mold lambda — Lambda Cloud web UI

mold lambda deploys mold to Lambda Cloud as a private, Docker-first web UI. The local CLI creates or reuses Lambda resources, starts the mold container on the instance, opens an SSH tunnel to 127.0.0.1:7680, and prints the matching MOLD_HOST export.

Setup

Create a Lambda Cloud API key in the Lambda console, then configure mold:

mold config set lambda.api_key <your-key>
# or, for shell-only use:
export LAMBDA_API_KEY=<your-key>

mold lambda doctor

Lambda access stays private by default. The remote container binds mold to 127.0.0.1:7680 on the instance, and your laptop reaches it through SSH:

mold lambda deploy --instance-type gpu_1x_a10 --region us-west-1
eval "$(mold lambda tunnel)"
open http://127.0.0.1:7680

Discovery

mold lambda availability
mold lambda availability --json

Availability shows the Lambda instance type, region, GPU description, GPU count, generation_slots, price per hour, memory/storage, and the selected Docker image tag. Slots equal GPU count: mold keeps one GPU-resident engine per worker/GPU and does not claim extra parallel slots on a single large GPU.

Availability displays the human-readable target tag below. Stable automatic deployments fetch the digest manifest generated by that exact release workflow manifest and submit ghcr.io/utensils/mold@sha256:…; they fail closed if that manifest is unavailable or does not match the CLI source. Rolling/source/Nix binaries use mutable latest* tags.

GPU family	Automatic image tag
Ampere / A30 / A100	:<version>-sm80
Ampere / A2/A10/A16/A40, RTX A4000–A6000, RTX 3050–3090	:<version>-sm86
Ada / L40 / RTX 4090	:<version>
Hopper / H100 / H200	:<version>-sm90
Datacenter Blackwell / B200 / B300	:<version>-sm100
Consumer Blackwell / named RTX PRO or GeForce RTX 50-series	:<version>-sm120

The shared name table includes RTX 3050, all named GeForce RTX 50-series, and named RTX PRO 1000/2000/4000/4500/5000/6000 Blackwell variants. Unknown names stay on the conservative default rather than being guessed.

Generic Ampere selects sm_80. A bare Blackwell name is ambiguous between incompatible sm_100 and sm_120 targets, so it conservatively uses the default sm_89-compatible tag rather than guessing.

B200 support is simulated, not hardware-qualified. Mold does not launch Lambda qualification resources automatically; the real 8×B200 campaign is a separate deferred milestone.

GH200, GB200, and GB300 require future linux/arm64 artifacts and are unsupported. The published Mold CUDA images are Linux amd64 only, and both availability output and deploy fail closed before selecting an image for a Grace instance.

Persistent Storage

mold lambda deploy creates or reuses one Lambda filesystem per region. The default name is mold-<region>, mounted at /data/mold and bound into the container as /workspace, so models, LoRAs, cache, the metadata DB, and gallery outputs survive instance replacement.

mold lambda filesystems
mold lambda filesystems --json

Filesystems continue billing while they exist. Use mold lambda reset --to-zero when you want all mold-managed Lambda storage removed.

Secrets

Secrets are not forwarded by default. To copy local HF_TOKEN and CIVITAI_TOKEN into the remote service env, opt in explicitly:

mold lambda deploy --instance-type gpu_1x_a10 --region us-west-1 --forward-secrets

The CLI writes a root-readable env file over SSH and restarts the remote mold-lambda.service.

Lifecycle

mold lambda status
mold lambda logs --follow
mold lambda ssh
mold lambda terminate

terminate stops the instance and local tunnel but leaves the regional filesystem. To remove mold-managed instances, tunnels, and filesystems:

mold lambda reset --to-zero

The reset command requires typing delete mold lambda resources unless you pass that exact phrase through --confirm.

Configuration

[lambda]
# api_key = "..."                         # Prefer LAMBDA_API_KEY for shells
endpoint = "https://cloud.lambda.ai/api/v1"
image_repository = "ghcr.io/utensils/mold"
ssh_key_name = "mold-laptop"
ssh_private_key_path = "~/.ssh/id_ed25519"
filesystem_prefix = "mold"
filesystem_mount_path = "/data/mold"
confirm_hourly_usd = 5.0
local_port = 7680

All keys are settable with mold config set lambda.<key> <value>.