Fix clw-gpu-limit-exceeded: GPU Resource Quota Exceeded in OpenClaw

By Maya Rodriguez
OpenClaw intermediate Linux macOS Windows Cloud Shell

1. Symptoms

The clw-gpu-limit-exceeded error manifests when a workload attempts to utilize more GPU compute resources than are available within your OpenClaw account quota. This error typically surfaces during job submission, container deployment, or when scaling GPU-accelerated workloads.

Shell Output Indicators

When attempting to launch a GPU-accelerated workload, you may encounter output similar to the following:

$ clw job submit --gpus 4 --image tensorflow:latest --command "python train.py"
Error: clw-gpu-limit-exceeded
Message: Requested 4 GPUs but only 2 available in quota. Current usage: 2/2 GPUs allocated.

Quota Details:
  - Total GPU quota: 2
  - Currently in use: 2
  - Requested: 4
  - Available: 0

Suggestion: Reduce GPU request, wait for running jobs to complete, or request a quota increase via the OpenClaw console.

Additional Symptoms

Beyond the command-line output, you may observe the following indicators that suggest a GPU limit issue:

  • Job submissions with GPU requirements remain in a pending state indefinitely
  • Container orchestrator reports insufficient GPU capacity even when physical GPUs are present in the cluster
  • API responses include HTTP status code 429 (Too Many Requests) with the specific error code
  • Dashboard interfaces display a red warning badge next to GPU quota meters
  • Scheduled jobs fail silently with GPU allocation errors in their logs

2. Root Cause

The clw-gpu-limit-exceeded error occurs because OpenClaw enforces strict GPU resource quotas at multiple levels of the platform architecture. Understanding the quota hierarchy is essential for diagnosing and resolving this error.

Quota Hierarchy

OpenClaw implements GPU quotas at three distinct levels, and exhaustion at any single level will trigger the error. The first level is the account quota, which defines the maximum number of GPUs that can be simultaneously allocated across all projects within your organization. The second level is the project quota, which controls GPU allocation for specific projects or teams. The third level is the job quota, which limits the number of GPUs that a single job or workflow can request.

Common Trigger Scenarios

Several scenarios commonly lead to GPU quota exhaustion. First, launching multiple concurrent training jobs without proper job queue management quickly consumes available GPU allocation. Second, requesting more GPUs than necessary for a workload—often as a buffer “just in case”—wastes quota and prevents other jobs from accessing needed resources. Third, jobs that fail to release GPU resources properly, perhaps due to unhandled exceptions or improper cleanup, leave GPUs in an allocated but unused state. Fourth, aggressive autoscaling configurations that request GPUs beyond the allocated quota will consistently fail until the quota is increased or scaling parameters are adjusted.

Technical Mechanism

When you submit a workload with GPU requirements, the OpenClaw scheduler performs a two-phase allocation check. During the first phase, the scheduler validates that your request does not exceed your project-level GPU quota. During the second phase, the scheduler verifies that sufficient GPUs are physically available within the cluster and not already allocated to other workloads. If either check fails, the scheduler rejects the request with the clw-gpu-limit-exceeded error code.

3. Step-by-Step Fix

Resolving the GPU limit exceeded error requires a methodical approach. Follow these steps to diagnose the issue and restore your ability to launch GPU-accelerated workloads.

Step 1: Inspect Current GPU Quota Usage

Begin by examining your current GPU allocation and utilization across all active jobs. Use the OpenClaw CLI to retrieve detailed quota information.

Before:

clw quota show

After:

clw quota show --resource-type gpu --verbose

This command displays your GPU quota allocation, current utilization, and the number of GPUs requested by each active job. Review the output to identify which jobs are consuming GPU resources.

Step 2: Identify and Terminate Stale GPU Allocations

Locate jobs that are holding GPU allocations but are not actively computing. These stale allocations prevent new workloads from launching.

Before:

# List all running jobs without filtering
clw job list

After:

# List GPU jobs with their status and age
clw job list --gpus --format "id,name,status,age,gpus,created_by" | grep -E "RUNNING|PENDING"

# Check for jobs stuck in terminal states
clw job list --gpus --status TERMINATED --since 24h | awk '$5 > 0 {print $1}'

For jobs that appear stuck with GPU allocations, force termination to release resources.

Before:

# Attempt graceful cancellation
clw job cancel <job-id>

After:

# Force termination of stuck job
clw job terminate <job-id> --force --reason "stuck-holding-gpu-resources"

Step 3: Modify Job GPU Request to Match Actual Needs

Reduce the GPU allocation for your workload to fit within available quota. Analyze your workload’s actual GPU requirements before submitting.

Before:

# Over-provisioned request
clw job submit --gpus 8 --image pytorch:latest --command "python train.py"

After:

# Request only necessary GPUs based on workload analysis
# For data-parallel training with 4 GPUs across 32 batches, request 4 GPUs
clw job submit --gpus 4 --image pytorch:latest --command "python train.py"

# For single-GPU inference workload
clw job submit --gpus 1 --image pytorch:latest --command "python inference.py"

Step 4: Use Job Queueing for Multiple Workloads

When you need to run multiple GPU jobs, submit them to the shared queue rather than launching them simultaneously.

Before:

# Immediate parallel submission
clw job submit --gpus 2 --name job-1 --command "python script1.py" &
clw job submit --gpus 2 --name job-2 --command "python script2.py" &
wait

After:

# Submit to managed queue with priority
clw job submit --gpus 2 --name job-1 --queue default --priority 10 --command "python script1.py"
clw job submit --gpus 2 --name job-2 --queue default --priority 5 --command "python script2.py"

# Monitor queue position
clw job list --queue default --gpus

Step 5: Request Quota Increase (If Legitimate Need Exists)

If your workload genuinely requires more GPU resources than your current quota allows, submit a formal quota increase request through the OpenClaw console or API.

Before:

# Attempt to submit job that requires more quota than available
clw job submit --gpus 16 --image large-model:latest --command "python train.py"

After:

# Submit quota increase request with justification
clw quota request-increase \
  --resource-type gpu \
  --current-limit 8 \
  --requested-limit 16 \
  --justification "Training large language models requiring 16 GPUs for efficient data parallelism" \
  --project-id <your-project-id>

# While awaiting approval, use fractional GPU allocation if supported
clw job submit --gpus 8 --gpu-fraction 0.5 --image large-model:latest --command "python train.py"

4. Verification

After implementing the fix steps, verify that your GPU workloads can now launch successfully. Use the following verification procedures to confirm the resolution.

Confirm GPU Availability

Execute a simple GPU verification command to ensure GPUs are accessible and properly allocated.

# Check available GPU resources
clw quota show --resource-type gpu

# Expected output should show available GPUs > 0
# Verify the specific job can see GPUs
clw job exec <job-id> --command "nvidia-smi"

The nvidia-smi output within your job should display the allocated GPUs, confirming that the scheduling and allocation mechanisms are functioning correctly.

Test New Job Submission

Submit a minimal GPU workload to verify that the quota system accepts your request.

# Submit a simple test job
clw job submit --gpus 1 --name gpu-test --image cuda-samples:latest --command "nvidia-smi" --wait

# Verify job completed successfully
clw job describe gpu-test --format "status,exit_code,gpus_allocated"

A successful verification shows status=SUCCEEDED, exit_code=0, and confirms the requested GPU count matches the allocated GPU count.

Monitor for Recurrence

Set up monitoring to detect if the error reoccurs in the future.

# Create a monitoring alert for GPU quota exhaustion
clw alert create \
  --name gpu-quota-warning \
  --condition "gpu_available < 1" \
  --severity WARNING \
  --notification-channel email,slack

# Enable quota usage tracking
clw metrics enable --resource-type gpu --retention 30d

5. Common Pitfalls

Avoid these frequent mistakes when addressing the clw-gpu-limit-exceeded error. These pitfalls often cause the error to recur or lead to unintended resource waste.

Ignoring fractional GPU allocation options: Many workloads do not require a full GPU. OpenClaw supports fractional GPU allocation through GPU partitioning technologies. Requesting a half-GPU or quarter-GPU when your workload cannot fully utilize a full GPU wastes resources that other jobs could use. Always evaluate whether your workload can benefit from fractional allocation before requesting a full GPU.

Not implementing proper job cleanup: Jobs that terminate abnormally often leave GPU allocations in an orphaned state. Implement signal handlers in your application code to ensure graceful shutdown and proper GPU memory release. Configure your job templates with appropriate cleanup hooks that execute regardless of job exit status.

Over-provisioning GPU requests: Requesting more GPUs than necessary “just to be safe” is a wasteful practice that contributes to quota exhaustion across the organization. Profile your workloads to determine actual GPU utilization and request only what your workload requires. Most deep learning training workloads scale efficiently only up to a certain number of GPUs due to communication overhead.

Submitting jobs without checking current usage: Launching GPU jobs without first checking available quota guarantees eventual failure when quota is exhausted. Always include a quick quota check as part of your job submission workflow or automation scripts.

Assuming quota resets automatically: Some administrators incorrectly assume that GPU quotas reset daily or weekly. In OpenClaw, quotas are continuous limits that persist until explicitly changed. Terminated jobs release their GPU allocations immediately, but accumulated usage across multiple jobs can still exceed quota limits.

The following errors share similar root causes and resolution patterns with the clw-gpu-limit-exceeded error.

clw-memory-limit-exceeded

This error occurs when workloads attempt to allocate more system memory (RAM) than the quota allows. Like GPU limits, memory quotas are enforced at account, project, and job levels. The resolution approach mirrors the GPU limit fix: inspect current usage, terminate stale allocations, reduce memory requests, and submit quota increase requests if necessary. Memory leaks in long-running jobs commonly cause this error to surface unexpectedly after periods of normal operation.

clw-cpu-limit-exceeded

CPU quota exhaustion presents identical symptoms to GPU quota issues but affects compute cores rather than GPU devices. This error frequently occurs alongside GPU limits in compute-intensive workloads that require both CPU and GPU resources. The combined resource requirements mean that exhausting CPU quota can indirectly prevent GPU workloads from launching even when GPU quota remains available.

clw-resource-quota-exceeded

This generic quota error serves as a catch-all for resource exhaustion scenarios not covered by specific error codes. When you encounter this error, the accompanying diagnostic message specifies which resource type triggered the rejection. The resolution depends on identifying the limiting resource and applying the appropriate mitigation strategy from the resource-specific error handling procedures.

gpu quota resource-limit compute openclaw exceeded clw-gpu-limit-exceeded