1. Symptoms
The clw-gpu-exhausted error occurs when attempting to launch new GPU-accelerated compute instances or allocate additional GPU resources through the OpenClaw platform. Users encounter this error during critical deployment workflows, particularly when scaling machine learning workloads, running distributed training jobs, or provisioning inference endpoints.
Shell Output Examples:
When using the OpenClaw CLI (clw), the error manifests in several distinguishable ways depending on the operation:
$ clw compute launch --instance-type gpu-standard --count 2
Error: clw-gpu-exhausted
GPU compute resources exhausted in region us-west-2.
Current allocation: 8/8 units. Retry after 2024-01-15T18:00:00Z
Request ID: req_7f3a9c2d8e1b
$ clw training submit --config ./ml_config.yaml
[ERROR] Failed to provision GPU workers: clw-gpu-exhausted
The requested 4x NVIDIA A100 GPUs exceed available capacity (0/2 available)
$ clw inference deploy --model deeplearning-model-v3 --replicas 3
CRITICAL: clw-gpu-exhausted
Region: eu-central-1
Available GPU memory: 0 GB / 80 GB
Recommended action: Scale down existing workloads or select alternate region
Additional observable symptoms include failed autoscaling events where GPU-enabled node pools cannot scale beyond current limits, pending reservation requests that remain unsatisfied for extended periods, and dashboard indicators showing GPU utilization at 100% across all available compute nodes in the target region.
2. Root Cause
The clw-gpu-exhausted error indicates that all GPU compute units in the specified region or availability zone have been fully allocated to existing workloads. This represents a fundamental capacity constraint rather than a configuration issue, meaning the platform has legitimately reached its GPU resource ceiling.
Under normal operating conditions, OpenClaw provisions GPU resources from a shared pool that services multiple customers and projects. Each GPU type (NVIDIA A100, H100, V100, or equivalent) maintains a maximum allocation count determined by hardware inventory, reservation commitments, and capacity planning thresholds. When cumulative requests from all users exhaust this pool, subsequent GPU allocation attempts fail with the clw-gpu-exhausted code.
Several contributing factors typically precede this exhaustion state. First, sustained demand from compute-intensive applications such as large language model training, computer vision pipelines, or real-time inference serving creates sustained pressure on GPU capacity. Second, long-running jobs that hold GPU reservations without releasing them between execution phases accumulate and diminish available headroom. Third, regional capacity imbalances occur when certain availability zones receive disproportionate demand while adjacent regions retain unused GPU inventory. Fourth, insufficient quota configuration may prevent users from accessing available capacity even when physical GPUs remain unallocated, though this typically manifests as a clw-quota-exceeded error instead.
Understanding the distinction between physical exhaustion and quota-based throttling proves essential for selecting the appropriate remediation strategy. Physical exhaustion requires either waiting for capacity to free up or redistributing workloads across regions, while quota-based limitations demand administrative intervention to increase allocated limits.
3. Step-by-Step Fix
Resolving the clw-gpu-exhausted error requires a systematic approach combining immediate workarounds with sustainable capacity management. Follow these steps in sequence to restore GPU compute capability.
Step 1: Check Real-Time GPU Availability Across Regions
Query the current GPU allocation status across all available regions to identify locations with remaining capacity:
# Query GPU availability across all regions
clw compute gpu-status --all-regions
# Example output showing capacity distribution
REGION GPU_TYPE TOTAL ALLOCATED AVAILABLE
us-west-2 A100-80GB 8 8 0
us-east-1 A100-80GB 12 9 3
eu-central-1 A100-80GB 6 4 2
ap-southeast-1 A100-80GB 10 6 4
Before:
clw compute launch --instance-type gpu-standard --region us-west-2
After:
clw compute launch --instance-type gpu-standard --region us-east-1
Step 2: Terminate Idle GPU Workloads
Identify and release GPU resources held by idle or completed workloads:
# List all running GPU instances
clw compute list --filter "state=running" --instance-type "gpu-*"
# Identify instances running longer than expected (potential leaks)
clw compute list --format json | jq '.[] | select(.instance_type | startswith("gpu")) | {id, name, uptime_hours, state}'
# Terminate idle GPU instances
clw compute terminate --instance-id i-7a9f3c2e1d8b --force
Step 3: Resize Existing GPU Instances to Smaller Footprint
Consolidate workloads onto fewer GPU resources by downsizing instance types:
Before:
# ml_pipeline.yaml
version: "1.0"
compute:
worker:
instance_type: gpu.4xA100
count: 4
gpu_memory: 320GB
After:
# ml_pipeline.yaml
version: "1.0"
compute:
worker:
instance_type: gpu.2xA100
count: 4
gpu_memory: 160GB
optimization: mixed_precision
Step 4: Implement GPU Time-Slicing for Concurrent Workloads
Configure shared GPU access to maximize utilization of available units:
# Enable GPU time-slicing configuration
clw compute update-policy --gpu-sharing enabled --slice-ratio 4:1
# Launch with shared GPU allocation
clw compute launch --instance-type gpu-shared --gpu-sharing --count 4
Step 5: Request Capacity Reservation for Guaranteed Access
For production workloads requiring guaranteed GPU availability:
# Request a capacity reservation
clw capacity reserve \
--gpu-type A100-80GB \
--count 2 \
--duration 30d \
--priority high
# Monitor reservation status
clw capacity status --reservation-id res_4e8a2c1f
4. Verification
After implementing remediation steps, verify that GPU compute resources have become accessible and workloads can proceed without the clw-gpu-exhausted error.
Immediate Verification Commands:
# Test GPU instance launch in the target region
clw compute launch \
--instance-type gpu-standard \
--region us-east-1 \
--dry-run
# Confirm GPU allocation succeeds
clw compute launch \
--instance-type gpu-standard \
--region us-east-1 \
--count 1
# Verify the launched instance receives GPU resources
clw compute describe <instance-id> --format json | jq '.gpu_allocation'
Expected Output:
{
"instance_id": "i-9c3e2f1a8d7b",
"state": "running",
"gpu_allocation": {
"type": "A100-80GB",
"count": 1,
"memory_gb": 80,
"status": "active"
},
"region": "us-east-1"
}
End-to-End Workload Verification:
For machine learning workloads, confirm training jobs or inference endpoints initialize successfully:
# Submit a test training job
clw training submit \
--config ./test_config.yaml \
--wait \
--timeout 5m
# Verify job reaches running state with GPU assignment
clw training describe <job-id> --format json | jq '{state, gpu_assigned, start_time}'
{
"state": "running",
"gpu_assigned": true,
"gpu_count": 1,
"start_time": "2024-01-15T14:32:18Z"
}
5. Common Pitfalls
Avoid these frequent mistakes that either fail to resolve the clw-gpu-exhausted error or create additional complications during remediation.
Pitfall 1: Immediately Retrying Without Understanding Capacity Windows
Repeated launch attempts against an exhausted region do not create additional capacity and may trigger rate limiting. The platform does not dynamically provision hardware based on demand spikes. Instead, capacity becomes available only when existing reservations expire or terminate.
Pitfall 2: Assuming the Error Indicates a Temporary Glitch
The clw-gpu-exhausted code specifically communicates that physical resources have reached allocation limits. Treating it as a transient error and repeatedly submitting requests generates unnecessary support tickets and may result in temporary API access restrictions.
Pitfall 3: Migrating to an Unfamiliar Region Without Considering Latency
Cross-region GPU allocation solves the immediate capacity problem but introduces network latency that impacts workload performance. Machine learning training jobs running across regions experience increased inter-node communication delays, while inference endpoints may produce unacceptable response time degradation.
Pitfall 4: Over-Provisioning Once Capacity Becomes Available
When GPU resources free up, teams sometimes launch excessive instances “just in case,” which accelerates re-exhaustion and deprives other teams of access. Implement resource tagging and quota enforcement to ensure responsible consumption.
Pitfall 5: Neglecting to Set Up Monitoring Alerts
Without proactive monitoring, teams discover GPU exhaustion only when critical deployment pipelines fail. Configure OpenClaw alerts to trigger when GPU utilization exceeds 80% threshold, providing adequate response time before complete exhaustion occurs.
Pitfall 6: Ignoring Spot/Preemptible GPU Options
On-demand GPU instances face the highest competition during shortage periods. Spot instances, though subject to potential interruption, often retain availability when on-demand capacity exhausts completely. Evaluate whether workload architecture tolerates spot instance characteristics.
6. Related Errors
clw-instance-limit
The clw-instance-limit error shares structural similarities with clw-gpu-exhausted but pertains to general compute instance counts rather than GPU-specific allocation. This error triggers when the total number of running instances across all types reaches the account-level or project-level ceiling, regardless of whether those instances utilize GPU resources. Resolution involves either terminating existing instances, requesting quota increases, or implementing instance lifecycle policies that automatically clean up completed workloads.
clw-quota-exceeded
The clw-quota-exceeded error indicates that a specific resource consumption threshold has been reached, but the threshold is configurable rather than representing physical hardware limits. Unlike clw-gpu-exhausted, which reflects genuine capacity constraints, clw-quota-exceeded stems from administrative policies governing maximum resource consumption per user, team, or project. Administrative users can resolve this through the OpenClaw dashboard or CLI by adjusting quota values, whereas regular users must request increases through proper approval channels.
clw-resource-locked
The clw-resource-locked error occurs when GPU resources exist but cannot be allocated due to active locks preventing modification. Locks may result from ongoing maintenance operations, security holds, billing disputes, or resource reservation commitments. This error differs from clw-gpu-exhausted because available capacity genuinely exists but remains inaccessible. Resolution typically requires waiting for lock expiration or contacting OpenClaw support to investigate the specific lock reason and determine appropriate解除 procedures.