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For user-facing semantics see the resource model; this page covers how ops configures it.

GPU series

Specs and scheduling parameters for one class of GPU (h200 / h100 built-in defaults, overridable via FORGE_HARDWARE_SERIES JSON):

Profiles

GPU series + default shape + tuning overrides (built-in defaults, overridable via FORGE_CLUSTER_PROFILES):
  • name / series / num_nodes / gpus_per_node: the shape is the authoritative topology for quotas and training;
  • env: process environment (NCCL, Ray memory thresholds, PyTorch allocator) — for multi-node, must be identical to what ray start sees;
  • overrides: framework override entries (parallelism, vLLM memory fraction, micro batch). Only put keys shared by all experiments (CLI overrides use struct mode — changing a non-existent key raises an error).
The console’s Admin → Platform settings → Hardware offers visual editing with hot reload (instances pull on FORGE_HWCONFIG_REFRESH_INTERVAL).

User quotas

FORGE_QUOTA_ENFORCE=0 downgrades the whole system to “record only, don’t block” (for trial periods).

Storage quota

--max-storage-gb caps the total held by all of a user’s run directories (working copy + checkpoints + logs). It counts runs/<user>/; the caches (weights, datasets, corpora) are shared platform infrastructure and are not charged to anyone — the weights one job pulls, every later job uses. They answer different questions: the quota is “who is holding too much”, the watermark is “the disk is about to fill”.

Schedule windows

Restrict daily run windows per GPU series (Admin → Platform settings → Schedules): Jobs paused this way resume training automatically when the window reopens.