capacity works

1

Define the requirement

Share GPU type, topology, region, start date, and term.
2

Source qualified options

Lilac compares available capacity across its network and surfaces options based on technical and commercial fit.
3

Coordinate launch

Lilac coordinates commercial details, access, and handoff into your environment. Provisioning is not instant or self-service.
4

Stay supported

Lilac remains the point of contact during the deployment and coordinates issues and escalations.

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Pricing and availability

Final pricing and availability depend on configuration, region, start date, and term. Indicative pricing on the marketing site is not a fixed quote and specific hardware is not universally available. See current indicative options on the dedicated GPUs page.

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Talk to us

Book a call about dedicated GPUs

Share the workload, model, throughput, region, and term. We’ll come back with options.
Or email contact@getlilac.com.