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Owl

Beta

Owl is the configuration workspace for dedicated vLLM inference. Customers can choose a supported model or provide a custom model reference, size its GPU runtime, configure the endpoint, and submit a deployment request to the managed control plane.

Open Owl
Beta operating manual

Owl beta deployment manual

Prepare and submit a governed vLLM model deployment request with reviewed model source, runtime sizing, and endpoint behavior.

Manual
Version 1.0
Product
Owl 0.1.0 beta
Verified
July 15, 2026
Language
English · Source
Owner
Owl AI platform and support
Source
owl · 704071b
Review cycle
45 days
Start here

Audience, scope, and prerequisites

Covers model selection, runtime planning, command review, and deployment submission through the current Owl beta. Owl either forwards to a configured control plane or returns a deployment plan; a successful response is not proof that a healthy model endpoint exists.

Who this is for

  • Approved model owners
  • AI platform reviewers
  • Owl infrastructure operators

Before you sign in

  • Approval to deploy and operate the model
  • License, provenance, security, and data-use review
  • A GPU capacity and cost allocation
  • An endpoint owner, access policy, monitoring plan, and rollback decision
Access model

Roles and responsibilities

RoleAccessResponsibilities
Model requesterConfigure a supported preset or custom model request.
  • Provide accurate source and sizing
  • State the intended endpoint task
AI platform reviewerReview model, code trust, runtime, security, and resource plan.
  • Validate license and supply chain
  • Approve risk and capacity
Infrastructure operatorFulfil or monitor requests through the vLLM control plane.
  • Protect endpoint credentials
  • Verify isolation, health, and rollback
Workspace map

Navigation

Configure

/#configure

Choose a curated preset or custom model reference.

Runtime

/#runtime

Set serving name, GPU parallelism, context, memory, quantization, dtype, and endpoint mode.

Deploy

/#deploy

Review the generated command and submit the request.

Chapter 01

Configure and review a model

Model code, weights, license, context, quantization, and memory choices determine both security and runtime behavior.

Procedure 1.1

Select a preset or custom model

Choose a source that has been approved for the intended task and tenant.

  1. 1
    Define the use case

    State chat, embeddings, or other supported endpoint purpose, data classes, throughput, and quality requirements.

  2. 2
    Choose a preset

    Review provider, repository, size, context, GPU count, memory estimate, and tags.

  3. 3
    Or enter a custom reference

    Use an exact Hugging Face repository or approved mounted artifact path and pin the accepted revision outside the free-text field.

  4. 4
    Review license and provenance

    Confirm commercial use, redistribution, gated access, training-data restrictions, and model-card risks.

  5. 5
    Set a stable served name

    Use an approved endpoint-facing name that does not disclose secrets or personal data.

Expected result

The request identifies one approved model source, revision, owner, task, and serving name.

Procedure 1.2

Size the vLLM runtime

Set capacity conservatively and record the reason for every non-default option.

  1. 1
    Set tensor parallel

    Match approved GPU count and model/runtime support.

  2. 2
    Set maximum model length

    Choose the minimum context that meets the use case; longer context increases memory and latency.

  3. 3
    Set GPU memory utilization

    Leave headroom for runtime overhead and operational stability.

  4. 4
    Choose dtype and quantization

    Use only combinations validated for model quality and hardware.

  5. 5
    Choose endpoint mode

    Select OpenAI compatible, Embeddings, or Chat only according to intended use.

Expected result

The runtime profile fits approved capacity and has explicit quality, context, and endpoint tradeoffs.

Procedure 1.3

Review remote-code and credential options

Treat trust-remote-code and endpoint credentials as security decisions.

  1. 1
    Keep remote code disabled by default

    Enable it only after code and pinned revision review in an isolated environment.

  2. 2
    Review generated command

    Inspect model reference, served name, GPU, context, memory, dtype, quantization, and task flags.

  3. 3
    Set endpoint credential handling

    Use the platform-managed secret process; do not enter a reusable real secret into screenshots or tickets.

  4. 4
    Obtain platform approval

    Attach model, supply-chain, data, cost, and runtime decisions to the request.

Expected result

The generated vLLM plan contains no unreviewed code trust, secret exposure, or unexpected runtime flag.

Chapter 02

Submit and verify deployment

Owl may return a plan without creating infrastructure when no control plane is configured.

Procedure 2.1

Submit the deployment request

Submit once and preserve the returned identity, status, command, and endpoint.

  1. 1
    Reconfirm configuration

    Compare the form and generated command with the approved request.

  2. 2
    Select Deploy

    Submit once and wait for the response.

  3. 3
    Record the response

    Capture deployment ID, status, endpoint, generated command, and whether the control plane accepted it.

  4. 4
    Distinguish plan from deployment

    If the service returned only a command/environment plan, route it to the operator rather than announcing readiness.

  5. 5
    Verify through the platform

    Confirm capacity allocation, image/model integrity, endpoint authentication, health, and monitoring.

Expected result

One traceable request exists and is correctly classified as planned, provisioning, failed, or operational.

Procedure 2.2

Accept a model endpoint

Test security, function, quality, limits, and rollback before application use.

  1. 1
    Verify endpoint identity

    Confirm URL, certificate, served model name, authentication, tenant scope, and network access.

  2. 2
    Run bounded functional tests

    Test intended task, context, streaming, embeddings or chat format, and error behavior with synthetic data.

  3. 3
    Review quality and safety

    Use the approved evaluation set and record known failure modes.

  4. 4
    Review operations

    Confirm metrics, logs, rate/capacity limits, alerting, scaling, shutdown, and rollback.

  5. 5
    Approve integration

    Allow applications to use the endpoint only after the accountable owner accepts results.

Expected result

The endpoint is either accepted for a bounded use case or remains isolated with documented defects.

Operating rules

Security and data handling

  • Pin and review model and code revisions.
  • Keep trust-remote-code disabled unless explicitly approved.
  • Protect endpoint API keys and deployment control-plane access.
  • Use synthetic data for acceptance and apply approved data policy in operation.
  • Limit context, GPU, network, and endpoint capabilities to the use case.
Current release

Known limitations

Read before relying on an unsupported workflow

These limits describe the source-pinned Owl 0.1.0 beta scope and must be rechecked when the product changes.

  • Owl is a beta request workspace, not evidence that GPU capacity or a healthy endpoint exists.
  • Without VLLM_CONTROL_PLANE_URL, submission returns a command and environment plan without external deployment.
  • Preset memory and GPU values are planning guidance and require operator validation.
  • The UI does not expose a full deployment inventory, lifecycle, rollback, monitoring, or deletion workflow.
  • Custom model quality, license, code, and artifact integrity remain the requester’s and reviewer’s responsibility.
Problem solving

Troubleshooting

You cannot sign in to Owl

Likely cause: The session expired, the identity is not assigned to the application, or the tenant claim is missing.

  1. Open the application from the approved portal and complete sign-in again.
  2. Confirm that the correct organizational identity and tenant are selected.
  3. If access is still denied, record the time and request an entitlement check from support.

A documented action or navigation item is not visible

Likely cause: The current role, release, tenant configuration, or feature flag does not expose that operation.

  1. Confirm the role and prerequisites listed for the procedure.
  2. Reload the page after signing in again; do not attempt to bypass the interface through direct URLs.
  3. Ask the product owner whether the feature is enabled for the tenant before reporting a defect.

A saved change or background operation is not visible

Likely cause: Validation failed, processing is still running, or the current view is stale or filtered.

  1. Review inline validation, status indicators, filters, and the selected tenant or workspace.
  2. Refresh once and search for the item by its stable name or identifier.
  3. Do not repeat irreversible or externally visible actions until the original operation status is known.

Submission returns a plan but no working endpoint

Likely cause: The vLLM control plane is not configured or did not provision the request.

  1. Read the returned status and endpoint fields.
  2. Treat the command as a plan, not an instruction to run it outside change control.
  3. Send the deployment ID and approved plan to the AI platform operator.

The model fails to load or runs out of memory

Likely cause: Model revision, dtype, quantization, context, tensor parallelism, or GPU capacity is incompatible.

  1. Preserve deployment logs and configuration without secrets.
  2. Stop automated retries that consume capacity.
  3. Have the operator revise one sizing variable at a time and repeat acceptance tests.
Escalation

Contact support

Contact support when

  • A repeatable Owl error blocks an approved workflow
  • Expected data, permissions, or tenant boundaries appear incorrect
  • A security, privacy, compliance, or data-loss concern is suspected
  • Unreviewed remote code may have executed
  • A deployment exposes an unauthenticated or cross-tenant endpoint
  • Provisioning consumes unexpected GPU capacity

Include

  • The page, action, and expected result
  • The exact error text and time of occurrence, including time zone
  • Your tenant, role, browser, and a sanitized record or item identifier
  • Whether the problem can be reproduced and which troubleshooting steps were tried

Never include

  • Passwords, access tokens, API keys, recovery codes, or session cookies
  • Unredacted personal, health, financial, or other regulated data
  • Private encryption keys or complete confidential documents unless an approved channel is provided
  • Endpoint API keys, Hugging Face tokens, private artifact credentials, or complete confidential prompts
Contact support